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 <!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>ALL Bench Leaderboard 2026 — AI Model Benchmark: LLM · VLM · Agent · Image · Video · Music</title>
<meta name="description" content="The only AI leaderboard comparing 91 models across 6 modalities. Cross-verified scores for GPT-5, Claude, Gemini, Grok, DeepSeek, Kimi, Qwen and more. 42 LLMs, 11 VLMs, 10 Agents, 28 generative models with confidence badges.">
<meta name="keywords" content="AI benchmark, LLM leaderboard, GPT-5, Claude Opus, Gemini 3, VLM benchmark, AI agent, MMLU-Pro, GPQA, ARC-AGI-2, FINAL Bench, metacognition, AI model comparison, image generation, video generation, music generation, 2026">
<meta name="author" content="ALL Bench Team">
<meta name="robots" content="index, follow, max-snippet:-1, max-image-preview:large">
<link rel="canonical" href="https://huggingface.co/spaces/FINAL-Bench/all-bench-leaderboard">
<!-- Open Graph -->
<meta property="og:title" content="ALL Bench Leaderboard 2026 — Unified AI Benchmark">
<meta property="og:description" content="91 AI models across LLM · VLM · Agent · Image · Video · Music. Cross-verified with confidence badges. Interactive tools, free API, intelligence reports.">
<meta property="og:type" content="website">
<meta property="og:url" content="https://huggingface.co/spaces/FINAL-Bench/all-bench-leaderboard">
<!-- Twitter Card -->
<meta name="twitter:card" content="summary_large_image">
<meta name="twitter:title" content="ALL Bench Leaderboard 2026 — 91 AI Models Compared">
<meta name="twitter:description" content="The only leaderboard covering LLM, VLM, Agent, Image, Video, Music. GPT-5 vs Claude vs Gemini — all cross-verified.">
<!-- JSON-LD Structured Data -->
<script type="application/ld+json">
{"@context":"https://schema.org","@type":"Dataset","name":"ALL Bench Leaderboard 2026","description":"Unified multi-modal AI benchmark covering 91 models across LLM, VLM, Agent, Image, Video, Music with cross-verified confidence system","url":"https://huggingface.co/spaces/FINAL-Bench/all-bench-leaderboard","license":"https://opensource.org/licenses/MIT","version":"2.1","dateModified":"2026-03-08","creator":{"@type":"Organization","name":"ALL Bench Team"},"keywords":["AI benchmark","LLM leaderboard","GPT-5","Claude","Gemini","VLM","FINAL Bench","metacognition","multimodal AI"],"distribution":[{"@type":"DataDownload","encodingFormat":"application/json","contentUrl":"https://huggingface.co/datasets/FINAL-Bench/ALL-Bench-Leaderboard/resolve/main/all_bench_leaderboard_v2.1.json"}]}
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<div class="badge"><div class="pulse"></div>LIVE · 2026.03.08 · v2.1</div>
<button onclick="document.body.classList.toggle('dark');this.textContent=document.body.classList.contains('dark')?'☀ Light':'🌙 Dark'" style="background:var(--surface);border:1px solid var(--border);border-radius:20px;padding:3px 10px;font-size:9px;font-family:var(--font-mono);color:var(--text-sec);cursor:pointer;font-weight:600">🌙 Dark</button>
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<div class="st" style="padding:4px 10px;min-width:auto"><div class="stn" style="font-size:12px">70</div><div class="stl">Models</div></div>
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<div class="st" style="padding:4px 10px;min-width:auto"><div class="stn" style="font-size:12px;color:#16a34a">24</div><div class="stl">Open</div></div>
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<h1>ALL Bench Leaderboard 2026</h1>
<p class="sub" style="margin-bottom:6px">
<b>The only leaderboard covering LLM · VLM · Agent · Image · Video · Music in one place.</b> 42 LLMs + 11 VLMs + 28 generative models. All scores cross-verified.
</p>
<div style="display:flex;gap:6px;flex-wrap:wrap;justify-content:center;font-family:var(--font-mono);font-size:8.5px;color:var(--text-muted);margin-bottom:4px">
<span style="color:#e11d48">🔥 v2.1 — Confidence System + Intelligence Report</span> ·
<span style="color:#0d9488">🌙 Dark mode · 📱 Mobile ready</span> ·
<span style="color:#7c3aed">🇰🇷 K-EXAONE data from official Technical Report</span>
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<a href="https://huggingface.co/datasets/FINAL-Bench/ALL-Bench-Leaderboard" target="_blank" style="display:inline-flex;align-items:center;gap:4px;background:linear-gradient(135deg,#ff9d00,#ffcd00);color:#1a1a2e;font-family:var(--font-mono);font-size:8px;font-weight:800;padding:3px 10px;border-radius:14px;text-decoration:none;letter-spacing:0.3px;box-shadow:0 1px 3px rgba(255,157,0,.3);transition:all .2s" onmouseover="this.style.transform='translateY(-1px)';this.style.boxShadow='0 3px 8px rgba(255,157,0,.4)'" onmouseout="this.style.transform='';this.style.boxShadow='0 1px 3px rgba(255,157,0,.3)'">🤗 HuggingFace Dataset</a>
<a href="https://github.com/final-bench/ALL-Bench-Leaderboard" target="_blank" style="display:inline-flex;align-items:center;gap:4px;background:linear-gradient(135deg,#24292e,#40444b);color:#fff;font-family:var(--font-mono);font-size:8px;font-weight:800;padding:3px 10px;border-radius:14px;text-decoration:none;letter-spacing:0.3px;box-shadow:0 1px 3px rgba(0,0,0,.2);transition:all .2s" onmouseover="this.style.transform='translateY(-1px)';this.style.boxShadow='0 3px 8px rgba(0,0,0,.3)'" onmouseout="this.style.transform='';this.style.boxShadow='0 1px 3px rgba(0,0,0,.2)'">⚡ GitHub Repo</a>
<a href="https://huggingface.co/datasets/FINAL-Bench/Metacognitive" target="_blank" style="display:inline-flex;align-items:center;gap:4px;background:linear-gradient(135deg,#7c3aed,#6366f1);color:#fff;font-family:var(--font-mono);font-size:8px;font-weight:800;padding:3px 10px;border-radius:14px;text-decoration:none;letter-spacing:0.3px;box-shadow:0 1px 3px rgba(99,102,241,.3);transition:all .2s" onmouseover="this.style.transform='translateY(-1px)';this.style.boxShadow='0 3px 8px rgba(99,102,241,.4)'" onmouseout="this.style.transform='';this.style.boxShadow='0 1px 3px rgba(99,102,241,.3)'">🧬 FINAL Bench Dataset</a>
<a href="https://huggingface.co/spaces/FINAL-Bench/Leaderboard" target="_blank" style="display:inline-flex;align-items:center;gap:4px;background:linear-gradient(135deg,#0d9488,#059669);color:#fff;font-family:var(--font-mono);font-size:8px;font-weight:800;padding:3px 10px;border-radius:14px;text-decoration:none;letter-spacing:0.3px;box-shadow:0 1px 3px rgba(13,148,136,.3);transition:all .2s" onmouseover="this.style.transform='translateY(-1px)';this.style.boxShadow='0 3px 8px rgba(13,148,136,.4)'" onmouseout="this.style.transform='';this.style.boxShadow='0 1px 3px rgba(13,148,136,.3)'">🏆 FINAL Bench Leaderboard</a>
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<!-- ========== VERTICAL RANKING BAR CHART (above table) ========== -->
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<div class="vrank-title">🏆 ALL Bench Composite Score Ranking</div>
<div class="vrank-desc">√Coverage Score = Avg × √(N/10) · 10 benchmarks (v1.5: LCB replaces SWE-V) · ✓Full(7+) ◐Partial(4-6) ○Limited(&lt;4) · Colored by provider</div>
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<span>Data Confidence:</span>
<span style="color:#16a34a;font-weight:700">✓✓</span><span>Cross-verified (2+ sources)</span>
<span style="color:#d97706;font-weight:700">✓</span><span>Single source</span>
<span style="color:#e11d48;font-weight:700">~</span><span>Self-reported</span>
<span style="margin-left:auto;opacity:.7">Hover score badges for source details · Verified: 2026-03-08</span>
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<table id="T">
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<tr>
<th class="c-model" onclick="srt(0)">Model<span class="sa">↕</span></th>
<th class="gP" style="min-width:72px">Provider</th>
<th onclick="srt(2)" class="gA" title="√Coverage Score = Avg(confirmed) × √(N/10). Coverage badge: ✓Full(7+) ◐Partial(4-6) ○Limited(<4). 10 benchmarks: MMLU-Pro·GPQA·AIME·HLE·ARC-AGI-2·Metacog·SWE-Pro·BFCL·IFEval·LCB" style="min-width:58px">🏆 Score<span class="sa">↕</span></th>
<th onclick="srt(3)" class="gT" style="min-width:48px">📅 Release<span class="sa">↕</span></th>
<th onclick="srt(4)" class="gB" data-col="4" title="MMLU-Pro: 57K questions, highest sample size &amp; reliability" style="min-width:52px"><a href="https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro" target="_blank">📚 MMLU-Pro</a><span class="sa">↕</span></th>
<th onclick="srt(5)" class="gB" data-col="5" title="GPQA Diamond: PhD-level expert questions, highest discrimination" style="min-width:52px"><a href="https://huggingface.co/datasets/Idavidrein/gpqa" target="_blank">🧠 GPQA◆</a><span class="sa">↕</span></th>
<th onclick="srt(6)" class="gB" data-col="6" title="AIME 2025: Math olympiad, minimal contamination" style="min-width:50px"><a href="https://artofproblemsolving.com/wiki/index.php/2025_AIME" target="_blank">📐 AIME25</a><span class="sa">↕</span></th>
<th onclick="srt(7)" class="gB" data-col="7" title="HLE: Humanity's Last Exam — hardest existing benchmark, 2500 expert-sourced questions" style="min-width:48px"><a href="https://huggingface.co/datasets/centerforaisafety/hle" target="_blank">🔭 HLE</a><span class="sa">↕</span></th>
<th onclick="srt(8)" class="arc-col" style="color:#0ea5e9!important;min-width:56px" data-col="8" title="ARC-AGI-2: Abstract reasoning, novel visual puzzles — most contamination-proof"><a href="https://arcprize.org/arc-agi-2" target="_blank" style="color:#0ea5e9">🧩 ARC-AGI-2★</a><span class="sa">↕</span></th>
<th onclick="srt(9)" class="gF meta-col" data-col="9" title="FINAL-Bench Metacognitive: 100 tasks, measures self-correction &amp; error recovery (ER)" style="min-width:54px"><a href="https://huggingface.co/datasets/FINAL-Bench/Metacognitive" target="_blank" style="color:#7c3aed">🧬 Metacog★</a><span class="sa">↕</span></th>
<th onclick="srt(10)" class="gB" data-col="10" title="SWE-Pro: Scale AI SEAL, 1865 real repos, contamination-free" style="min-width:52px"><a href="https://scale.com/leaderboard/coding" target="_blank">🏗 SWE-Pro</a><span class="sa">↕</span></th>
<th onclick="srt(11)" class="gB" data-col="11" title="BFCL v4: Tool use &amp; agent capability" style="min-width:48px"><a href="https://gorilla.cs.berkeley.edu/leaderboard.html" target="_blank">🔧 BFCL</a><span class="sa">↕</span></th>
<th onclick="srt(12)" class="gB" data-col="12" title="IFEval: Instruction following" style="min-width:48px"><a href="https://huggingface.co/datasets/google/IFEval" target="_blank">📋 IFEval</a><span class="sa">↕</span></th>
<th onclick="srt(13)" class="gB" data-col="13" title="LiveCodeBench: Competitive programming" style="min-width:44px"><a href="https://livecodebench.github.io/leaderboard.html" target="_blank">🖥 LCB</a><span class="sa">↕</span></th>
<th onclick="srt(35)" class="gB" data-col="35" title="Terminal-Bench 2.0: Agentic terminal tasks — tbench.ai official (best agent+model combo)" style="min-width:50px"><a href="https://www.tbench.ai/leaderboard/terminal-bench/2.0" target="_blank">🖥 TB2.0★</a><span class="sa">↕</span></th>
<th onclick="srt(36)" class="gB" data-col="36" title="SciCode: Scientific coding — 338 sub-problems from 80 real research tasks (AA independent)" style="min-width:48px"><a href="https://scicode-bench.github.io/" target="_blank">🔬 SciCode★</a><span class="sa">↕</span></th>
<th onclick="srt(14)" class="gB" style="opacity:.75" data-col="14" title="SWE-Verified: ⚠ Contamination risk, 59.4% tasks found defective by OpenAI audit" style="min-width:48px"><a href="https://www.swebench.com" target="_blank">💻 SWE-V⚠</a><span class="sa">↕</span></th>
<th onclick="srt(15)" class="gM" data-col="15" title="MMMLU: 50+ languages multilingual MMLU" style="min-width:52px"><a href="https://huggingface.co/datasets/openai/MMMLU" target="_blank">🌍 MMMLU</a><span class="sa">↕</span></th>
<th onclick="srt(16)" class="gT" data-col="16" style="min-width:44px">📥 CtxIn<span class="sa">↕</span></th>
<th onclick="srt(17)" class="gT" data-col="17" style="min-width:44px">📤 CtxOut<span class="sa">↕</span></th>
<th onclick="srt(18)" class="gT" data-col="18" style="min-width:44px">⚡ tok/s<span class="sa">↕</span></th>
<th onclick="srt(19)" class="gN" data-col="19" title="TTFT — lower is faster" style="min-width:44px">⏱ TTFT<span class="sa">↕</span></th>
<th class="gN" data-col="20" style="min-width:80px">👁 Vision</th>
<th class="gN" data-col="21" style="min-width:80px">⚙ Arch</th>
<th onclick="srt(22)" class="gP" data-col="22" style="min-width:48px">🏆 ELO<span class="sa">↕</span></th>
<th class="gP" data-col="23" style="min-width:52px">📄 License</th>
<th onclick="srt(24)" class="gP" data-col="24" style="min-width:50px">💰 $/M in<span class="sa">↕</span></th>
</tr>
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<tbody id="TB"></tbody>
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</div>
<div class="leg">
<span class="lt">Grade:</span>
<div class="li"><div class="ld" style="background:#6366f1"></div>S≥90%</div>
<div class="li"><div class="ld" style="background:#0d9488"></div>A≥75%</div>
<div class="li"><div class="ld" style="background:#d97706"></div>B≥60%</div>
<div class="li"><div class="ld" style="background:#e11d48"></div>C&lt;60%</div>
<span style="color:#db2777;font-size:9px;margin-left:8px">★ = New in v1.0</span>
<span style="color:#16a34a;font-size:9px;margin-left:6px">💚 Green row = Open-source value pick</span>
<span style="font-family:var(--font-mono);font-size:8px;color:#0ea5e9;margin-left:6px">🧩 ARC-AGI-2 = arcprize.org official</span>
<span style="font-family:var(--font-mono);font-size:8px;color:#7c3aed;margin-left:6px">🧬 Metacog = FINAL-Bench official (8 of 9 tested models in bench)</span>
<span style="font-family:var(--font-mono);font-size:8px;color:#0ea5e9;margin-left:6px">🖥 TB2.0 = tbench.ai official · 🔬 SciCode = AA independent</span>
<span style="font-family:var(--font-mono);font-size:8px;color:#e11d48;margin-left:6px">⚙ Score = Avg × √(N/10): <span style="color:#16a34a">✓</span>Full(7+) <span style="color:#d97706">◐</span>Partial(4-6) <span style="color:#e11d48">○</span>Limited(&lt;4) · v1.5: LCB replaces SWE-V</span>
</div>
</div>
<!-- ========== TAB: VISION LANGUAGE ========== -->
<div id="vision" class="tpane">
<div style="margin-bottom:14px;">
<div style="display:flex;align-items:center;gap:8px;margin-bottom:8px;">
<span style="background:linear-gradient(135deg,#7c3aed,#6366f1);color:#fff;font-size:8px;font-family:var(--font-mono);font-weight:800;padding:3px 8px;border-radius:20px;letter-spacing:1px;">👁 VISION LANGUAGE v2.1</span>
<span style="font-family:var(--font-mono);font-size:9px;color:var(--text-muted);">Flagship + Open-Source SOTA · 15 Models × 10 Key Benchmarks + Detailed Comparison</span>
</div>
<p style="font-size:9.5px;color:var(--text-sec);line-height:1.7;">
<b>NEW v2.1:</b> Flagship VLM comparison across 10 multimodal models. Sources: Vals.ai, Google DeepMind, OpenAI official, Anthropic, InternVL3 paper, Qwen official.
<b>Confidence:</b> <span style="color:#16a34a;font-weight:700">✓✓</span> Cross-verified · <span style="color:#d97706;font-weight:700">✓</span> Single source · <span style="color:#e11d48;font-weight:700">~</span> Self-reported
</p>
</div>
<!-- FLAGSHIP VLM COMPARISON -->
<div style="margin-bottom:6px"><span style="font-size:10px;font-weight:800;color:var(--text)">🏆 Flagship VLM Comparison</span> <span style="font-size:8px;color:var(--text-muted);font-family:var(--font-mono)">· Cross-provider multimodal intelligence ranking</span></div>
<div class="tw" style="margin-bottom:14px;">
<table>
<thead>
<tr>
<th style="min-width:140px;text-align:left;font-size:7.5px">Model</th>
<th style="min-width:48px;font-size:7px">MMMU</th>
<th style="min-width:48px;font-size:7px">MMMU-Pro</th>
<th style="min-width:48px;font-size:7px">MathVista</th>
<th style="min-width:48px;font-size:7px">AI2D</th>
<th style="min-width:48px;font-size:7px">OCRBench</th>
<th style="min-width:48px;font-size:7px">MMStar</th>
<th style="min-width:48px;font-size:7px">Hallusion</th>
<th style="min-width:48px;font-size:7px">MMBenchEN</th>
<th style="min-width:48px;font-size:7px">RealWorldQA</th>
<th style="min-width:48px;font-size:7px">VideoMME</th>
</tr>
</thead>
<tbody id="VTF"></tbody>
</table>
</div>
<!-- LIGHTWEIGHT MODEL COMPARISON (ORIGINAL) -->
<div style="margin-bottom:6px"><span style="font-size:10px;font-weight:800;color:var(--text)">⚡ Lightweight / Edge Model Detail</span> <span style="font-size:8px;color:var(--text-muted);font-family:var(--font-mono)">· Qwen official 34 benchmarks · 5 models</span></div>
<div class="tw" style="margin-bottom:14px;">
<table id="VT">
<thead>
<tr><th colspan="2" style="background:linear-gradient(135deg,#7c3aed11,#6366f111);color:#7c3aed;text-align:left;padding-left:10px;font-size:9px;letter-spacing:1px">STEM & PUZZLE</th>
<th style="min-width:50px;font-size:7.5px">MMMU</th><th style="min-width:50px;font-size:7.5px">MMMU-Pro</th><th style="min-width:50px;font-size:7.5px">MathVision</th><th style="min-width:50px;font-size:7.5px">MathVista</th><th style="min-width:50px;font-size:7.5px">We-Math</th><th style="min-width:50px;font-size:7.5px">DynaMath</th><th style="min-width:50px;font-size:7.5px">ZEROBench</th><th style="min-width:50px;font-size:7.5px">ZERO_sub</th><th style="min-width:50px;font-size:7.5px">VlmsBlind</th><th style="min-width:50px;font-size:7.5px">BabyVis</th></tr>
</thead>
<tbody id="VTB1"></tbody>
</table>
</div>
<div class="tw" style="margin-bottom:14px;">
<table>
<thead>
<tr><th colspan="2" style="background:linear-gradient(135deg,#0d948811,#6366f111);color:#0d9488;text-align:left;padding-left:10px;font-size:9px;letter-spacing:1px">GENERAL VQA & DOCUMENT</th>
<th style="min-width:50px;font-size:7.5px">RealWorldQA</th><th style="min-width:50px;font-size:7.5px">MMStar</th><th style="min-width:50px;font-size:7.5px">MMBenchEN</th><th style="min-width:50px;font-size:7.5px">SimpleVQA</th><th style="min-width:50px;font-size:7.5px">Hallusion</th><th style="min-width:50px;font-size:7.5px">OmniDoc</th><th style="min-width:50px;font-size:7.5px">CharXiv</th><th style="min-width:50px;font-size:7.5px">MMLongDoc</th><th style="min-width:50px;font-size:7.5px">CC-OCR</th><th style="min-width:50px;font-size:7.5px">AI2D</th><th style="min-width:50px;font-size:7.5px">OCRBench</th></tr>
</thead>
<tbody id="VTB2"></tbody>
</table>
</div>
<div class="tw" style="margin-bottom:14px;">
<table>
<thead>
<tr><th colspan="2" style="background:linear-gradient(135deg,#d9770611,#6366f111);color:#d97706;text-align:left;padding-left:10px;font-size:9px;letter-spacing:1px">SPATIAL · VIDEO · AGENT · MEDICAL</th>
<th style="min-width:46px;font-size:7px">ERQA</th><th style="min-width:46px;font-size:7px">CountB</th><th style="min-width:46px;font-size:7px">EmbSpatial</th><th style="min-width:46px;font-size:7px">RefSpatial</th><th style="min-width:46px;font-size:7px">LingoQA</th><th style="min-width:46px;font-size:7px">VidMME+</th><th style="min-width:46px;font-size:7px">VidMME</th><th style="min-width:46px;font-size:7px">VidMMMU</th><th style="min-width:46px;font-size:7px">MLVU</th><th style="min-width:46px;font-size:7px">MMVU</th><th style="min-width:46px;font-size:7px">ScreenSP</th><th style="min-width:46px;font-size:7px">OSWorld</th><th style="min-width:46px;font-size:7px">Android</th><th style="min-width:46px;font-size:7px">TIR-B</th><th style="min-width:46px;font-size:7px">SLAKE</th><th style="min-width:46px;font-size:7px">PMC-VQA</th><th style="min-width:46px;font-size:7px">MedXpert</th></tr>
</thead>
<tbody id="VTB3"></tbody>
</table>
</div>
<div class="leg">
<span class="lt">Flagship VLM:</span>
<div class="li"><div class="ld" style="background:#4285f4"></div>Gemini 3.1 Pro</div>
<div class="li"><div class="ld" style="background:#34a853"></div>Gemini 3 Flash</div>
<div class="li"><div class="ld" style="background:#10a37f"></div>GPT-5.2</div>
<div class="li"><div class="ld" style="background:#d97706"></div>Claude Opus 4.6</div>
<div class="li"><div class="ld" style="background:#ef4444"></div>Grok 4 Heavy</div>
<div class="li"><div class="ld" style="background:#3b82f6"></div>InternVL3.5-241B</div>
<div class="li"><div class="ld" style="background:#6366f1"></div>InternVL3-78B</div>
<div class="li"><div class="ld" style="background:#f97316"></div>Qwen2.5-VL-72B</div>
<div class="li"><div class="ld" style="background:#8b5cf6"></div>Kimi-VL-A3B</div>
<div class="li"><div class="ld" style="background:#ec4899"></div>GPT-5 (original)</div>
<br>
<span class="lt">Edge Models:</span>
<div class="li"><div class="ld" style="background:#10a37f"></div>GPT-5-Nano</div>
<div class="li"><div class="ld" style="background:#4285f4"></div>Gemini-2.5-FL-Lite</div>
<div class="li"><div class="ld" style="background:#f97316"></div>Qwen3-VL-30B-A3B</div>
<div class="li"><div class="ld" style="background:#ea580c"></div>Qwen3.5-9B</div>
<div class="li"><div class="ld" style="background:#c2410c"></div>Qwen3.5-4B</div>
<span style="font-family:var(--font-mono);font-size:8px;color:var(--text-muted);margin-left:8px">Source: Qwen official benchmarks · BabyVision & TIR-Bench show "with CI / without CI"</span>
</div>
</div>
<!-- ========== TAB: AGENT BENCH ========== -->
<div id="agent" class="tpane">
<div style="margin-bottom:14px;">
<div style="display:flex;align-items:center;gap:8px;margin-bottom:8px;">
<span style="background:linear-gradient(135deg,#0d9488,#059669);color:#fff;font-size:8px;font-family:var(--font-mono);font-weight:800;padding:3px 8px;border-radius:20px;letter-spacing:1px;">🤖 AGENT BENCH v2.1</span>
<span style="font-family:var(--font-mono);font-size:9px;color:var(--text-muted);">Agentic Capability Comparison — Desktop, Web, Terminal, Tool Use</span>
</div>
<p style="font-size:9.5px;color:var(--text-sec);line-height:1.7;">
Sources: Anthropic, OpenAI, Google DeepMind official announcements + Onyx AI, Vellum, NxCode, DataCamp independent reviews.<br>
<b>⚠ Note:</b> Agent scores vary significantly by scaffolding (agent framework). Values shown are best reported across implementations. "—" = not published / not applicable.
</p>
</div>
<div class="tw" style="margin-bottom:14px;">
<table>
<thead>
<tr>
<th class="c-model" style="text-align:left;padding-left:10px;min-width:140px">Model</th>
<th style="min-width:56px;font-size:7.5px;color:#0d9488" title="OSWorld-Verified: Autonomous desktop GUI navigation — clicking, typing, multi-step workflows">🖥 OSWorld</th>
<th style="min-width:56px;font-size:7.5px;color:#6366f1" title="τ²-bench Telecom: Multi-turn tool calling in telecom domain">🔧 τ²-bench</th>
<th style="min-width:56px;font-size:7.5px;color:#d97706" title="BrowseComp: Autonomous web research — finding specific info across the web">🌐 BrowseComp</th>
<th style="min-width:56px;font-size:7.5px;color:#7c3aed" title="Terminal-Bench 2.0: tbench.ai agentic terminal tasks">🖥 TB 2.0</th>
<th style="min-width:56px;font-size:7.5px;color:#e11d48" title="GDPval-AA Elo: Real-world professional knowledge work across 44 occupations">📋 GDPval</th>
<th style="min-width:56px;font-size:7.5px;color:#0081fb" title="SWE-Bench Pro: Scale AI SEAL standardized scaffolding">🏗 SWE-Pro</th>
<th style="min-width:56px;font-size:7.5px;color:#f97316" title="BFCL v4: Berkeley Function-Calling Leaderboard">🔧 BFCL v4</th>
<th style="min-width:56px;font-size:7.5px;color:#14b8a6" title="AndroidWorld: Mobile automation benchmark">📱 Android</th>
</tr>
</thead>
<tbody id="ATB"></tbody>
</table>
</div>
<div class="leg">
<span class="lt">Agent Benchmarks:</span>
<span style="font-family:var(--font-mono);font-size:8px;color:var(--text-muted)">OSWorld = desktop GUI · τ²-bench = multi-turn tools · BrowseComp = web research · TB2.0 = terminal · GDPval = professional work · SWE-Pro = SEAL coding · BFCL = function calling</span>
</div>
</div>
<!-- ========== TAB: IMAGE GEN ========== -->
<div id="imggen" class="tpane">
<div style="margin-bottom:14px;">
<div style="display:flex;align-items:center;gap:8px;margin-bottom:8px;">
<span style="background:linear-gradient(135deg,#e11d48,#f43f5e);color:#fff;font-size:8px;font-family:var(--font-mono);font-weight:800;padding:3px 8px;border-radius:20px;letter-spacing:1px;">🖼 IMAGE GENERATION v2.1</span>
<span style="font-family:var(--font-mono);font-size:9px;color:var(--text-muted);">10 Models — Qualitative & Arena Ranking Comparison</span>
</div>
<p style="font-size:9.5px;color:var(--text-sec);line-height:1.7;">
Image generation lacks standardized numeric benchmarks like LLMs. Rankings combine LM Arena Elo, expert reviews (Cliprise, Vellum, Awesome Agents), and community consensus.<br>
Dimensions: <b>Photorealism</b> · <b>Artistic Quality</b> · <b>Text Rendering</b> · <b>Prompt Adherence</b> · <b>Speed</b> · <b>Cost</b>. Ratings: ⬛S (top tier) · 🟦A (strong) · 🟧B (capable) · ⬜C (limited).
</p>
</div>
<div class="tw" style="margin-bottom:14px;">
<table>
<thead>
<tr>
<th class="c-model" style="text-align:left;padding-left:10px;min-width:150px">Model</th>
<th style="min-width:60px;font-size:7.5px">Provider</th>
<th style="min-width:50px;font-size:7.5px">Release</th>
<th style="min-width:50px;font-size:7.5px" title="LM Arena ranking or equivalent">🏆 Arena</th>
<th style="min-width:52px;font-size:7.5px">📷 Photo</th>
<th style="min-width:52px;font-size:7.5px">🎨 Art</th>
<th style="min-width:52px;font-size:7.5px">📝 Text</th>
<th style="min-width:52px;font-size:7.5px">🎯 Prompt</th>
<th style="min-width:52px;font-size:7.5px">⚡ Speed</th>
<th style="min-width:52px;font-size:7.5px">💰 Cost</th>
<th style="min-width:60px;font-size:7.5px">License</th>
</tr>
</thead>
<tbody id="ITB"></tbody>
</table>
</div>
<div class="leg">
<span class="lt">Ratings:</span>
<span style="font-family:var(--font-mono);font-size:9px;color:#6366f1">⬛S = Top tier</span>
<span style="font-family:var(--font-mono);font-size:9px;color:#0d9488">🟦A = Strong</span>
<span style="font-family:var(--font-mono);font-size:9px;color:#d97706">🟧B = Capable</span>
<span style="font-family:var(--font-mono);font-size:9px;color:#94a3b8">⬜C = Limited</span>
<span style="font-family:var(--font-mono);font-size:8px;color:var(--text-muted);margin-left:8px">Sources: LM Arena, Cliprise, Vellum, Awesome Agents, community consensus (Feb 2026)</span>
</div>
</div>
<!-- ========== TAB: VIDEO GEN ========== -->
<div id="vidgen" class="tpane">
<div style="margin-bottom:14px;">
<div style="display:flex;align-items:center;gap:8px;margin-bottom:8px;">
<span style="background:linear-gradient(135deg,#f43f5e,#ec4899);color:#fff;font-size:8px;font-family:var(--font-mono);font-weight:800;padding:3px 8px;border-radius:20px;letter-spacing:1px;">🎬 VIDEO GENERATION v2.1</span>
<span style="font-family:var(--font-mono);font-size:9px;color:var(--text-muted);">10 Models — Quality · Motion · Audio · Duration · Resolution · Cost</span>
</div>
<p style="font-size:9.5px;color:var(--text-sec);line-height:1.7;">
Sources: LaoZhang AI, Pinggy, RizzGen, CrePal, TeamDay, Awesome Agents (Feb 2026). All models rated on S/A/B/C scale.<br>
<b>2026 breakthroughs:</b> Native audio generation (Veo 3.1, Sora 2, Kling 3.0) · Multi-shot sequences (Kling 3.0) · 4K output (LTX-2) · Open-source parity (Wan 2.6)
</p>
</div>
<div class="tw" style="margin-bottom:14px;">
<table>
<thead>
<tr>
<th class="c-model" style="text-align:left;padding-left:10px;min-width:140px">Model</th>
<th style="min-width:56px;font-size:7.5px">Provider</th>
<th style="min-width:44px;font-size:7.5px">Release</th>
<th style="min-width:50px;font-size:7.5px">📷 Quality</th>
<th style="min-width:50px;font-size:7.5px">🎬 Motion</th>
<th style="min-width:50px;font-size:7.5px">🔊 Audio</th>
<th style="min-width:50px;font-size:7.5px">🎯 Prompt</th>
<th style="min-width:50px;font-size:7.5px">⏱ Max Dur</th>
<th style="min-width:50px;font-size:7.5px">📐 Max Res</th>
<th style="min-width:50px;font-size:7.5px">💰 Cost</th>
<th style="min-width:56px;font-size:7.5px">License</th>
</tr>
</thead>
<tbody id="VIDTB"></tbody>
</table>
</div>
<div class="leg">
<span class="lt">Key:</span>
<span style="font-family:var(--font-mono);font-size:8px;color:var(--text-muted)">Quality = visual fidelity · Motion = physics/consistency · Audio = native sound gen · Prompt = adherence to description · Duration = max single generation · Open = open-source weights available</span>
</div>
</div>
<!-- ========== TAB: MUSIC GEN ========== -->
<div id="musicgen" class="tpane">
<div style="margin-bottom:14px;">
<div style="display:flex;align-items:center;gap:8px;margin-bottom:8px;">
<span style="background:linear-gradient(135deg,#8b5cf6,#6366f1);color:#fff;font-size:8px;font-family:var(--font-mono);font-weight:800;padding:3px 8px;border-radius:20px;letter-spacing:1px;">🎵 MUSIC / AUDIO GEN v2.1</span>
<span style="font-family:var(--font-mono);font-size:9px;color:var(--text-muted);">8 Models — Vocal · Instrumental · Lyrics · Duration · Style Range</span>
</div>
<p style="font-size:9.5px;color:var(--text-sec);line-height:1.7;">
<b>⚠ No standardized benchmarks exist for music generation.</b> Rankings based on expert reviews, community consensus, and platform capabilities.<br>
Dimensions: <b>Vocal Quality</b> · <b>Instrumental</b> · <b>Lyrics Understanding</b> · <b>Max Duration</b> · <b>Style Range</b> · <b>Commercial Rights</b>
</p>
</div>
<div class="tw" style="margin-bottom:14px;">
<table>
<thead>
<tr>
<th class="c-model" style="text-align:left;padding-left:10px;min-width:140px">Model</th>
<th style="min-width:56px;font-size:7.5px">Provider</th>
<th style="min-width:44px;font-size:7.5px">Release</th>
<th style="min-width:50px;font-size:7.5px">🎤 Vocal</th>
<th style="min-width:50px;font-size:7.5px">🎸 Instru</th>
<th style="min-width:50px;font-size:7.5px">📝 Lyrics</th>
<th style="min-width:50px;font-size:7.5px">🎨 Styles</th>
<th style="min-width:50px;font-size:7.5px">⏱ Max Dur</th>
<th style="min-width:50px;font-size:7.5px">💰 Cost</th>
<th style="min-width:56px;font-size:7.5px">License</th>
</tr>
</thead>
<tbody id="MUSTB"></tbody>
</table>
</div>
<div class="leg">
<span class="lt">Note:</span>
<span style="font-family:var(--font-mono);font-size:8px;color:var(--text-muted)">Music AI is the least benchmarked domain. Ratings reflect community consensus + expert reviews. Commercial rights vary — check each provider's terms before publishing.</span>
</div>
</div>
<!-- ========== TAB: INTERACTIVE TOOLS ========== -->
<div id="three" class="tpane">
<div style="margin-bottom:10px;">
<div style="display:flex;align-items:center;gap:8px;margin-bottom:8px;">
<span style="background:linear-gradient(135deg,#6366f1,#0d9488);color:#fff;font-size:8px;font-family:var(--font-mono);font-weight:800;padding:3px 8px;border-radius:20px;letter-spacing:1px;">🔍 INTERACTIVE TOOLS v2.1</span>
<span style="font-family:var(--font-mono);font-size:9px;color:var(--text-muted);">Find · Compare · Verify · Visualize — 67 models across all modalities</span>
</div>
</div>
<div style="display:flex;gap:0;border-bottom:1px solid var(--border);margin-bottom:12px;">
<div class="tab on" onclick="show3DSub('finder',this)" style="font-size:9.5px;padding:8px 14px">🔍 Model Finder</div>
<div class="tab" onclick="show3DSub('h2h',this)" style="font-size:9.5px;padding:8px 14px">⚔ Head-to-Head</div>
<div class="tab" onclick="show3DSub('coverage',this)" style="font-size:9.5px;padding:8px 14px">📊 Trust Map</div>
<div class="tab" onclick="show3DSub('barrace',this)" style="font-size:9.5px;padding:8px 14px">🏁 Bar Race</div>
</div>
<!-- SUB: MODEL FINDER — "What's the best model for MY budget?" -->
<div id="sub_finder" class="sub3d" style="display:block">
<div style="font-size:10px;color:var(--text-sec);margin-bottom:10px;line-height:1.7">
<b style="color:var(--ac)">Find your optimal model:</b> Filter by price, capability, and type. Each dot = one model. X = Price · Y = Composite Score. Hover for details. The best value models are in the <b style="color:#16a34a">top-left zone</b> (high score, low cost).
</div>
<div style="display:flex;gap:6px;flex-wrap:wrap;margin-bottom:10px;align-items:center;">
<span style="font-size:8.5px;font-family:var(--font-mono);color:var(--text-muted);font-weight:600">FILTER:</span>
<button class="fb on" onclick="finderFilter('all',this)">All LLMs</button>
<button class="fb" onclick="finderFilter('open',this)">🔓 Open Only</button>
<button class="fb" onclick="finderFilter('closed',this)">🔒 Closed Only</button>
<button class="fb" onclick="finderFilter('cheap',this)">💚 Under $1/M</button>
<button class="fb" onclick="finderFilter('free',this)">🆓 Free</button>
</div>
<div style="background:var(--surface);border:1px solid var(--border);border-radius:var(--radius);overflow:hidden;position:relative;height:440px" id="finderBox">
<canvas id="finderCanvas" style="width:100%;height:100%;display:block"></canvas>
<div id="finderTip" style="display:none;position:absolute;background:rgba(15,23,42,.95);color:#e2e8f0;border:1px solid var(--ac);border-radius:8px;padding:10px 14px;font-family:var(--font-mono);font-size:9px;pointer-events:none;z-index:10;line-height:1.8;max-width:240px;backdrop-filter:blur(8px)"></div>
</div>
</div>
<!-- SUB: HEAD-TO-HEAD — "A vs B, which is better?" -->
<div id="sub_h2h" class="sub3d" style="display:none">
<div style="font-size:10px;color:var(--text-sec);margin-bottom:10px;line-height:1.7">
<b style="color:var(--ac)">Head-to-Head:</b> Select two models and instantly see who wins on each benchmark. Green = winner. The wider the bar, the bigger the gap.
</div>
<div style="display:flex;gap:10px;margin-bottom:12px;flex-wrap:wrap;align-items:center;">
<select id="h2hA" onchange="drawH2H()" style="padding:5px 10px;border:1px solid var(--border);border-radius:8px;font-family:var(--font-mono);font-size:10px;background:var(--surface);color:var(--text);min-width:160px"></select>
<span style="font-family:var(--font-mono);font-size:14px;font-weight:800;color:var(--ac)">VS</span>
<select id="h2hB" onchange="drawH2H()" style="padding:5px 10px;border:1px solid var(--border);border-radius:8px;font-family:var(--font-mono);font-size:10px;background:var(--surface);color:var(--text);min-width:160px"></select>
</div>
<div style="background:var(--surface);border:1px solid var(--border);border-radius:var(--radius);overflow:hidden;height:440px" id="h2hBox">
<canvas id="h2hCanvas" style="width:100%;height:100%;display:block"></canvas>
</div>
</div>
<!-- SUB: TRUST MAP — "Which models have the most verified data?" -->
<div id="sub_coverage" class="sub3d" style="display:none">
<div style="font-size:10px;color:var(--text-sec);margin-bottom:10px;line-height:1.7">
<b style="color:var(--ac)">Benchmark Coverage Trust Map:</b> Each cell = one model × one benchmark. <span style="color:#6366f1">■ Confirmed</span> = score verified from 2+ sources. <span style="color:#d97706">■ Self-reported</span> = provider only. <span style="color:#e2e8f0">□ Missing</span> = no data. <b>More coverage = more trustworthy ranking.</b>
</div>
<div style="background:var(--surface);border:1px solid var(--border);border-radius:var(--radius);overflow:auto;max-height:480px" id="covBox">
<canvas id="covCanvas" style="display:block"></canvas>
</div>
</div>
<!-- SUB: BAR RACE — kept and improved -->
<div id="sub_barrace" class="sub3d" style="display:none">
<div style="font-size:10px;color:var(--text-sec);margin-bottom:8px;line-height:1.7">
<b style="color:var(--ac)">AI Evolution Timeline:</b> Watch frontier models evolve from Jan 2025 → Mar 2026. Bar length = composite score. Press ▶ to animate 18 months of AI progress.
</div>
<div style="background:#0f172a;border-radius:var(--radius);overflow:hidden;height:480px;border:1px solid var(--border);position:relative" id="barraceBox">
<canvas id="barraceCanvas" style="width:100%;height:100%;display:block"></canvas>
<button onclick="startBarRace()" style="position:absolute;top:12px;right:12px;background:linear-gradient(135deg,#6366f1,#4f46e5);color:#fff;border:none;border-radius:20px;padding:6px 16px;font-family:var(--font-mono);font-size:10px;font-weight:700;cursor:pointer;z-index:5">▶ Play</button>
<div id="brYear" style="position:absolute;top:12px;left:12px;font-family:var(--font-mono);font-size:22px;font-weight:800;color:#6366f1;z-index:5">2025.01</div>
</div>
</div>
</div>
<!-- ========== TAB: REPORT ========== -->
<div id="report" class="tpane">
<div id="reportContent" style="max-width:800px;margin:0 auto;">
<div style="display:flex;justify-content:space-between;align-items:center;margin-bottom:14px;flex-wrap:wrap;gap:8px">
<div>
<span style="background:linear-gradient(135deg,#6366f1,#0d9488);color:#fff;font-size:8px;font-family:var(--font-mono);font-weight:800;padding:3px 8px;border-radius:20px;letter-spacing:1px;">📄 INTELLIGENCE REPORT</span>
<span style="font-family:var(--font-mono);font-size:9px;color:var(--text-muted);margin-left:6px" id="rptDate"></span>
</div>
<div style="display:flex;gap:6px">
<button onclick="downloadPDF()" style="background:linear-gradient(135deg,#e11d48,#f43f5e);color:#fff;border:none;border-radius:8px;padding:6px 14px;font-family:var(--font-mono);font-size:9px;font-weight:700;cursor:pointer">📥 PDF</button>
<button onclick="downloadDOCX()" style="background:linear-gradient(135deg,#6366f1,#4f46e5);color:#fff;border:none;border-radius:8px;padding:6px 14px;font-family:var(--font-mono);font-size:9px;font-weight:700;cursor:pointer">📥 DOCX</button>
</div>
</div>
<!-- EXECUTIVE SUMMARY -->
<div style="background:var(--surface);border:1px solid var(--border);border-radius:var(--radius);padding:20px;margin-bottom:14px;box-shadow:var(--shadow)">
<h2 style="font-size:14px;font-weight:800;color:var(--text);margin-bottom:12px;letter-spacing:-0.5px">🏆 Executive Summary</h2>
<div id="rptSummary" style="font-size:11px;color:var(--text-sec);line-height:2"></div>
</div>
<!-- CATEGORY WINNERS -->
<div style="background:var(--surface);border:1px solid var(--border);border-radius:var(--radius);padding:20px;margin-bottom:14px;box-shadow:var(--shadow)">
<h2 style="font-size:14px;font-weight:800;color:var(--text);margin-bottom:12px;letter-spacing:-0.5px">🥇 Category Winners</h2>
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<h2 style="font-size:14px;font-weight:800;color:var(--text);margin-bottom:12px;letter-spacing:-0.5px">📊 Top 10 LLM Ranking</h2>
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<th style="text-align:center;padding:6px 4px;font-family:var(--font-mono);font-size:8px;color:var(--text-muted)">Score</th>
<th style="text-align:center;padding:6px 4px;font-family:var(--font-mono);font-size:8px;color:var(--text-muted)">Coverage</th>
<th style="text-align:center;padding:6px 4px;font-family:var(--font-mono);font-size:8px;color:var(--text-muted)">Type</th>
<th style="text-align:right;padding:6px 4px;font-family:var(--font-mono);font-size:8px;color:var(--text-muted)">Price</th>
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<h2 style="font-size:14px;font-weight:800;color:var(--text);margin-bottom:12px;letter-spacing:-0.5px">💡 Key Insights</h2>
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<b style="color:var(--text)">Data Confidence:</b>
<span style="color:#16a34a;font-weight:700">✓✓</span> Cross-verified (2+ independent sources) ·
<span style="color:#d97706;font-weight:700">✓</span> Single source (provider official) ·
<span style="color:#e11d48;font-weight:700">~</span> Self-reported / unverified ·
<span style="color:#94a3b8">—</span> No data available<br>
<b>Last verified:</b> <span id="rptVerified"></span> · <b>Methodology:</b> 5-Axis Intelligence Framework (Knowledge · Expert Reasoning · Abstract · Metacognition · Execution)
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<!-- ROW 1: ARC-AGI-2 + Metacog Delta -->
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<h3>🧩 ARC-AGI-2 — Abstract Reasoning Frontier</h3>
<p>Official arcprize.org · Vertical bars by score · Contamination-proof visual reasoning benchmark</p>
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<div class="chart-insight"><b>Key:</b> Gemini 3.1 Pro leads at <b>77.1%</b> (verified arcprize.org). Claude Opus 4.6 68.8% · GPT-5.2 52.9% · Kimi K2.5 12.1%. Each model shows distinct reasoning profile — ARC-AGI-2 is the most contamination-proof benchmark.</div>
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<h3>🧬 Metacog: Baseline → Self-Correction Gain (Δ)</h3>
<p>FINAL-Bench official · Baseline FINAL Score vs MetaCog condition · Error Recovery drives 94.8% of gains</p>
<canvas id="cMetaDelta" height="220"></canvas>
<div class="chart-insight"><b>Key:</b> Claude Opus 4.6 has lowest baseline (rank 9) but <b>largest Δ gain (+20.13)</b> — strongest self-correction. Kimi K2.5 highest baseline but smallest gain. Declarative–Procedural gap persists across all models.</div>
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<h3>🕸 Capability Radar — TOP 6 Multi-Axis Profile</h3>
<p>MMLU-Pro · GPQA · AIME · HLE · ARC-AGI-2 · MMMLU · Each axis normalized to 100</p>
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<div class="chart-insight"><b>Key:</b> No single model dominates all axes. Gemini leads MMMLU+HLE, GPT-5.2 leads MMLU-Pro, Kimi K2.5 exceptional on MMLU-Pro 92.0. Different strengths suggest routing strategies.</div>
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<h3>📊 Capability Domains — Reasoning vs Coding vs Language</h3>
<p>Grouped bars: Reasoning avg (GPQA+AIME+HLE) · Coding avg (SWE-Pro+LCB) · Language avg (MMLU-Pro+MMMLU+IFEval)</p>
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<div class="chart-insight"><b>Key:</b> Claude Opus 4.6 leads Coding domain. Gemini 3.1 Pro leads Language. GPT-5.2 most balanced across all three domains — ideal for general-purpose deployment.</div>
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<h3>💰 Performance vs Cost — Value Frontier Map</h3>
<p>X = Input price log scale ($/M tokens) · Y = Composite Score · Top-left quadrant = elite value zone</p>
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<div class="chart-insight"><b>Value leaders:</b> DeepSeek V3.2 ($0.14/M, score ~74) and GLM-5 ($0.35/M) offer exceptional open-weight value. GPT-OSS-120B is truly free with competitive performance.</div>
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<h3>🏭 Provider Strength — Average Score by Company</h3>
<p>Average composite score across all models per provider · Shows lab-level consistency</p>
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<div class="chart-insight"><b>Key:</b> OpenAI strongest average (combining closed+OSS models). Alibaba's Qwen3.5 family shows remarkable breadth. DeepSeek punches above weight with MIT-licensed models.</div>
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<h3>📅 Intelligence Timeline — Score vs Release Date</h3>
<p>Bubble size = context window (log scale) · Color = provider · Rapid capability gains 2025→2026</p>
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<div class="chart-insight"><b>Key:</b> ~15-point score jump from Jan 2025 to Feb 2026. Feb 2026 releases (GPT-5.2, Gemini 3.1 Pro) establish new ceiling. Context window growth independent of intelligence score.</div>
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<h3>⚖ Open vs Closed — Distribution Comparison</h3>
<p>Score distribution: Open-weight (18 models) vs Closed-API (6 models) · Box plot style with individual points</p>
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<div class="chart-insight"><b>Key:</b> Open-weight models now overlap significantly with closed-API. Top open models (Kimi K2.5, Qwen3.5-397B) match or exceed many closed offerings — open-source gap is closing.</div>
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<h3>📐 Benchmark Score Variance — Consistency Analysis</h3>
<p>For each benchmark: show min/max/mean across all models · Reveals benchmark difficulty &amp; discrimination power</p>
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<div class="chart-insight"><b>Key:</b> HLE shows widest variance (7.0–44.9) = best discrimination. ARC-AGI-2 also highly discriminating (12.1–88.1). AIME25 scores cluster high — many models saturating it.</div>
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<h3>🌡 Full Benchmark Heatmap — 39 Models × 11 Benchmarks</h3>
<p>Color intensity = score · White/light = unreported · Indigo = high · Reveals capability patterns across the entire landscape</p>
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<div class="fni"><b>🧩 ARC-AGI-2 ★NEW — Abstract Reasoning</b><p>Tests novel visual pattern completion — cannot be solved by memorization. <a href="https://arcprize.org/arc-agi-2" target="_blank">arcprize.org</a>. Gemini 3.1 Pro 77.1% (verified) · Claude Opus 4.6 68.8% · GPT-5.4 73.3% · GPT-5.2 52.9% · Kimi K2.5 12.1%. Most contamination-proof benchmark available.</p></div>
<div class="fni" style="border-left:4px solid #c9002b;background:linear-gradient(135deg,#c9002b08,#00347808)"><b>🇰🇷 Korean Sovereign AI — National Foundation Model Project</b><p>
Ministry of Science and ICT "National AI Foundation Model Project" as of 2026.02 — 4 elite teams: <b>LG AI Research (K-EXAONE)</b> · <b>SK Telecom (A.X K1)</b> · <b>Upstage (Solar Open 100B)</b> · <b>Motif Technologies</b>. Plus <b>KT (Mi:dm 2.0)</b> as independent Korea-centric AI.<br>
• 1st evaluation (2026.01.15): 5 teams → 3 teams (Naver Cloud & NC AI eliminated)<br>
• Wildcard round (2026.02.20): Motif Technologies added → 4-team structure<br>
• K-EXAONE: 1st place in evaluation · 72-point avg across 13 benchmarks · AA open-weight top 10 · 236B MoE<br>
• Solar Open 100B: AIME 84.3% · 19.7T tokens · 100B MoE · arXiv 2601.07022<br>
• A.X K1: Korea's first 500B parameter model · Apache 2.0 open-source<br>
• Goal: Achieve 95%+ of global AI model performance · Final 2 teams selected by 2027 · KRW 530B budget
</p></div>
<div class="fni"><b>🧬 Metacognitive ★NEW — FINAL-Bench</b><p>Official: <a href="https://huggingface.co/datasets/FINAL-Bench/Metacognitive" target="_blank">HF FINAL-Bench/Metacognitive</a>. 100 tasks, 9 SOTA models tested. Baseline FINAL Score: Kimi K2.5 68.71 · GPT-5.2 62.76 · GLM-5 62.50 · MiniMax-M2.5 60.54 · GPT-OSS-120B 60.42 · DeepSeek-V3.2 60.04 · GLM-4.7P 59.54 · Gemini 59.5 · Opus 4.6 56.04. ER (error recovery) accounts for 94.8% of self-correction gains. 8 of 9 tested models now in ALL Bench.</p></div>
<div class="fni"><b>📊 Composite Score — √Coverage Weighted (v1.5)</b><p><b>5-Axis Intelligence Framework:</b><br>
• <b>Knowledge</b> (MMLU-Pro) — 57K questions, highest statistical reliability<br>
• <b>Expert Reasoning</b> (GPQA + AIME + HLE) — PhD-level science + math olympiad + frontier-hard<br>
• <b>Abstract Reasoning</b> (ARC-AGI-2) — contamination-proof visual pattern recognition<br>
• <b>Metacognition</b> (FINAL Bench) — self-correction &amp; error recovery<br>
• <b>Execution</b> (SWE-Pro + BFCL + IFEval + LCB) — real coding + tool use + instruction following + competitive programming<br><br>
<b>Formula:</b> <code>Score = Avg(confirmed) × √(N/10)</code><br>
• N = confirmed benchmarks out of 10 · √ softens penalty: 10/10=×1.00 · 7/10=×0.84 · 4/10=×0.63<br>
• <b style="color:#16a34a">✓ Full</b> (7+) · <b style="color:#d97706">◐ Partial</b> (4-6) · <b style="color:#e11d48">○ Limited</b> (&lt;4)<br>
• <b>v1.5 change:</b> SWE-Verified removed from composite (59.4% tasks defective per OpenAI audit). Replaced with LiveCodeBench — continuously updated, contamination-resistant.</p></div>
<div class="fni"><b>📚 MMLU-Pro</b><p><a href="https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro" target="_blank">HF: TIGER-Lab/MMLU-Pro</a>. 57,000 expert-level questions across disciplines. Largest sample size → highest statistical reliability. Much harder than original MMLU. Gold standard general knowledge benchmark.</p></div>
<div class="fni"><b>🧠 GPQA Diamond ⭐</b><p><a href="https://huggingface.co/datasets/Idavidrein/gpqa" target="_blank">HF: Idavidrein/gpqa</a>. 198 PhD-level questions in biology, chemistry, physics. Human expert average ~65%. Highest discrimination power among frontier models.</p></div>
<div class="fni"><b>📐 AIME 2025</b><p><a href="https://artofproblemsolving.com/wiki/index.php/2025_AIME" target="_blank">AoPS: 2025 AIME</a>. American Invitational Mathematics Examination. 2025 problem set minimizes contamination. Tests mathematical reasoning and creative problem solving.</p></div>
<div class="fni"><b>🔭 HLE — Humanity's Last Exam</b><p><a href="https://huggingface.co/datasets/centerforaisafety/hle" target="_blank">HF: centerforaisafety/hle</a>. 2,500 expert-submitted questions. Intended to be the final closed-ended academic benchmark. Kimi K2.5 44.9% · Gemini 3.1 Pro 44.7% lead.</p></div>
<div class="fni"><b>🏗 SWE-Pro ⭐ Recommended</b><p><a href="https://scale.com/leaderboard/coding" target="_blank">scale.com/leaderboard/coding</a>. Scale AI SEAL, 1865 real repos. Contamination-free. ~35pt lower than SWE-Verified — honest measure of real coding. OpenAI recommends over Verified.</p></div>
<div class="fni"><b>💻 SWE-Verified ⚠ Caution</b><p><a href="https://www.swebench.com" target="_blank">swebench.com</a>. 59.4% of tasks found defective in OpenAI audit. Memorization/contamination risk. Reference only. Prefer SWE-Pro for accurate assessment.</p></div>
<div class="fni"><b>🔧 BFCL v4</b><p><a href="https://gorilla.cs.berkeley.edu/leaderboard.html" target="_blank">gorilla.cs.berkeley.edu</a>. Berkeley Function-Calling Leaderboard. Measures tool use and agent capability. Qwen3.5-122B world #1.</p></div>
<div class="fni"><b>📋 IFEval</b><p><a href="https://huggingface.co/datasets/google/IFEval" target="_blank">HF: google/IFEval</a>. Instruction following evaluation. Verifiable output constraints. Tests precision compliance.</p></div>
<div class="fni"><b>🖥 LiveCodeBench</b><p><a href="https://livecodebench.github.io/leaderboard.html" target="_blank">livecodebench.github.io</a>. Competitive programming from LeetCode, AtCoder, Codeforces. Continuously updated to prevent contamination.</p></div>
<div class="fni" style="border-left:4px solid #0ea5e9;background:linear-gradient(135deg,#0ea5e908,#6366f108)"><b>🖥 Terminal-Bench 2.0 ★NEW — Agentic Terminal Tasks</b><p>
<a href="https://www.tbench.ai/leaderboard/terminal-bench/2.0" target="_blank">tbench.ai</a>. Stanford + Laude Institute. ~80 tasks: compile code, train models, configure servers, play games, debug systems.<br>
• Best agent+model combo scores: Gemini 3.1 Pro 78.4% · GPT-5.3 Codex 77.3% · Claude Opus 4.6 74.7% · Gemini 3 Flash 64.3%<br>
• Tests real-world terminal capability — distinct from SWE-bench (file editing) · Agent framework matters: same model varies 10-20pts by scaffold<br>
• <b>Source:</b> tbench.ai official leaderboard (best model score across all agents)
</p></div>
<div class="fni" style="border-left:4px solid #7c3aed;background:linear-gradient(135deg,#7c3aed08,#6366f108)"><b>🔬 SciCode ★NEW — Scientific Research Coding</b><p>
<a href="https://scicode-bench.github.io/" target="_blank">scicode-bench.github.io</a>. 338 sub-problems from 80 real research tasks across 16 scientific disciplines (Chemistry, Physics, Biology, Math).<br>
• AA independent: Gemini 3.1 Pro 58.9% · Gemini 3 Pro 56.1% · GPT-5.2 Codex 54.6%<br>
• Only 3 model scores publicly available from AA — most models show "—" (data insufficient)<br>
• <b>Why included:</b> Fills the "science coding" gap — existing benchmarks (SWE-Pro, LCB) test SE/competitive only
</p></div>
<div class="fni"><b>🌍 MMMLU — Multilingual</b><p><a href="https://huggingface.co/datasets/openai/MMMLU" target="_blank">HF: openai/MMMLU</a>. MMLU in 57 languages. Gemini 3.1 Pro ~88% leads. Qwen3.5 officially supports 201 languages.</p></div>
<div class="fni"><b>⚙ Architecture</b><p>MoE = sparse activation (efficient), Dense = full params (quality), Hybrid = DeltaNet+MoE. Parentheses = active/total params. Active params determine inference cost. Qwen3.5-35B: 3B active → 194 tok/s.</p></div>
<div class="fni"><b>⏱ TTFT Latency</b><p>Time To First Token (seconds). Lower is faster. Mistral Large 3 0.3s · GPT-5.2 0.6s fastest. Reasoning models (DeepSeek R1 8s) are slower due to chain-of-thought. &lt;2s recommended for real-time apps.</p></div>
<div class="fni" style="border-left:4px solid #10a37f;background:linear-gradient(135deg,#10a37f06,#6366f106)"><b>🔥 GPT-5.4 — OpenAI's Most Capable Model (2026.03.05)</b><p>
<a href="https://openai.com/index/introducing-gpt-5-4/" target="_blank">OpenAI: Introducing GPT-5.4</a>. Dense reasoning model, Proprietary, released March 5, 2026.<br>
• <b style="color:#10a37f">HLE 52.1% — ALL Bench #1</b> (dethroning Kimi K2.5 44.9%) · GPT-5.4 Pro reaches 58.7%<br>
• ARC-AGI-2: 73.3% (+20pt from GPT-5.2) · Pro: 83.3% (approaching Gemini 3.1 Pro 88.1%)<br>
• SWE-Pro: 57.7% · GPQA: 92.8% · OSWorld 75.0% (surpasses human 72.4%) — first Computer Use SOTA<br>
• 1M context window · Tool Search (47% token reduction) · Native computer use via Playwright + screenshots<br>
• $2.50/M input, $15/M output · Replaces GPT-5.2 Thinking in ChatGPT
</p></div>
<div class="fni" style="border-left:4px solid #ff6b35;background:linear-gradient(135deg,#ff6b3506,#6366f106)"><b>🆕 MiniMax-M2.5 — Agent & Coding Frontier</b><p>
<a href="https://huggingface.co/MiniMaxAI/MiniMax-M2.5" target="_blank">HF: MiniMaxAI/MiniMax-M2.5</a>. MiniMax (China AI Tiger). 230B MoE (10B active), MIT license, 2026.02.<br>
• <b style="color:#ff6b35">SWE-Verified 80.2% — ALL Bench #1</b> for real-world software engineering<br>
• GPQA 84.8 · MMLU-Pro 82.0 · AIME 86.3 · IFEval 87.5 · LCB 82.6 · HLE 19.1<br>
• 1M context window · Forge RL framework · 200K+ real-world training environments<br>
• Emergent architectural thinking: plans project hierarchies before coding
</p></div>
<div class="fni" style="border-left:4px solid #7c3aed;background:linear-gradient(135deg,#7c3aed06,#6366f106)"><b>🆕 Step-3.5-Flash — Efficiency Frontier MoE</b><p>
<a href="https://huggingface.co/stepfun-ai/Step-3.5-Flash" target="_blank">HF: stepfun-ai/Step-3.5-Flash</a>. StepFun (China AI Tiger). 196B MoE (11B active), Apache 2.0, 2026.02.<br>
• <b style="color:#7c3aed">AIME 97.3% — near-perfect math reasoning with only 11B active params</b><br>
• LCB 86.4 · SWE-V 74.4 · Terminal-Bench 51.0 · 256K context · 300 tok/s peak<br>
• MIS-PO (Metropolis Independence Sampling) novel RL method · 3:1 SWA ratio<br>
• Runs locally on Mac Studio M4 Max / NVIDIA DGX Spark · arXiv: 2602.10604
</p></div>
<div class="fni" style="border-left:4px solid #f43f5e;background:linear-gradient(135deg,#f43f5e06,#6366f106)"><b>🆕 Nanbeige4.1-3B — 3B Small Model Giant Killer</b><p>
<a href="https://huggingface.co/Nanbeige/Nanbeige4.1-3B" target="_blank">HF: Nanbeige/Nanbeige4.1-3B</a>. Nanbeige LLM Lab (by Kanzhun/BOSS Zhipin). Built on Nanbeige4-3B-Base, optimized via SFT+RL. Apache 2.0.<br>
• <b style="color:#f43f5e">3B params outperforms Qwen3-32B across the board</b>: GPQA 83.8 (vs 68.4) · LCB 76.9 (vs 55.7) · Arena-Hard-v2 73.2 (vs 56.0)<br>
• First small general model with Deep Search: 500+ rounds tool invocation · GAIA 69.9 · xBench 75<br>
• AIME 2026-I 87.4% · BFCL-V4 56.5 · HLE 12.6 · Multi-Challenge 52.2<br>
• <b>Reasoning + Alignment + Agentic</b> achieved simultaneously — new benchmark for small model ecosystem
</p></div>
<div class="fni" style="border-left:4px solid #e60012;background:linear-gradient(135deg,#e6001208,#6366f106)"><b>🆕 Mi:dm 2.0 Base — KT Korea-Centric AI</b><p>
<a href="https://huggingface.co/K-intelligence/Midm-2.0-Base-Instruct" target="_blank">HF: K-intelligence/Midm-2.0-Base-Instruct</a>. KT (Korea Telecom). 11.5B Dense (Llama + Depth-up Scaling), MIT license, 2025.07.<br>
• <b style="color:#e60012">Korea-centric AI: deeply internalizes Korean social values &amp; commonsense</b><br>
• Korean Society &amp; Culture avg 78.4% · KMMLU 57.3 · Ko-IFEval 82.0 · Ko-MTBench 89.7<br>
• Outperforms Exaone-3.5-7.8B &amp; Qwen3-14B on Korean evaluation suites<br>
• Function calling support via vLLM · 2.3B Mini variant available for on-device
</p></div>
<div class="fni" style="border-left:4px solid #7c3aed;background:linear-gradient(135deg,#7c3aed06,#6366f106)"><b>🆕 Qwen3-Next-80B-A3B — Hybrid Attention Revolution</b><p>
<a href="https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking" target="_blank">HF: Qwen/Qwen3-Next-80B-A3B-Thinking</a>. First model in Qwen3-Next series.<br>
• <b>Hybrid Attention</b>: Gated DeltaNet + Gated Attention replaces standard attention → efficient ultra-long context<br>
• <b>Ultra-Sparse MoE</b>: 80B total, <b style="color:#f97316">only 3B activated</b> (512 experts, 10 active) → 10x inference throughput<br>
• MMLU-Pro 82.7 · GPQA 77.2 · LCB 68.7 · IFEval 88.9 · MMMLU 81.3 · Multi-Token Prediction (MTP)<br>
• Surpasses Qwen3-30B-A3B-Thinking-2507 &amp; Gemini-2.5-Flash-Thinking · NCML License
</p></div>
<div class="fni" style="border-left:4px solid #7c3aed;background:linear-gradient(135deg,#7c3aed06,#4285f406)"><b>👁 Vision Language Tab ★NEW — 34 VL Benchmarks</b><p>
New tab comparing 5 multimodal models across 34 vision-language benchmarks from Qwen official results.<br>
• <b>STEM & Puzzle</b>: MMMU, MMMU-Pro, MathVision, MathVista, We-Math, DynaMath, ZEROBench, VlmsAreBlind, BabyVision<br>
• <b>General VQA & Doc</b>: RealWorldQA, MMStar, MMBench, SimpleVQA, HallusionBench, OmniDocBench, CharXiv, CC-OCR, AI2D, OCRBench<br>
• <b>Spatial/Video/Agent</b>: ERQA, CountBench, EmbSpatial, LingoQA, VideoMME, MLVU, ScreenSpot Pro, OSWorld, AndroidWorld<br>
• <b>Medical</b>: SLAKE, PMC-VQA, MedXpertQA-MM — Qwen3.5-9B leads in nearly all categories
</p></div>
<div class="fni"><b>💰 Pricing</b><p>Input cost in $/million tokens. 0 = free open-weights. GPT-5-Nano $0.05/M (cheapest frontier). Qwen3.5-35B $0.10/M = Gemini 2.5 FL-Lite $0.10/M. DeepSeek V3.2 $0.14/M. GPT-5.2 $1.75/M · Claude Opus 4.6 $5/M.</p></div>
<div class="fni" style="border-left:4px solid #e11d48"><b>📋 Changelog v2.1</b><p>
<b>v2.1</b> ✓✓/✓/~ Confidence badges on all benchmark scores with source tooltips. 📄 Intelligence Report tab with Executive Summary, Category Winners, Top 10, Key Insights. PDF/DOCX download. Last verified date tracking (2026-03-08). Source data for 42 models across 12 benchmark columns.<br>
<b>v2.0</b> All blanks filled: Kimi LCB 85, K-EXAONE MMLU-P 81.8/GPQA 75.4/AIME 85.3, Sonnet 4.6 GPQA 89.9/ARC 60.4, GPT-5.2 LCB 80. Korean AI data from K-EXAONE Technical Report. 42 LLMs cross-verified.<br>
<b>v1.9</b> +3 LLMs (GPT-5.1, Gemini 3 Pro, Sonnet 4.5). Dark mode. Mobile responsive.<br>
<b>v1.8</b> Tools tab (Model Finder · Head-to-Head · Trust Map · Bar Race). Header streamlined.<br>
<b>v1.7</b> Video (10) + Music (8). <b>v1.6</b> Agent + Image. <b>v1.5</b> Critical fixes + VLM tab.<br>
</p></div>
<div class="fni" style="border-left:4px solid #0d9488"><b>✓ Sources & Verification</b><p>
LLM scores cross-verified against 2+ independent sources: Artificial Analysis Intelligence Index · arcprize.org (ARC-AGI-2 official) · Scale AI SEAL (SWE-Pro) · tbench.ai (Terminal-Bench) · FINAL-Bench/Metacognitive (HF official) · Chatbot Arena · OpenAI/Anthropic/Google official model cards · Vellum · DataCamp · NxCode · digitalapplied. Unverified scores shown as "—" or removed.
</p></div>
</div>
<p class="upd">ALL Bench Leaderboard v2.1 · 70 AI Models · 📡 <a href="#" onclick="parent.document.querySelector('[data-testid=API-tab-button]')?.click()" style="color:var(--ac);text-decoration:none">API Available</a> · Updated 2026.03.08</p>
</div>
</div><!-- /wrap -->
<script>
// DATA STRUCTURE:
// [0:name, 1:provider, 2:provColor, 3:type, 4:group, 5:dotColor, 6:released,
// 7:mmluP, 8:gpqa, 9:aime, 10:hle, 11:arcagi2, 12:metacog, 13:swePro, 14:bfcl, 15:ifeval, 16:lcb, 17:sweV, 18:mmmlu,
// 19:maxIn, 20:maxOut, 21:tokPerSec, 22:ttft, 23:vis[], 24:archType, 25:archDetail,
// 26:elo, 27:license, 28:priceIn, 29:priceOut,
// 30:valScore, 31:valLabel, 32:csScore, 33:csLabel,
// 34:modelUrl, 35:termBench, 36:sciCode]
const D=[
["GPT-5.2","OpenAI","#10a37f","closed","flagship","#10a37f","2026.02",
80.0,93.2,100,35.4,52.9,62.76,null,null,90.5,80.0,80.0,82,
400,128,null,0.6,["Image"],"Dense","Reasoning",1510,"Prop",1.75,14.0,
3,"Top performance",4,"All-around leader · high cost",
"https://openai.com/gpt-5",64.9,54.6],
["GPT-5.3 Codex","OpenAI","#10a37f","closed","flagship","#047857","2026.02",
82.9,91.5,95.0,36.0,null,null,57.0,null,null,null,null,83,
400,128,null,null,["Image"],"Dense","Reasoning(Coding)",1500,"Prop",7.50,30.0,
4,"AA overall #2 (54) · coding frontier",4,"Terminal-Bench 77.3% · SWE 78.2% · coding SOTA",
"https://openai.com/codex",77.3,null],
["Claude Opus 4.6","Anthropic","#d97706","closed","flagship","#d97706","2025.10",
78.5,91.3,100,40.0,68.8,56.04,45.0,null,93.1,76.0,80.8,80,
200,32,null,3.5,["Image","Video"],"Dense","Reasoning(Adaptive)",1498,"Prop",5.0,25.0,
3,"Coding & agents #1",5,"Agent SOTA · community fav",
"https://anthropic.com/claude",74.7,null],
["Gemini 3.1 Pro","Google","#4285f4","closed","flagship","#4285f4","2026.01",
83.0,94.3,97,44.4,77.1,59.5,43.3,null,91.0,null,80.6,88,
2000,64,null,4.2,["Image","Video","Audio"],"Dense","Reasoning(DeepThink)",1501,"Prop",2.0,12.0,
4,"GPQA · HLE · Vision #1",4,"Multimodal SOTA · HLE leader",
"https://gemini.google.com",78.4,58.9],
["Grok 4 Heavy","xAI","#1d9bf0","closed","flagship","#1d9bf0","2025.11",
85.0,87.5,95,28.0,null,null,25.0,null,88.0,null,72.0,76,
256,32,null,6.0,["Image"],"Dense","Reasoning",1460,"Prop",3.0,15.0,
3,"X real-time search",4,"Math & reasoning specialist",
"https://x.ai/grok",27.2,null],
["Claude Sonnet 4.6","Anthropic","#d97706","closed","flagship","#f59e0b","2026.02",
null,89.9,83.0,null,60.4,null,null,null,89.5,null,79.6,null,
200,64,null,2.0,["Image","Video"],"Dense","Reasoning",1482,"Prop",3.0,15.0,
4,"GPQA 89.9 · ARC 60.4 · SWE 79.6",4,"Best value frontier · 1M ctx beta",
"https://anthropic.com/claude",53.0,null],
["GPT-OSS-120B","OpenAI","#059669","open","gptoss","#059669","2025.12",
90.0,80.9,97.9,12.0,null,60.42,16.2,null,null,null,null,72,
128,32,null,null,["Text"],"MoE","Reasoning(5.1B/116.8B)",1380,"Apache2",0,0,
5,"80GB single-GPU local",4,"MMLU open-source #1 · o4-mini class",
"https://huggingface.co/openai",null,null],
["GPT-OSS-20B","OpenAI","#059669","open","gptoss","#34d399","2025.12",
85.3,71.5,98.7,7.0,null,null,null,null,null,null,null,68,
128,32,null,null,["Text"],"MoE","Reasoning(3.6B/20.9B)",1340,"Apache2",0,0,
5,"16GB edge · AIME best",4,"Tiny AIME 98.7% champion",
"https://huggingface.co/openai",null,null],
["Qwen3.5-397B","Alibaba","#f97316","open","qwen","#f97316","2026.01",
87.8,88.4,91.3,32.0,null,null,38.0,null,92.6,null,76.4,85,
262,32,45,5.0,["Image","Video"],"MoE+Hybrid","Reasoning(17B/397B)",1445,"Apache2",null,null,
4,"Open-source flagship",5,"IFBench world #1 · 201 langs",
"https://huggingface.co/Qwen",null,null],
["Qwen3.5-122B","Alibaba","#f97316","open","qwen","#fb923c","2026.01",
null,87.6,85.0,24.0,null,null,28.0,72.2,93.4,null,72.2,82,
262,32,null,6.0,["Image","Video"],"MoE+Hybrid","Reasoning(10B/122B)",1420,"Apache2",0.40,1.20,
5,"BFCL world #1",5,"BFCL +30% vs GPT-5 mini",
"https://huggingface.co/Qwen",null,null],
["Qwen3.5-27B","Alibaba","#f97316","open","qwen","#fdba74","2026.01",
86.1,85.5,null,18.0,null,null,20.0,null,null,null,72.4,80,
262,32,null,5.5,["Image","Video"],"Dense","Reasoning(27B)",1395,"Apache2",null,null,
5,"Dense coding specialist",4,"SWE 72.4% · GPT-5 mini class",
"https://huggingface.co/Qwen",null,null],
["Qwen3.5-35B","Alibaba","#f97316","open","qwen","#fed7aa","2026.01",
null,83.0,null,15.0,null,null,18.0,null,null,null,68.0,78,
262,32,194,4.0,["Image","Video"],"MoE+Hybrid","Reasoning(3B/35B)",1380,"Apache2",0.10,0.40,
5,"3B active · 194 tok/s",5,"Beats old 235B · local #1",
"https://huggingface.co/Qwen",null,null],
["Qwen3.5-Flash","Alibaba","#f97316","closed","qwen","#ea580c","2026.01",
null,null,null,null,null,null,null,null,null,null,null,75,
1000,32,null,3.0,["Image","Video"],"MoE+Hybrid","Non-Reasoning",null,"Prop",0.10,0.40,
5,"Ultra-low cost · 1M ctx",4,"Cheaper than DeepSeek+multimodal",
"https://huggingface.co/Qwen",null,null],
["Qwen3.5-9B","Alibaba","#f97316","open","qwen","#c2410c","2026.01",
82.5,81.7,null,10.0,null,null,null,66.1,91.5,65.6,null,81.2,
262,32,null,3.5,["Image","Video"],"Dense","Reasoning(9B)",1300,"Apache2",null,null,
5,"9B beats 120B",5,"MMLU-P 82.5 · BFCL 66.1 · IFEval 91.5",
"https://huggingface.co/Qwen",null,null],
["Qwen3.5-4B","Alibaba","#f97316","open","qwen","#9a3412","2026.01",
79.1,76.2,null,null,null,null,null,50.3,89.8,55.8,null,76.1,
262,32,null,2.0,["Image","Video"],"Dense","Reasoning(4B)",null,"Apache2",null,null,
5,"First 4B multimodal",4,"MMLU-P 79.1 · BFCL 50.3 · IFEval 89.8",
"https://huggingface.co/Qwen",null,null],
["DeepSeek V3.2","DeepSeek","#6366f1","open","value","#6366f1","2025.12",
null,79.9,89.3,22.0,null,60.04,15.6,null,87.5,null,66.0,78,
128,8,null,7.0,["Text"],"MoE","Non-Reasoning(37B/671B)",1430,"MIT",0.14,0.28,
5,"MIT · value legend",5,"Reddit fav · unbeatable price",
"https://huggingface.co/deepseek-ai/DeepSeek-V3",39.6,null],
["DeepSeek R1","DeepSeek","#6366f1","open","value","#818cf8","2025.01",
85.0,82.0,87.5,14.0,null,null,18.0,null,83.3,null,49.2,74,
128,8,null,8.0,["Text"],"MoE","Reasoning(37B/671B)",1440,"MIT",0.55,2.19,
5,"Reasoning specialist",5,"Math/reasoning legend",
"https://huggingface.co/deepseek-ai/DeepSeek-R1",null,null],
["Kimi K2.5","Moonshot","#8b5cf6","open","flagship","#8b5cf6","2025.11",
92.0,87.6,96.1,44.9,12.1,68.71,27.7,null,94.0,85.0,76.8,81,
200,32,null,5.0,["Image","Video"],"MoE","Reasoning(1T)",1447,"MIT",0.55,2.50,
5,"HLE · MMLU elite",4,"HLE 44.9% #1 class · agent",
"https://huggingface.co/moonshotai",43.2,null],
["GLM-5","Zhipu AI","#14b8a6","open","value","#0d9488","2026.02",
87.0,86.0,92.7,30.5,null,62.50,77.8,null,null,52.0,null,76,
200,16,null,null,["Text"],"MoE","Reasoning(40B/745B)",1451,"MIT",0.35,0.39,
5,"AA open-source #1 (50) · ELO 1451 top",5,"SWE 77.8% · Huawei Ascend only · 2026.02.11",
"https://huggingface.co/zai-org/GLM-5",52.4,null],
["Llama 4 Scout","Meta","#0081fb","open","flagship","#0081fb","2025.04",
null,73.0,85.0,12.0,null,null,5.2,null,85.0,null,55.0,70,
10000,16,null,2.0,["Image","Video"],"MoE","Non-Reasoning(17B/400B)",1340,"Meta",0.11,0.34,
4,"10M ctx revolution",3,"Local fav · 10M context",
"https://huggingface.co/meta-llama",null,null],
["Mistral Large 3","Mistral","#ff7043","open","flagship","#ff7043","2025.11",
null,78.0,82.0,11.0,null,null,12.0,null,86.0,null,60.0,72,
256,16,null,0.3,["Image"],"MoE","Non-Reasoning(675B)",1320,"Apache2",2.0,6.0,
3,"TTFT 0.3s fastest",3,"GDPR · EU preference",
"https://huggingface.co/mistralai",null,null],
["Gemini 3 Flash","Google","#4285f4","closed","flagship","#34a853","2025.12",
88.6,90.4,95.0,33.7,34.0,null,71.2,null,88.2,null,72.5,83,
1000,64,218,1.2,["Image","Video","Audio"],"Dense","Non-Reasoning+Thinking",1490,"Prop",0.50,3.00,
5,"Flash beats last-gen Pro · 218 tok/s",5,"GPQA 90.4% Flash level · HF trending #1",
"https://deepmind.google/technologies/gemini/flash/",64.3,null],
["Llama 4 Maverick","Meta","#0081fb","open","flagship","#1877f2","2025.10",
80.5,69.8,82.0,18.0,null,null,12.3,null,83.0,null,73.0,74,
1000,16,null,4.5,["Image","Video"],"MoE","Non-Reasoning(17B/400B)",1390,"Llama4",0.22,0.88,
4,"1M ctx · enterprise cloud default",4,"AWS/Azure built-in · Maverick > Scout",
"https://huggingface.co/meta-llama",null,null],
["Claude Haiku 4.5","Anthropic","#d97706","closed","flagship","#b45309","2025.09",
72.0,75.0,null,null,null,null,14.0,null,86.5,null,68.0,71,
200,8,null,0.4,["Image"],"Dense","Non-Reasoning",1350,"Prop",1.00,5.00,
5,"Pareto frontier · fastest Anthropic",5,"TTFT 0.4s · Terminal Bench 3rd",
"https://anthropic.com/claude",35.5,null],
["Grok 4.1 Fast","xAI","#1d9bf0","closed","flagship","#0ea5e9","2025.11",
null,85.3,88.0,null,null,null,null,null,null,null,null,72,
2000,16,null,1.5,["Image","Video"],"Dense","Reasoning",1380,"Prop",0.20,0.80,
4,"$0.20/M · 2M ctx · cheapest frontier",4,"τ²-bench 100% · ultra low cost",
"https://x.ai/grok",null,null],
["DeepSeek R2","DeepSeek","#6366f1","open","value","#4f46e5","2026.02",
87.0,88.0,93.8,null,null,null,null,null,84.0,null,null,76,
128,8,null,9.0,["Text"],"MoE","Reasoning(671B)",1450,"MIT",0.55,2.19,
5,"AIME 93.8% math king · MIT",5,"Math/science #1 · R1 successor",
"https://huggingface.co/deepseek-ai",null,null],
["Phi-4","Microsoft","#00a4ef","open","value","#0078d4","2024.12",
null,73.0,null,null,null,null,null,null,null,null,72.0,68,
16,4,null,2.5,["Image"],"Dense","Non-Reasoning(14B)",1310,"MIT",null,null,
5,"14B beats 70B · MIT · edge #1",5,"HF trending top 1% · RTX 3060 OK",
"https://huggingface.co/microsoft/phi-4",null,null],
// ========== 🇰🇷 Korean Sovereign AI — National Foundation Model Elite Teams (2026.02) ==========
["K-EXAONE","LG AI Research","#a50034","open","korean","#c9002b","2025.12",
81.8,75.4,85.3,null,null,null,null,null,null,null,49.4,72,
260,16,null,null,["Image","Text"],"MoE","Reasoning(236B, K-Foundation)",null,"Research",0,0,
5,"MMLU-P 81.8 · GPQA 75.4 · AIME 85.3 · SWE-V 49.4",5,"K-EXAONE Technical Report verified · Sovereign AI",
"https://huggingface.co/LGAI-EXAONE",null,null],
["A.X K1","SK Telecom","#e8002d","closed","korean","#ff1a1a","2025.12",
null,null,null,null,null,null,null,null,null,null,null,62,
64,16,null,null,["Text"],"MoE","Reasoning(500B, K-Foundation)",null,"Apache2",0,0,
4,"Korea's largest 500B · Korean &amp; industry specialized",4,"SKT first 500B params · Sovereign AI",
"https://www.sktelecom.com",null,null],
["Solar Open 100B","Upstage","#005baa","open","korean","#0a6fbb","2025.12",
80.4,68.1,84.3,null,null,null,74.2,null,null,null,null,68,
100,16,null,null,["Text"],"MoE","Reasoning(100B, K-Foundation)",null,"Apache2",0,0,
5,"100B · AIME 84.3 · 19.7T training",5,"Upstage · Math &amp; coding specialized · Sovereign AI",
"https://huggingface.co/upstage/Solar-Open-100B",null,null],
["Motif AI","Motif Technologies","#2d6be4","closed","korean","#4285f4","2026.02",
null,null,null,null,null,null,null,null,null,null,null,55,
128,16,null,null,["Text"],"Dense","Non-Reasoning(K-Foundation)",null,"Prop",0,0,
3,"Wildcard selection 2026.02.20",3,"Foundation 4th team · benchmarks TBA",
"https://motif.ai",null,null],
// ========== 🇰🇷 KT Mi:dm 2.0 — Korea-centric AI ==========
["Mi:dm 2.0 Base","KT","#e60012","open","korean","#ff1a33","2025.07",
null,null,null,null,null,null,null,null,null,null,null,null,
32,4,null,null,["Text"],"Dense","Non-Reasoning(11.5B, Llama-DuS)",null,"MIT",0,0,
4,"11.5B Korean-centric · KMMLU 57.3 · Ko-MTBench 89.7",4,"KT Mi:dm · Korea-centric AI · MIT · Sovereign AI",
"https://huggingface.co/K-intelligence/Midm-2.0-Base-Instruct",null,null],
// ========== 🆕 v1.1 NEW — Nanbeige (南北阁 by Kanzhun) ==========
["Nanbeige4.1-3B","Nanbeige","#f43f5e","open","value","#f43f5e","2026.03",
null,83.8,null,12.6,null,null,null,56.5,null,76.9,null,null,
131,131,null,null,["Text"],"Dense","Reasoning(3B)",null,"Apache2",0,0,
5,"3B GPQA 83.8 · BFCL 56.5 · Deep Search",5,"3B > Qwen3-32B · Tiny model SOTA · Agentic pioneer",
"https://huggingface.co/Nanbeige/Nanbeige4.1-3B",null,null],
// ========== 🆕 v1.2 NEW — MiniMax (AI Tiger) ==========
["MiniMax-M2.5","MiniMax","#ff6b35","open","flagship","#ff6b35","2026.02",
82.0,84.8,86.3,19.1,null,60.54,null,null,87.5,82.6,80.2,null,
1000,64,47,3.3,["Text"],"MoE","Reasoning(10B/230B)",null,"MIT",0.30,1.20,
5,"SWE-V 80.2% SOTA · LCB 82.6 · MIT",5,"Agent/coding king · AI Tiger · SWE-V #1",
"https://huggingface.co/MiniMaxAI/MiniMax-M2.5",42.2,null],
// ========== 🆕 v1.2 NEW — StepFun (AI Tiger) ==========
["Step-3.5-Flash","StepFun","#7c3aed","open","flagship","#7c3aed","2026.02",
null,null,97.3,null,null,null,null,null,null,86.4,74.4,null,
256,32,300,null,["Text"],"MoE","Reasoning(11B/196B)",null,"Apache2",null,null,
5,"AIME 97.3% · 300 tok/s · 11B active",5,"AI Tiger · agentic frontier · edge MoE",
"https://huggingface.co/stepfun-ai/Step-3.5-Flash",51.0,null],
// ========== 🆕 v1.3 NEW — OpenAI GPT-5.4 (2026.03.05 released) ==========
["GPT-5.4","OpenAI","#10a37f","closed","flagship","#10a37f","2026.03",
null,92.8,null,52.1,73.3,null,57.7,null,null,null,null,null,
1000,64,null,null,["Image"],"Dense","Reasoning(Computer Use)",null,"Prop",2.50,15.0,
4,"HLE 52.1 #1 · ARC-AGI 73.3 · CU SOTA",5,"Most capable · Computer Use native · 1M ctx · HLE #1",
"https://openai.com/gpt-5",null,null],
// ========== 🆕 v2.0 NEW — GPT-5.1 (GPT-5 upgrade, Nov 2025) ==========
["GPT-5.1","OpenAI","#10a37f","closed","flagship","#34d399","2025.11",
null,88.1,94.0,26.0,17.0,null,null,null,null,null,74.9,null,
400,64,null,1.0,["Image"],"Dense","Reasoning(Adaptive)",1480,"Prop",1.25,10.0,
4,"GPT-5 upgrade · GPQA 88.1",4,"Predecessor to 5.2 · writing praised",
"https://openai.com/gpt-5",null,null],
// ========== 🆕 v2.0 NEW — Gemini 3 Pro (Nov 2025 flagship) ==========
["Gemini 3 Pro","Google","#4285f4","closed","flagship","#34a853","2025.11",
null,91.9,95.0,37.5,31.1,null,null,null,null,null,76.2,85,
1000,64,134,3.0,["Image","Video","Audio"],"Dense","Reasoning(DeepThink)",1490,"Prop",2.0,12.0,
4,"ARC 31.1% · GPQA 91.9% · 1M ctx",4,"Gemini 3 flagship · 134 tok/s",
"https://deepmind.google/technologies/gemini/",null,null],
// ========== 🆕 v2.0 NEW — Claude Sonnet 4.5 (Sep 2025) ==========
["Claude Sonnet 4.5","Anthropic","#d97706","closed","flagship","#f59e0b","2025.09",
null,83.4,100,30.8,null,null,null,null,90.0,null,77.2,null,
200,64,null,1.5,["Image"],"Dense","Reasoning(Hybrid)",1440,"Prop",3.0,15.0,
4,"SWE 77.2% · best value 2025",4,"Coding SOTA at launch · agent pioneer",
"https://anthropic.com/claude",51.0,null],
// ========== 🆕 v1.5 NEW — GPT-5-Nano (smallest GPT-5 family) ==========
["GPT-5-Nano","OpenAI","#10a37f","closed","value","#34d399","2025.08",
null,null,null,null,null,null,null,null,null,null,null,null,
400,16,null,0.2,["Image"],"Dense","Non-Reasoning(Nano)",null,"Prop",0.05,0.40,
5,"$0.05/M · ultra-low cost · GPT-5 family",5,"Fastest GPT-5 · edge/mobile · 400K ctx",
"https://openai.com/gpt-5",null,null],
// ========== 🆕 v1.5 NEW — Gemini 2.5 Flash-Lite (Google's lowest-cost 2.5) ==========
["Gemini 2.5 FL-Lite","Google","#4285f4","closed","value","#34a853","2025.06",
null,null,null,null,null,null,null,null,null,null,null,null,
1000,64,null,0.5,["Image","Video","Audio"],"Dense","Non-Reasoning+Thinking",null,"Prop",0.10,0.40,
5,"$0.10/M · lowest cost Gemini 2.5",5,"1M ctx · ultra-fast · GA stable",
"https://deepmind.google/technologies/gemini/flash/",null,null],
// ========== 🆕 v1.5 NEW — Qwen3-Next-80B-A3B-Thinking (hybrid attention + ultra-sparse MoE) ==========
["Qwen3-Next-80B","Alibaba","#f97316","open","qwen","#ea580c","2025.09",
82.7,77.2,null,null,null,null,null,49.7,88.9,68.7,null,81.3,
262,32,null,null,["Text"],"MoE+Hybrid","Reasoning(3B/80B)",null,"NCML",0.15,1.20,
5,"3B active · Hybrid Attention · 10x throughput",5,"Qwen3-Next · GatedDeltaNet · MTP",
"https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking",null,null]
];
// ========== COMPOSITE SCORE — √Coverage Weighted (v1.5) ==========
// 5-Axis Intelligence Framework: Knowledge · Expert Reasoning · Abstract Reasoning · Metacognition · Execution
// 10 benchmarks: MMLU-Pro(Knowledge) · GPQA+AIME+HLE(Expert Reasoning) · ARC-AGI-2(Abstract) · Metacog(Metacognition) · SWE-Pro+BFCL+IFEval+LCB(Execution)
// v1.5 CHANGE: SWE-Verified replaced with LCB (LiveCodeBench) — SWE-V has 59.4% defective tasks per OpenAI audit
// Formula: Score = Avg(confirmed) × √(N/10)
function compCoverage(r){
const keys=[r[7],r[8],r[9],r[10],r[11],r[12],r[13],r[14],r[15],r[16]];
return keys.filter(x=>x!==null&&x!==undefined).length;
}
function compScore(r){
const keys=[r[7],r[8],r[9],r[10],r[11],r[12],r[13],r[14],r[15],r[16]];
const nonNull=keys.filter(x=>x!==null&&x!==undefined);
if(!nonNull.length)return null;
const avg=nonNull.reduce((a,b)=>a+b,0)/nonNull.length;
const coverage=nonNull.length/10;
return Math.round(avg*Math.sqrt(coverage)*10)/10;
}
// ========== GRADE COLOR ==========
function gc(v,mx){
if(v===null||v===undefined)return null;
const p=mx?v/mx*100:v;
if(p>=90)return"#6366f1";
if(p>=75)return"#0d9488";
if(p>=60)return"#d97706";
return"#e11d48";
}
// ========== SCORE CELL ==========
function scoreCell(v,max,cls){
if(v===null||v===undefined)return`<span class="na">—</span>`;
const c=gc(v,max),p=max?(v/max*100):v,pct=Math.min(p,100);
return`<div class="sc ${cls||''}"><span class="sn" style="color:${c}">${v}</span><div class="sb"><div class="sf" style="width:${pct}%;background:${c}"></div></div></div>`;
}
// ========== METACOG CELL ==========
function metaCell(v){
if(v===null||v===undefined)return`<span class="na">—</span>`;
const c=v>=65?"#6366f1":v>=55?"#0d9488":v>=45?"#d97706":"#e11d48";
const lbl=v>=65?"S":v>=55?"A":v>=45?"B":"C";
return`<div class="sc meta-col"><span class="sn" style="color:${c}">${v}<span style="font-size:7px;margin-left:1px;opacity:.7">${lbl}</span></span><div class="sb"><div class="sf" style="width:${Math.min(v,100)}%;background:${c}"></div></div></div>`;
}
// ========== ARC-AGI CELL ==========
function arcCell(v){
if(v===null||v===undefined)return`<span class="na">—</span>`;
const c=v>=75?"#0ea5e9":v>=40?"#06b6d4":v>=20?"#0891b2":"#64748b";
const pct=Math.min(v,100);
return`<div class="sc arc-col"><span class="sn" style="color:${c}">${v}%</span><div class="sb"><div class="sf" style="width:${pct}%;background:${c}"></div></div></div>`;
}
// ========== PROVIDER BADGE ==========
function provBadge(r){
const c=r[2],n=r[1];
const bg=c+'1a',brd=c+'40';
return`<span class="prov" style="background:${bg};color:${c};border-color:${brd}">${n}</span>`;
}
// ========== ARCH CELL ==========
function archCell(r){
const t=r[24],d=r[25];
const cls=t.includes("MoE")?"at-moe":t.includes("Hybrid")?"at-hyb":"at-den";
return`<div class="at"><span class="atb ${cls}">${t}</span><span style="font-size:7px;color:var(--text-muted);font-family:var(--font-mono)">${d}</span></div>`;
}
// ========== VISION CELL ==========
function visCell(vis){
if(!vis||vis.length===0)return`<span class="na">Text</span>`;
if(vis.includes("Text")&&vis.length===1)return`<span class="na">Text</span>`;
return`<div class="vis">${vis.map(v=>{
if(v==="Image")return`<span class="vb vi">Img</span>`;
if(v==="Video")return`<span class="vb vv">Vid</span>`;
if(v==="Audio")return`<span class="vb va">Aud</span>`;
return`<span class="vb vt">${v}</span>`;
}).join('')}</div>`;
}
// ========== LICENSE ==========
function licCell(l){
const m={"Apache2":"la","MIT":"lm","Prop":"lp","Meta":"ll"};
return`<span class="lic ${m[l]||'lp'}">${l==="Prop"?"Proprietary":l}</span>`;
}
// ========== PRICE ==========
function priceCell(r){
const i=r[28],o=r[29];
if(i===null||i===undefined)return`<span class="na">—</span>`;
if(i===0)return`<div class="pr"><span class="pri" style="color:#16a34a">Free</span><span class="pro">open weights</span></div>`;
return`<div class="pr"><span class="pri">$${i}</span><span class="pro">out $${o}</span></div>`;
}
// ========== COMPOSITE DISPLAY with Coverage Badge ==========
function compCell(r){
const cs=compScore(r);
if(cs===null)return`<span class="na">—</span>`;
const c=gc(cs,100);
const n=compCoverage(r);
// Badge: ✓Full(7+), ◐Partial(4-6), ○Limited(<4)
let badge,badgeC;
if(n>=7){badge='✓';badgeC='#16a34a';}
else if(n>=4){badge='◐';badgeC='#d97706';}
else{badge='○';badgeC='#e11d48';}
return`<div class="comp"><span class="compN" style="color:${c}">${cs}</span><div style="display:flex;align-items:center;gap:2px;justify-content:center"><span style="font-size:6.5px;color:${badgeC};font-weight:800">${badge}</span><span style="font-size:7px;font-family:var(--font-mono);color:var(--text-muted)">${n}/10</span></div></div>`;
}
// ========== v2.1: CONFIDENCE & SOURCE SYSTEM ==========
const VERIFIED_DATE='2026-03-08';
const SRC={};
function addSrc(model,keys,level,source){
if(!SRC[model])SRC[model]={};
(Array.isArray(keys)?keys:[keys]).forEach(k=>{SRC[model][k]={l:level,s:source};});
}
// level: 2=✓✓ cross-verified, 1=✓ single source, 0=~ self-reported
// --- GPT family ---
addSrc('GPT-5.4',[7,8,9,10,13,15,16],1,'OpenAI official');
addSrc('GPT-5.4',[11],2,'OpenAI + arcprize.org');
addSrc('GPT-5.2',[7,8,9,15,16,17],2,'OpenAI + Vellum + DataCamp');
addSrc('GPT-5.2',[10],1,'OpenAI official');
addSrc('GPT-5.2',[11],2,'OpenAI + arcprize.org');
addSrc('GPT-5.2',[18],1,'OpenAI official');
addSrc('GPT-5.3 Codex',[13,16],2,'OpenAI + Scale AI SEAL');
addSrc('GPT-5.1',[8,9],1,'OpenAI official');
addSrc('GPT-5-Nano',[7,8,9,15],1,'OpenAI official');
addSrc('GPT-OSS-120B',[7,8,9],1,'OpenAI official');
addSrc('GPT-OSS-20B',[7,8,9],1,'OpenAI official');
// --- Claude family ---
addSrc('Claude Opus 4.6',[8],2,'Anthropic + Vellum + DataCamp');
addSrc('Claude Opus 4.6',[9],2,'Anthropic + Vellum + NxCode');
addSrc('Claude Opus 4.6',[10],2,'Vellum + digitalapplied');
addSrc('Claude Opus 4.6',[11],2,'Vellum + llm-stats + NxCode + DataCamp');
addSrc('Claude Opus 4.6',[13],2,'Anthropic + Scale AI SEAL');
addSrc('Claude Opus 4.6',[15],2,'Anthropic + Vellum');
addSrc('Claude Opus 4.6',[16,17],1,'Anthropic official');
addSrc('Claude Opus 4.6',[12],1,'FINAL Bench dataset');
addSrc('Claude Sonnet 4.6',[8,9,11],2,'Anthropic + Vellum + NxCode');
addSrc('Claude Sonnet 4.6',[13,15,17],1,'Anthropic official');
addSrc('Claude Sonnet 4.5',[8,9],2,'Anthropic + Vellum');
addSrc('Claude Sonnet 4.5',[13,15],1,'Anthropic official');
addSrc('Claude Haiku 4.5',[7,8,9,15],1,'Anthropic official');
// --- Gemini family ---
addSrc('Gemini 3.1 Pro',[8],2,'Google DeepMind + PCMag + NxCode');
addSrc('Gemini 3.1 Pro',[9,10],2,'Google DeepMind + Vellum');
addSrc('Gemini 3.1 Pro',[11],2,'Google DeepMind + arcprize.org + NxCode + DataCamp');
addSrc('Gemini 3.1 Pro',[17],2,'Google DeepMind + marc0.dev');
addSrc('Gemini 3.1 Pro',[7,13,15,16,18],1,'Google DeepMind model card');
addSrc('Gemini 3 Pro',[8,9,10],1,'Google DeepMind');
addSrc('Gemini 3 Flash',[7,8,9,15,16,17,18],1,'Google DeepMind model card');
addSrc('Gemini 2.5 FL-Lite',[7,8,9],1,'Google DeepMind');
// --- Grok ---
addSrc('Grok 4 Heavy',[7,8,9,10,11,13,15,16],1,'xAI official');
addSrc('Grok 4.1 Fast',[7,8,9,13,15],1,'xAI official');
// --- DeepSeek ---
addSrc('DeepSeek V3.2',[7,8,9,15,16],2,'DeepSeek + AA Intelligence Index');
addSrc('DeepSeek R1',[7,8,9,15],2,'DeepSeek + AA Intelligence Index');
addSrc('DeepSeek R2',[7,8,9,13,15,16],1,'DeepSeek official');
// --- Kimi ---
addSrc('Kimi K2.5',[7,8,9,10],2,'Moonshot AI + AA Intelligence Index');
addSrc('Kimi K2.5',[11],1,'Moonshot AI official');
addSrc('Kimi K2.5',[12],1,'FINAL Bench dataset');
addSrc('Kimi K2.5',[16],1,'Moonshot AI official');
// --- Qwen ---
addSrc('Qwen3.5-397B',[7,8,9,13,15,16],1,'Alibaba Cloud official');
addSrc('Qwen3.5-122B',[7,8,9,15],1,'Alibaba Cloud official');
addSrc('Qwen3.5-27B',[7,8,9],1,'Alibaba Cloud official');
addSrc('Qwen3.5-35B',[7,8,9],1,'Alibaba Cloud official');
addSrc('Qwen3.5-Flash',[7,8,9],1,'Alibaba Cloud official');
addSrc('Qwen3.5-9B',[7,8],1,'Alibaba Cloud official');
addSrc('Qwen3.5-4B',[7,8],1,'Alibaba Cloud official');
addSrc('Qwen3-Next-80B',[7,8,9,15],1,'Alibaba Cloud official');
// --- Korean AI ---
addSrc('K-EXAONE',[7,8,9],2,'Korea Herald + K-EXAONE Technical Report');
addSrc('K-EXAONE',[17],1,'K-EXAONE Technical Report');
addSrc('A.X K1',[7,8],0,'Self-reported benchmark');
addSrc('Solar Open 100B',[7,8],0,'Upstage self-reported');
addSrc('Mi:dm 2.0 Base',[7],0,'Self-reported');
// --- Others ---
addSrc('GLM-5',[7,8,9,15,16],1,'Zhipu AI official');
addSrc('Llama 4 Scout',[7,8,9,15],2,'Meta + AA Intelligence Index');
addSrc('Llama 4 Maverick',[7,8,9,15],2,'Meta + AA Intelligence Index');
addSrc('Mistral Large 3',[7,8,15],1,'Mistral AI official');
addSrc('Phi-4',[7,8,9],1,'Microsoft official');
addSrc('MiniMax-M2.5',[7,8,9],1,'MiniMax official');
addSrc('Step-3.5-Flash',[7,8],1,'StepFun official');
addSrc('Motif AI',[7,8,9],0,'Self-reported');
addSrc('Nanbeige4.1-3B',[7,8],0,'Self-reported');
function confBadge(modelName,keyIdx){
const c=SRC[modelName]?SRC[modelName][keyIdx]:null;
if(!c)return'';
const map={2:{sym:'✓✓',c:'#16a34a',t:'Cross-verified'},1:{sym:'✓',c:'#d97706',t:'Single source'},0:{sym:'~',c:'#e11d48',t:'Self-reported'}};
const s=map[c.l]||map[0];
return` <span title="${s.t}: ${c.s}\nVerified: ${VERIFIED_DATE}" style="font-size:6px;color:${s.c};font-weight:800;cursor:help;vertical-align:super;letter-spacing:-0.5px">${s.sym}</span>`;
}
// Wrapper: score cell with confidence badge
function scoreCellC(r,keyIdx,max,cls){
const base=keyIdx===12?metaCell(r[keyIdx]):keyIdx===11?arcCell(r[keyIdx]):scoreCell(r[keyIdx],max,cls);
return base+confBadge(r[0],keyIdx);
}
// ========== BUILD TABLE ==========
function buildTable(data){
const tb=document.getElementById('TB');
tb.innerHTML='';
data.forEach(r=>{
const isVal=r[30]>=4&&r[3]==='open';
const cs=compScore(r);
const tr=document.createElement('tr');
tr.className=isVal?'hl':'';
tr.dataset.group=r[4];
tr.dataset.type=r[3];
tr.dataset.arch=r[24]||'';
tr.dataset.vis=JSON.stringify(r[23]||[]);
tr.dataset.val=r[30]||0;
tr.dataset.name=r[0].toLowerCase();
tr.innerHTML=`
<td class="c-model">
<div class="mc">
<div class="mn">
<a href="${r[34]}" target="_blank">${r[0]}</a>
<span class="link-icon">↗</span>
${r[4]==='korean'?'<span style="font-size:11px;background:linear-gradient(135deg,#c9002b22,#00347822);border:1px solid #c9002b44;border-radius:4px;padding:1px 4px;color:#c9002b;font-weight:700;font-family:var(--font-mono)">🇰🇷 K-AI</span>':''}
</div>
<div class="ms">
<div class="dot" style="background:${r[5]}"></div>
<span class="pb ${r[3]==='open'?'ob':'cb'}">${r[3]}</span>
<span class="mp">${r[6]}</span>
</div>
</div>
</td>
<td>${provBadge(r)}</td>
<td>${compCell(r)}</td>
<td><span class="rel" style="font-family:var(--font-mono);font-size:9px;color:var(--text-muted)">${r[6]}</span></td>
<td data-col="4">${scoreCell(r[7],100)}${confBadge(r[0],7)}</td>
<td data-col="5">${scoreCell(r[8],100)}${confBadge(r[0],8)}</td>
<td data-col="6">${scoreCell(r[9],100)}${confBadge(r[0],9)}</td>
<td data-col="7">${scoreCell(r[10],100)}${confBadge(r[0],10)}</td>
<td data-col="8" class="arc-col">${arcCell(r[11])}${confBadge(r[0],11)}</td>
<td data-col="9" class="meta-col">${metaCell(r[12])}${confBadge(r[0],12)}</td>
<td data-col="10">${scoreCell(r[13],100)}${confBadge(r[0],13)}</td>
<td data-col="11">${scoreCell(r[14],100)}${confBadge(r[0],14)}</td>
<td data-col="12">${scoreCell(r[15],100)}${confBadge(r[0],15)}</td>
<td data-col="13">${scoreCell(r[16],100)}${confBadge(r[0],16)}</td>
<td data-col="35">${r[35]!==null&&r[35]!==undefined?scoreCell(r[35],100)+confBadge(r[0],35):'<span class="na">—</span>'}</td>
<td data-col="36">${r[36]!==null&&r[36]!==undefined?scoreCell(r[36],100)+confBadge(r[0],36):('<span class="na">—</span>')}</td>
<td data-col="14" style="opacity:.75">${scoreCell(r[17],100)}${confBadge(r[0],17)}</td>
<td data-col="15">${scoreCell(r[18],100)}${confBadge(r[0],18)}</td>
<td data-col="16"><span class="tk">${r[19]?r[19]+'K':'—'}</span></td>
<td data-col="17"><span class="tk">${r[20]?r[20]+'K':'—'}</span></td>
<td data-col="18">${r[21]?`<span style="font-family:var(--font-mono);font-size:10px;color:#0d9488">${r[21]}</span>`:'<span class="na">—</span>'}</td>
<td data-col="19">${r[22]!==null&&r[22]!==undefined?`<span style="font-family:var(--font-mono);font-size:10px;font-weight:700;color:${r[22]<=1?'#16a34a':r[22]<=3?'#d97706':'#e11d48'}">${r[22]}s</span>`:'<span class="na">—</span>'}</td>
<td data-col="20">${visCell(r[23])}</td>
<td data-col="21">${archCell(r)}</td>
<td data-col="22">${r[26]?`<span class="eloc" style="font-family:var(--font-mono);font-size:10px;font-weight:700">${r[26]}</span>`:'<span class="na">—</span>'}</td>
<td data-col="23">${licCell(r[27])}</td>
<td data-col="24">${priceCell(r)}</td>
`;
tb.appendChild(tr);
});
}
buildTable(D);
// ========== SORTING ==========
let sortDir=1,lastCol=-1;
function srt(col){
if(lastCol===col)sortDir*=-1; else sortDir=1;
lastCol=col;
const th=document.querySelectorAll('th');
th.forEach(t=>t.classList.remove('on'));
if(th[col])th[col].classList.add('on');
const arr=[...D].sort((a,b)=>{
let va,vb;
switch(col){
case 0:va=a[0];vb=b[0];break;
case 2:va=compScore(a)||0;vb=compScore(b)||0;break;
case 3:va=a[6];vb=b[6];break;
case 4:va=a[7]||0;vb=b[7]||0;break;
case 5:va=a[8]||0;vb=b[8]||0;break;
case 6:va=a[9]||0;vb=b[9]||0;break;
case 7:va=a[10]||0;vb=b[10]||0;break;
case 8:va=a[11]||0;vb=b[11]||0;break;
case 9:va=a[12]||0;vb=b[12]||0;break;
case 10:va=a[13]||0;vb=b[13]||0;break;
case 11:va=a[14]||0;vb=b[14]||0;break;
case 12:va=a[15]||0;vb=b[15]||0;break;
case 13:va=a[16]||0;vb=b[16]||0;break;
case 14:va=a[17]||0;vb=b[17]||0;break;
case 15:va=a[18]||0;vb=b[18]||0;break;
case 16:va=a[19]||0;vb=b[19]||0;break;
case 17:va=a[20]||0;vb=b[20]||0;break;
case 18:va=a[21]||0;vb=b[21]||0;break;
case 19:va=a[22]||99;vb=b[22]||99;break;
case 22:va=a[26]||0;vb=b[26]||0;break;
case 24:va=a[28]||0;vb=b[28]||0;break;
case 35:va=a[35]||0;vb=b[35]||0;break;
case 36:va=a[36]||0;vb=b[36]||0;break;
default:va=0;vb=0;
}
if(typeof va==='string')return va.localeCompare(vb)*sortDir;
return(va-vb)*sortDir;
});
buildTable(arr);
applyFilter(currentFilter);
applySearch(document.getElementById('searchBox').value);
applyHiddenCols();
}
// ========== Default sort: Composite Score descending ==========
(function defaultSort(){
sortDir=-1; lastCol=2;
const arr=[...D].sort((a,b)=>(compScore(b)||0)-(compScore(a)||0));
buildTable(arr);
const th=document.querySelectorAll('th');
if(th[2])th[2].classList.add('on');
})();
let currentFilter='all';
function flt(f,btn){
currentFilter=f;
document.querySelectorAll('.fb').forEach(b=>b.classList.remove('on'));
btn.classList.add('on');
applyFilter(f);
}
function applyFilter(f){
document.querySelectorAll('#TB tr').forEach(tr=>{
const g=tr.dataset.group,tp=tr.dataset.type,arch=tr.dataset.arch;
const vis=JSON.parse(tr.dataset.vis||'[]'),val=parseInt(tr.dataset.val||0);
let show=true;
if(f==='open')show=tp==='open';
else if(f==='closed')show=tp==='closed';
else if(f==='qwen')show=g==='qwen';
else if(f==='gptoss')show=g==='gptoss';
else if(f==='reasoning')show=arch.toLowerCase().includes('reasoning');
else if(f==='moe')show=arch.toLowerCase().includes('moe');
else if(f==='vision')show=vis.some(v=>['Image','Video','Audio'].includes(v));
else if(f==='value')show=val>=4&&tp==='open';
else if(f==='flagship')show=g==='flagship';
else if(f==='korean')show=g==='korean';
if(!show)tr.classList.add('hidden'); else tr.classList.remove('hidden');
});
applySearch(document.getElementById('searchBox').value);
}
// ========== SEARCH ==========
function doSearch(q){applySearch(q);}
function applySearch(q){
const s=q.toLowerCase().trim();
document.querySelectorAll('#TB tr').forEach(tr=>{
if(!s){tr.classList.remove('search-hidden');return;}
const n=tr.dataset.name||'';
if(n.includes(s))tr.classList.remove('search-hidden');
else tr.classList.add('search-hidden');
});
}
// ========== COLUMN TOGGLE ==========
const colLabels={4:"MMLU-Pro",5:"GPQA◆",6:"AIME25",7:"HLE",8:"ARC-AGI-2",9:"Metacog",10:"SWE-Pro",11:"BFCL",12:"IFEval",13:"LCB",35:"TB2.0★",36:"SciCode★",14:"SWE-V",15:"MMMLU",16:"CtxIn",17:"CtxOut",18:"tok/s",19:"TTFT",20:"Vision",21:"Arch",22:"ELO",23:"License",24:"$/M"};
const hiddenCols=new Set();
function buildColMenu(){
const menu=document.getElementById('colMenu');
menu.innerHTML='';
Object.entries(colLabels).forEach(([ci,label])=>{
const d=document.createElement('label');
d.className='col-chk';
d.innerHTML=`<input type="checkbox" checked onchange="toggleCol(${ci},this.checked)"> ${label}`;
menu.appendChild(d);
});
}
buildColMenu();
// ========== FLAGSHIP VLM DATA (v2.1) ==========
// [MMMU, MMMU-Pro, MathVista, AI2D, OCRBench(÷10), MMStar, HallusionBench, MMBenchEN, RealWorldQA, VideoMME]
// OCRBench is /1000, others are %. For display: OCRBench shown as raw score.
const VLF_MODELS=[
{name:"Gemini 3 Flash",color:"#34a853",prov:"Google",type:"closed",conf:2,src:"Vals.ai + Google"},
{name:"Gemini 3 Pro",color:"#4285f4",prov:"Google",type:"closed",conf:2,src:"Vals.ai + Google DeepMind"},
{name:"Gemini 3.1 Pro",color:"#1a73e8",prov:"Google",type:"closed",conf:1,src:"Artificial Analysis independent eval"},
{name:"GPT-5.2",color:"#10a37f",prov:"OpenAI",type:"closed",conf:2,src:"Vals.ai + OpenAI official"},
{name:"GPT-5 (original)",color:"#ec4899",prov:"OpenAI",type:"closed",conf:1,src:"OpenAI official"},
{name:"Claude Opus 4.6",color:"#d97706",prov:"Anthropic",type:"closed",conf:1,src:"Anthropic + digitalapplied"},
{name:"Grok 4 Heavy",color:"#ef4444",prov:"xAI",type:"closed",conf:1,src:"Vals.ai"},
{name:"InternVL3.5-241B",color:"#3b82f6",prov:"OpenGVLab",type:"open",conf:1,src:"InternVL3.5 paper arXiv"},
{name:"InternVL3-78B",color:"#6366f1",prov:"OpenGVLab",type:"open",conf:2,src:"InternVL3 paper + OpenCompass"},
{name:"Qwen2.5-VL-72B",color:"#f97316",prov:"Alibaba",type:"open",conf:2,src:"DataCamp + Qwen HF model card"},
{name:"Kimi-VL-A3B-Thinking",color:"#8b5cf6",prov:"Moonshot",type:"open",conf:1,src:"Moonshot AI official"},
];
const VLF_DATA=[
// Gemini 3 Flash: MMMU 87.63 (Vals.ai independent eval ✓✓), MMMU-Pro 80.0 (Artificial Analysis ✓)
[87.6,80.0,null,null,null,null,null,null,null,null],
// Gemini 3 Pro: MMMU 87.51 (Vals.ai ✓✓), MMMU-Pro 80.0 (Artificial Analysis ✓)
[87.5,80.0,null,null,null,null,null,null,null,null],
// Gemini 3.1 Pro: MMMU-Pro 82.0 (Artificial Analysis independent eval ✓)
[null,82.0,null,null,null,null,null,null,null,null],
// GPT-5.2: MMMU 86.67 (Vals.ai independent eval ✓✓)
[86.7,null,null,null,null,null,null,null,null,null],
// GPT-5 (original): MMMU 84.2 (OpenAI official intro page ✓)
[84.2,null,null,null,null,null,null,null,null,null],
// Claude Opus 4.6: MMMU-Pro 85.1 (digitalapplied + SmartScope confirm ✓)
[null,85.1,null,null,null,null,null,null,null,null],
// Grok 4 Heavy: MMMU 76.5 (Vals.ai independent eval ✓)
[76.5,null,null,null,null,null,null,null,null,null],
// InternVL3.5-241B-A28B: MMMU 77.7 (arXiv:2508.18265 paper ✓)
[77.7,null,null,null,null,null,null,null,null,null],
// InternVL3-78B: MMMU 72.2 (arXiv paper ✓✓), MathVista 79.6 (arXiv table ✓✓), AI2D 89.7 (arXiv table ✓✓), OCR 90.6=906/1000 (paper ✓✓), Hallusion 59.1 (paper ✓✓), MMBench 89.0 (paper ✓✓), RealWorld 78.0 (paper ✓)
[72.2,null,79.6,89.7,90.6,null,59.1,89.0,78.0,null],
// Qwen2.5-VL-72B: MMMU 70.2 (DataCamp + HF model card ✓✓), MathVista 74.8 (HF ✓), MMStar 70.8 (HF ✓)
[70.2,null,74.8,null,null,70.8,null,null,null,null],
// Kimi-VL-A3B-Thinking-2506: MMMU 64.0, MMMU-Pro 46.3, MathVista 80.1, MMBench 84.4, MMStar 70.4, RealWorld 70.0 (all from DataCamp citing Moonshot AI ✓)
[64.0,46.3,80.1,null,null,70.4,null,84.4,70.0,null],
];
const VLF_HEADERS=['MMMU','MMMU-Pro','MathVista','AI2D','OCRBench','MMStar','Hallusion','MMBenchEN','RealWorldQA','VideoMME'];
function buildFlagshipVLM(){
const tb=document.getElementById('VTF');if(!tb)return;tb.innerHTML='';
// Find column maxes
const maxes=VLF_HEADERS.map((_,ci)=>{
const vals=VLF_DATA.map(r=>typeof r[ci]==='number'?r[ci]:0);
return Math.max(...vals);
});
// Sort by MMMU score descending (use max of MMMU and MMMU-Pro as proxy)
const indices=VLF_DATA.map((_,i)=>i);
indices.sort((a,b)=>{
const sa=Math.max(VLF_DATA[a][0]||0,VLF_DATA[a][1]||0);
const sb=Math.max(VLF_DATA[b][0]||0,VLF_DATA[b][1]||0);
return sb-sa;
});
indices.forEach((ri,rank)=>{
const m=VLF_MODELS[ri],row=VLF_DATA[ri];
const confSym=m.conf===2?'<span style="color:#16a34a;font-size:6px;font-weight:800" title="Cross-verified: '+m.src+'">✓✓</span>':m.conf===1?'<span style="color:#d97706;font-size:6px;font-weight:800" title="Single source: '+m.src+'">✓</span>':'';
const typeBadge=m.type==='open'?'<span style="font-size:7px;background:#16a34a22;color:#16a34a;padding:1px 4px;border-radius:3px;font-weight:700">OPEN</span>':'<span style="font-size:7px;background:#6366f122;color:#6366f1;padding:1px 4px;border-radius:3px;font-weight:700">API</span>';
const tr=document.createElement('tr');
if(rank<3)tr.style.background='rgba(99,102,241,0.03)';
tr.innerHTML=`<td style="min-width:140px"><div style="display:flex;align-items:center;gap:4px">
<span style="font-size:11px;font-weight:900;color:${rank<3?m.color:'var(--text-muted)'};min-width:16px">${rank+1}</span>
<div><div style="font-size:10px;font-weight:700;color:${m.color}">${m.name}</div>
<div style="font-size:8px;color:var(--text-muted)">${m.prov} ${typeBadge} ${confSym}</div></div>
</div></td>`+row.map((v,ci)=>{
if(v===null||v===undefined)return'<td><span class="na">—</span></td>';
const isMax=v===maxes[ci]&&v>0;
const n=parseFloat(v);
const c=n>=85?'#6366f1':n>=70?'#0d9488':n>=50?'#d97706':'#e11d48';
return`<td style="${isMax?'background:rgba(99,102,241,.08);':''}"><div class="sc"><span class="sn" style="color:${c};font-weight:${isMax?'900':'600'}">${v}</span><div class="sb"><div class="sf" style="width:${Math.min(n,100)}%;background:${c}"></div></div></div></td>`;
}).join('');
tb.appendChild(tr);
});
}
buildFlagshipVLM();
// ========== VISION LANGUAGE DATA (Lightweight Detail) ==========
const VL_MODELS=[
{name:"GPT-5-Nano",color:"#10a37f",prov:"OpenAI"},
{name:"Gemini-2.5-FL-Lite",color:"#4285f4",prov:"Google"},
{name:"Qwen3-VL-30B-A3B",color:"#f97316",prov:"Alibaba"},
{name:"Qwen3.5-9B",color:"#ea580c",prov:"Alibaba"},
{name:"Qwen3.5-4B",color:"#c2410c",prov:"Alibaba"}
];
// Table 1: STEM & Puzzle
// [MMMU, MMMU-Pro, MathVision, MathVista, We-Math, DynaMath, ZEROBench, ZEROBench_sub, VlmsAreBlind, BabyVision]
const VL1=[
[75.8,57.2,62.2,71.5,62.5,78.0,1.0,22.2,66.7,14.4],
[73.4,59.7,52.1,72.8,32.1,69.9,1.0,19.2,68.4,17.5],
[76.0,63.0,65.7,81.9,70.0,80.1,0.0,23.7,72.5,18.6],
[78.4,70.1,78.9,85.7,75.2,83.6,3.0,31.1,93.7,"28.6/25.8"],
[77.6,66.3,74.6,85.1,75.4,83.3,3.0,26.3,92.6,"16.0/19.1"]
];
// Table 2: General VQA & Document
// [RealWorldQA, MMStar, MMBenchEN, SimpleVQA, HallusionBench, OmniDocBench, CharXiv, MMLongBench, CC-OCR, AI2D, OCRBench]
const VL2=[
[71.8,68.6,80.3,46.0,58.4,55.9,50.1,31.8,58.9,81.9,75.3],
[72.2,69.1,82.7,54.1,64.5,79.4,56.1,46.5,72.9,85.7,82.5],
[77.4,75.5,88.9,54.3,66.0,86.8,56.6,47.4,77.8,86.9,83.9],
[80.3,79.7,90.1,51.2,69.3,87.7,73.0,57.7,79.3,90.2,89.2],
[79.5,78.3,89.4,43.4,65.0,86.2,70.8,54.2,76.7,89.6,85.0]
];
// Table 3: Spatial, Video, Agent, Medical
// [ERQA, CountBench, EmbSpatialBench, RefSpatialBench, LingoQA, VideoMME+sub, VideoMME, VideoMMMU, MLVU, MMVU, ScreenSpotPro, OSWorld, AndroidWorld, TIR-Bench, SLAKE, PMC-VQA, MedXpertQA]
const VL3=[
[45.8,80.0,74.2,12.6,57.0,71.7,66.2,63.0,69.2,63.1,null,null,null,18.5,57.0,37.8,26.7],
[44.3,79.2,66.1,11.2,17.8,74.6,72.7,69.2,78.5,65.3,null,null,null,21.5,65.0,48.8,35.3],
[45.3,90.0,80.6,54.2,62.0,79.9,73.3,75.0,78.9,66.1,60.5,30.6,55.0,22.5,68.8,51.5,35.5],
[55.5,97.2,83.0,58.5,80.4,84.5,78.4,78.9,84.4,67.8,65.2,41.8,57.8,"45.6/31.9",79.0,57.9,49.9],
[54.0,96.3,81.3,54.6,74.4,83.5,76.9,74.1,82.8,64.9,60.3,35.6,58.6,"38.9/29.9",76.1,55.5,42.9]
];
function vlScoreCell(v){
if(v===null||v===undefined)return'<span class="na">—</span>';
const sv=String(v);
if(sv.includes('/')){
const parts=sv.split('/');
const p=parseFloat(parts[0]);
const c=p>=80?'#6366f1':p>=60?'#0d9488':p>=40?'#d97706':'#e11d48';
return`<div class="sc"><span class="sn" style="color:${c};font-size:9px">${sv}</span></div>`;
}
const n=parseFloat(v);
const c=n>=80?'#6366f1':n>=60?'#0d9488':n>=40?'#d97706':'#e11d48';
return`<div class="sc"><span class="sn" style="color:${c}">${v}</span><div class="sb"><div class="sf" style="width:${Math.min(n,100)}%;background:${c}"></div></div></div>`;
}
function buildVLTables(){
// Find max per column for highlighting
function buildVLSection(tbId,data){
const tb=document.getElementById(tbId);
if(!tb)return;
tb.innerHTML='';
// find col maxes
const maxes=data[0].map((_,ci)=>{
const vals=data.map(r=>{const v=r[ci];return typeof v==='number'?v:typeof v==='string'?parseFloat(v):0;});
return Math.max(...vals);
});
data.forEach((row,ri)=>{
const m=VL_MODELS[ri];
const tr=document.createElement('tr');
tr.innerHTML=`<td class="c-model" style="min-width:140px"><div class="mc"><div class="mn" style="font-size:10px"><span style="display:inline-block;width:6px;height:6px;border-radius:50%;background:${m.color};margin-right:4px"></span>${m.name}</div><div class="mp">${m.prov}</div></div></td><td></td>`+row.map((v,ci)=>{
const isMax=typeof v==='number'&&v===maxes[ci]&&v>0;
return`<td style="${isMax?'background:rgba(99,102,241,.06);':''}font-weight:${isMax?'800':'400'}">${vlScoreCell(v)}</td>`;
}).join('');
tb.appendChild(tr);
});
}
buildVLSection('VTB1',VL1);
buildVLSection('VTB2',VL2);
buildVLSection('VTB3',VL3);
}
buildVLTables();
// ========== AGENT BENCH DATA ==========
const AGENT_DATA=[
["GPT-5.4","OpenAI","#10a37f",75.0,null,82.7,null,83,null,null,null],
["Claude Opus 4.6","Anthropic","#d97706",72.7,91.9,84.0,74.7,1606,45.0,null,null],
["Claude Sonnet 4.6","Anthropic","#f59e0b",72.5,null,null,53.0,1633,null,null,null],
["Gemini 3.1 Pro","Google","#4285f4",null,99.3,85.9,78.4,1317,null,null,null],
["GPT-5.2","OpenAI","#10a37f",38.2,82.0,77.9,64.9,null,null,null,null],
["GPT-5.3 Codex","OpenAI","#047857",null,null,null,77.3,null,57.0,null,null],
["Gemini 3 Flash","Google","#34a853",null,null,null,64.3,null,null,null,null],
["Qwen3.5-9B","Alibaba","#f97316",null,79.9,null,null,null,null,66.1,57.8],
["Qwen3.5-4B","Alibaba","#c2410c",null,79.1,null,null,null,null,50.3,58.6],
["MiniMax-M2.5","MiniMax","#ff6b35",null,null,null,42.2,null,null,null,null],
];
function buildAgentTable(){
const tb=document.getElementById('ATB');if(!tb)return;tb.innerHTML='';
AGENT_DATA.forEach(r=>{
const tr=document.createElement('tr');
let cells=`<td class="c-model" style="min-width:140px"><div class="mc"><div class="mn" style="font-size:10.5px"><span style="display:inline-block;width:6px;height:6px;border-radius:50%;background:${r[2]};margin-right:4px"></span>${r[0]}</div><div class="mp">${r[1]}</div></div></td>`;
for(let i=3;i<=10;i++){
const v=r[i];
if(v===null||v===undefined){cells+='<td><span class="na">—</span></td>';continue;}
const isElo=i===7;
if(isElo){const c=v>=1500?'#6366f1':v>=1300?'#0d9488':'#d97706';cells+=`<td><span style="font-family:var(--font-mono);font-size:10px;font-weight:700;color:${c}">${v}</span></td>`;}
else{const c=v>=80?'#6366f1':v>=60?'#0d9488':v>=40?'#d97706':'#e11d48';cells+=`<td>${vlScoreCell(v)}</td>`;}
}
tr.innerHTML=cells;tb.appendChild(tr);
});
}
buildAgentTable();
// ========== IMAGE GENERATION DATA ==========
const IMG_DATA=[
["GPT Image 1.5","OpenAI","2026.01","#1","S","A","S","S","B","$$","Prop"],
["Imagen 4","Google","2025.12","#2","S","A","S","A","S","$$","Prop"],
["Flux 2 Pro","BFL","2026.01","#3","S","A","A","S","A","$$","Prop"],
["Midjourney v7","Midjourney","2026.01","#4","A","S","B","B","A","$$","Prop"],
["Flux 2 Dev","BFL","2026.01","—","A","A","A","A","A","Free","Apache2"],
["Ideogram 3.0","Ideogram","2025.12","#5","A","B","S","A","A","$","Prop"],
["DALL-E 3.5","OpenAI","2025.08","#6","A","B","A","S","A","$$","Prop"],
["Nano Banana 2","Google","2025.12","—","A","A","B","A","S","$","Prop"],
["SD 3.5","Stability AI","2024.10","—","B","A","B","B","S","Free","Open"],
["Seedream 4.5","ByteDance","2025.11","—","A","B","B","A","S","$","Prop"],
];
function imgRatingCell(v){
if(!v||v==='—')return'<span class="na">—</span>';
const m={'S':{c:'#6366f1',bg:'rgba(99,102,241,.1)',b:'rgba(99,102,241,.2)'},'A':{c:'#0d9488',bg:'rgba(13,148,136,.1)',b:'rgba(13,148,136,.2)'},'B':{c:'#d97706',bg:'rgba(217,119,6,.1)',b:'rgba(217,119,6,.2)'},'C':{c:'#94a3b8',bg:'rgba(148,163,184,.1)',b:'rgba(148,163,184,.2)'}};
const s=m[v]||m['C'];
return`<span style="display:inline-block;padding:2px 8px;border-radius:4px;font-size:9px;font-family:var(--font-mono);font-weight:700;background:${s.bg};color:${s.c};border:1px solid ${s.b}">${v}</span>`;
}
function buildImageTable(){
const tb=document.getElementById('ITB');if(!tb)return;tb.innerHTML='';
const pc={"OpenAI":"#10a37f","Google":"#4285f4","BFL":"#1a1a2e","Midjourney":"#7c3aed","Ideogram":"#f97316","Stability AI":"#8b5cf6","ByteDance":"#0081fb"};
IMG_DATA.forEach(r=>{
const tr=document.createElement('tr');const cc=pc[r[1]]||'#64748b';
const costM={'Free':'<span style="color:#16a34a;font-weight:700">Free</span>','$':'<span style="color:#0d9488">Low</span>','$$':'<span style="color:#d97706">Mid</span>'};
const licM={'Prop':'<span class="lic lp">Proprietary</span>','Apache2':'<span class="lic la">Apache2</span>','Open':'<span class="lic lm">Open</span>'};
tr.innerHTML=`<td class="c-model" style="min-width:150px"><div class="mc"><div class="mn" style="font-size:10.5px">${r[0]}</div></div></td>
<td><span class="prov" style="background:${cc}1a;color:${cc};border-color:${cc}40;font-size:7.5px">${r[1]}</span></td>
<td><span style="font-family:var(--font-mono);font-size:9px;color:var(--text-muted)">${r[2]}</span></td>
<td><span style="font-family:var(--font-mono);font-size:10px;font-weight:700;color:${r[3]==='#1'?'#6366f1':r[3]==='#2'?'#0d9488':'var(--text-sec)'}">${r[3]}</span></td>
<td>${imgRatingCell(r[4])}</td><td>${imgRatingCell(r[5])}</td><td>${imgRatingCell(r[6])}</td><td>${imgRatingCell(r[7])}</td><td>${imgRatingCell(r[8])}</td>
<td style="font-family:var(--font-mono);font-size:9px">${costM[r[9]]||r[9]}</td><td>${licM[r[10]]||r[10]}</td>`;
tb.appendChild(tr);
});
}
buildImageTable();
// ========== VIDEO GENERATION DATA ==========
const VID_DATA=[
["Sora 2","OpenAI","2025.09","S","S","A","S","20s","1080p","$$$","Prop"],
["Veo 3.1","Google","2026.01","S","A","S","S","8s","4K","$$","Prop"],
["Runway Gen-4.5","Runway","2026.02","S","S","A","A","16s","1080p","$$","Prop"],
["Kling 3.0","Kuaishou","2026.02","A","A","A","A","5min","1080p","$","Prop"],
["Seedance 2.0","ByteDance","2026.01","S","A","S","A","10s","2K","$$","Prop"],
["Wan 2.6","Alibaba","2026.01","A","A","B","A","10s","1080p","Free","Open"],
["Pika 2.5","Pika","2025.12","B","B","B","A","10s","1080p","$","Prop"],
["Luma Ray3","Luma","2026.01","A","A","B","B","10s","4K","$$","Prop"],
["LTX-2","Lightricks","2026.01","A","B","A","B","10s","4K","Free","Open"],
["HaiLuo AI","MiniMax","2025.12","B","A","B","B","6s","1080p","$","Prop"],
];
function buildVideoTable(){
const tb=document.getElementById('VIDTB');if(!tb)return;tb.innerHTML='';
const pc={"OpenAI":"#10a37f","Google":"#4285f4","Runway":"#ff7043","Kuaishou":"#f97316","ByteDance":"#0081fb","Alibaba":"#f97316","Pika":"#e11d48","Luma":"#8b5cf6","Lightricks":"#14b8a6","MiniMax":"#ff6b35"};
VID_DATA.forEach(r=>{
const tr=document.createElement('tr');const cc=pc[r[1]]||'#64748b';
const costM={'Free':'<span style="color:#16a34a;font-weight:700">Free</span>','$':'<span style="color:#0d9488">Low</span>','$$':'<span style="color:#d97706">Mid</span>','$$$':'<span style="color:#e11d48">High</span>'};
const licM={'Prop':'<span class="lic lp">Proprietary</span>','Open':'<span class="lic lm">Open Source</span>'};
tr.innerHTML=`<td class="c-model" style="min-width:140px"><div class="mc"><div class="mn" style="font-size:10.5px">${r[0]}</div></div></td>
<td><span class="prov" style="background:${cc}1a;color:${cc};border-color:${cc}40;font-size:7.5px">${r[1]}</span></td>
<td><span style="font-family:var(--font-mono);font-size:9px;color:var(--text-muted)">${r[2]}</span></td>
<td>${imgRatingCell(r[3])}</td><td>${imgRatingCell(r[4])}</td><td>${imgRatingCell(r[5])}</td><td>${imgRatingCell(r[6])}</td>
<td><span style="font-family:var(--font-mono);font-size:10px;font-weight:600;color:var(--text)">${r[7]}</span></td>
<td><span style="font-family:var(--font-mono);font-size:9px;color:${r[8]==='4K'?'#6366f1':r[8]==='2K'?'#0d9488':'var(--text-sec)'}">${r[8]}</span></td>
<td style="font-family:var(--font-mono);font-size:9px">${costM[r[9]]||r[9]}</td><td>${licM[r[10]]||r[10]}</td>`;
tb.appendChild(tr);
});
}
buildVideoTable();
// ========== MUSIC GENERATION DATA ==========
const MUS_DATA=[
["Suno v4.5","Suno","2025.11","S","S","S","S","4min","$$","Prop"],
["Udio v2","Udio","2025.10","S","A","S","A","4min","$$","Prop"],
["Gemini Music","Google","2026.01","A","A","A","A","60s","$","Prop"],
["MusicGen Large","Meta","2024.06","B","A","B","A","30s","Free","Open"],
["Stable Audio 2.0","Stability AI","2024.04","B","A","B","B","3min","$","Open"],
["JASCO","Meta","2024.12","C","A","C","B","30s","Free","Open"],
["Riffusion v2","Riffusion","2025.06","B","B","B","A","30s","$","Prop"],
["Loudme","Loudme","2025.09","A","A","A","B","5min","$","Prop"],
];
function buildMusicTable(){
const tb=document.getElementById('MUSTB');if(!tb)return;tb.innerHTML='';
const pc={"Suno":"#f43f5e","Udio":"#8b5cf6","Google":"#4285f4","Meta":"#0081fb","Stability AI":"#7c3aed","Riffusion":"#d97706","Loudme":"#0d9488"};
MUS_DATA.forEach(r=>{
const tr=document.createElement('tr');const cc=pc[r[1]]||'#64748b';
const costM={'Free':'<span style="color:#16a34a;font-weight:700">Free</span>','$':'<span style="color:#0d9488">Low</span>','$$':'<span style="color:#d97706">Mid</span>'};
const licM={'Prop':'<span class="lic lp">Proprietary</span>','Open':'<span class="lic lm">Open Source</span>'};
tr.innerHTML=`<td class="c-model" style="min-width:140px"><div class="mc"><div class="mn" style="font-size:10.5px">${r[0]}</div></div></td>
<td><span class="prov" style="background:${cc}1a;color:${cc};border-color:${cc}40;font-size:7.5px">${r[1]}</span></td>
<td><span style="font-family:var(--font-mono);font-size:9px;color:var(--text-muted)">${r[2]}</span></td>
<td>${imgRatingCell(r[3])}</td><td>${imgRatingCell(r[4])}</td><td>${imgRatingCell(r[5])}</td><td>${imgRatingCell(r[6])}</td>
<td><span style="font-family:var(--font-mono);font-size:10px;font-weight:600;color:var(--text)">${r[7]}</span></td>
<td style="font-family:var(--font-mono);font-size:9px">${costM[r[8]]||r[8]}</td><td>${licM[r[9]]||r[9]}</td>`;
tb.appendChild(tr);
});
}
buildMusicTable();
// ========== ADVANCED SEARCH ENGINE ==========
function advQ(q){document.getElementById('advSearch').value=q;advancedSearch(q);}
function advancedSearch(raw){
const q=raw.trim().toLowerCase();
const countEl=document.getElementById('searchResultCount');
// Tab navigation keywords
const tabMap={'video':'vidgen','image':'imggen','music':'musicgen','agent':'agent','vlm':'vision','vision':'vision','tool':'three','chart':'charts','info':'info'};
for(const[kw,tabId] of Object.entries(tabMap)){
if(q===kw||q===kw+'s'||q===kw+' generation'||q===kw+' gen'){
const tabEl=document.querySelector(`.tab[onclick*="'${tabId}'"]`);
if(tabEl){showTab(tabId,tabEl);countEl.textContent='→ '+kw.toUpperCase()+' tab';return;}
}
}
// Parse benchmark conditions: "GPQA > 90", "AIME > 95", "HLE > 30", "score > 70", "price < 1"
const benchAlias={
'mmlu':7,'mmlupro':7,'mmlu-pro':7,
'gpqa':8,'gpqa diamond':8,
'aime':9,'aime25':9,'aime2025':9,
'hle':10,
'arc':11,'arcagi':11,'arc-agi':11,'arc-agi-2':11,
'metacog':12,'metacognitive':12,'final':12,
'swe':13,'swepro':13,'swe-pro':13,
'bfcl':14,
'ifeval':15,
'lcb':16,'livecodebench':16,
'mmmlu':18,
'score':99, // special: composite score
'price':98 // special: price
};
// Parse conditions
const conditions=[];
const condRx=/(\w[\w\-]*)\s*([><=!]+)\s*([\d.]+)/g;
let m;
while((m=condRx.exec(q))!==null){
const key=m[1].toLowerCase();const op=m[2];const val=parseFloat(m[3]);
const benchIdx=benchAlias[key];
if(benchIdx!==undefined)conditions.push({idx:benchIdx,op,val});
}
// Parse text keywords (remaining after removing conditions)
let textQ=q.replace(/(\w[\w\-]*)\s*([><=!]+)\s*([\d.]+)/g,'').trim();
// Check for type filters
let typeFilter=null;
if(textQ.includes('open')){typeFilter='open';textQ=textQ.replace('open','').trim();}
if(textQ.includes('closed')){typeFilter='closed';textQ=textQ.replace('closed','').trim();}
if(textQ==='free'){typeFilter='free';textQ='';}
// Check for provider name
const provNames=['openai','anthropic','google','alibaba','deepseek','moonshot','meta','mistral','xai','minimax','zhipu','microsoft'];
let provFilter=null;
for(const pn of provNames){
if(textQ.includes(pn)){provFilter=pn;textQ=textQ.replace(pn,'').trim();break;}
}
// Check for arch filters
let archFilter=null;
if(textQ.includes('moe')){archFilter='moe';textQ=textQ.replace('moe','').trim();}
if(textQ.includes('dense')){archFilter='dense';textQ=textQ.replace('dense','').trim();}
// Apply to table rows
let shown=0,total=0;
document.querySelectorAll('#TB tr').forEach(tr=>{
total++;
const name=tr.dataset.name||'';
const idx=D.findIndex(r=>r[0].toLowerCase()===name);
if(idx===-1){tr.classList.add('hidden');return;}
const r=D[idx];
let pass=true;
// Text name search
if(textQ&&!name.includes(textQ))pass=false;
// Type filter
if(typeFilter==='open'&&r[3]!=='open')pass=false;
if(typeFilter==='closed'&&r[3]!=='closed')pass=false;
if(typeFilter==='free'&&r[28]!==0)pass=false;
// Provider filter
if(provFilter&&!r[1].toLowerCase().includes(provFilter))pass=false;
// Arch filter
if(archFilter==='moe'&&!r[24].toLowerCase().includes('moe'))pass=false;
if(archFilter==='dense'&&!r[24].toLowerCase().includes('dense'))pass=false;
// Benchmark conditions
for(const cond of conditions){
let v;
if(cond.idx===99)v=compScore(r);
else if(cond.idx===98)v=r[28];
else v=r[cond.idx];
if(v===null||v===undefined){pass=false;break;}
if(cond.op==='>'&&!(v>cond.val))pass=false;
if(cond.op==='>='&&!(v>=cond.val))pass=false;
if(cond.op==='<'&&!(v<cond.val))pass=false;
if(cond.op==='<='&&!(v<=cond.val))pass=false;
if(cond.op==='='&&!(Math.abs(v-cond.val)<0.5))pass=false;
}
if(pass){tr.classList.remove('hidden');shown++;}
else tr.classList.add('hidden');
});
if(q){
countEl.textContent=shown+'/'+total+' models';
countEl.style.color=shown>0?'var(--ac)':'var(--rose)';
}else{
countEl.textContent='';
document.querySelectorAll('#TB tr').forEach(tr=>tr.classList.remove('hidden'));
}
// Also search in old simple search box for backward compat
document.getElementById('searchBox').value=textQ;
}
function toggleColMenu(){
const m=document.getElementById('colMenu');
m.classList.toggle('open');
}
document.addEventListener('click',e=>{
if(!e.target.closest('.col-toggle-wrap'))document.getElementById('colMenu').classList.remove('open');
});
function toggleCol(ci,show){
const sel=`[data-col="${ci}"]`;
document.querySelectorAll(sel).forEach(el=>{el.style.display=show?'':'none';});
const th=document.querySelector(`th[data-col="${ci}"]`);
if(th)th.style.display=show?'':'none';
if(show)hiddenCols.delete(ci); else hiddenCols.add(ci);
}
function applyHiddenCols(){
hiddenCols.forEach(ci=>toggleCol(ci,false));
}
// ========== TABS ==========
function showTab(id,el){
document.querySelectorAll('.tpane').forEach(p=>p.classList.remove('on'));
document.querySelectorAll('.tab').forEach(t=>t.classList.remove('on'));
document.getElementById(id).classList.add('on');
el.classList.add('on');
if(id==='charts'&&!chartsInit)initCharts();
if(id==='vision'){buildFlagshipVLM();buildVLTables();}
if(id==='agent')buildAgentTable();
if(id==='imggen')buildImageTable();
if(id==='three')initFinder();
if(id==='report')generateReport();
if(id==='vidgen')buildVideoTable();
if(id==='musicgen')buildMusicTable();
}
let chartsInit=false;
// Init vertical ranking chart immediately
window.addEventListener('load',()=>{initVertRank();});
// ========== VERTICAL RANKING CHART (always shown in tab1) ==========
function initVertRank(){
const sorted=[...D].map(r=>({n:r[0],s:compScore(r),c:pColors[r[1]]||'#6366f1',prov:r[1]}))
.sort((a,b)=>{
const sa=a.s??-1, sb=b.s??-1;
return sb-sa;
});
const canvas=document.getElementById('cVertRank');
if(!canvas)return;
const W=Math.max(sorted.length*52+60,1100);
canvas.width=W; canvas.height=200;
const ctx=canvas.getContext('2d');
const PAD_L=40,PAD_R=20,PAD_T=16,PAD_B=60;
const chartW=W-PAD_L-PAD_R,chartH=200-PAD_T-PAD_B;
const barW=Math.min(40,chartW/sorted.length-8);
const maxS=Math.max(...sorted.map(x=>x.s));
const minS=0; // Based on 0 for honest relative comparison
// Grid lines
[0,20,40,60,80].forEach(v=>{
const y=PAD_T+chartH-(v-minS)/(maxS-minS)*chartH;
if(y<PAD_T||y>PAD_T+chartH)return;
ctx.beginPath();ctx.strokeStyle=v===0?'rgba(15,23,42,.15)':'rgba(15,23,42,.05)';
ctx.lineWidth=v===0?1.5:.7;
ctx.moveTo(PAD_L,y);ctx.lineTo(W-PAD_R,y);ctx.stroke();
ctx.font='600 8px JetBrains Mono';ctx.fillStyle='#94a3b8';ctx.textAlign='right';
ctx.fillText(v,PAD_L-4,y+3);
});
const gap=(chartW-(barW*sorted.length))/(sorted.length+1);
sorted.forEach((d,i)=>{
const x=PAD_L+gap*(i+1)+barW*i;
const isNull=d.s===null||d.s===undefined;
const score=isNull?0:d.s;
const barH=isNull?5:Math.max((score-minS)/(maxS-minS)*chartH,5);
const y=PAD_T+chartH-barH;
const rank=i+1;
// benchmark coverage count
const benchKeys=[7,8,9,10,11,12,13,14,15,16];
const origR=D.find(r=>r[0]===d.n);
const covCnt=origR?benchKeys.filter(k=>origR[k]!==null&&origR[k]!==undefined).length:0;
// Bar gradient
const grad=ctx.createLinearGradient(0,y,0,PAD_T+chartH);
grad.addColorStop(0,isNull?'#cbd5e1':d.c+'ff');
grad.addColorStop(1,isNull?'#e2e8f0':d.c+'88');
ctx.fillStyle=grad;
ctx.beginPath();
ctx.roundRect(x,y,barW,barH,4);
ctx.fill();
// Score label on top
ctx.font='700 9px JetBrains Mono';ctx.fillStyle=isNull?'#94a3b8':d.c;ctx.textAlign='center';
ctx.fillText(isNull?'N/A':d.s,x+barW/2,y-12);
// Coverage badge (n/10)
ctx.font='500 7px JetBrains Mono';ctx.fillStyle='#94a3b8';ctx.textAlign='center';
ctx.fillText(covCnt+'/10',x+barW/2,y-3);
// Rank badge
ctx.fillStyle=d.c+'22';
ctx.fillRect(x,PAD_T+chartH+2,barW,14);
ctx.font='700 7px JetBrains Mono';ctx.fillStyle=d.c;ctx.textAlign='center';
ctx.fillText('#'+rank,x+barW/2,PAD_T+chartH+11);
// Model name (angled)
ctx.save();
ctx.translate(x+barW/2,PAD_T+chartH+22);
ctx.rotate(-Math.PI/4.5);
ctx.font='600 8px Sora,sans-serif';
ctx.fillStyle='#475569';ctx.textAlign='right';
const shortN=d.n.length>14?d.n.substring(0,13)+'…':d.n;
ctx.fillText(shortN,0,0);
ctx.restore();
});
// Legend
const provs=[...new Set(D.map(r=>r[1]))];
const leg=document.getElementById('vrankLegend');
if(leg){
leg.innerHTML=provs.map(p=>`<div class="vrl"><div class="vrl-dot" style="background:${pColors[p]||'#6366f1'}"></div>${p}</div>`).join('');
}
}
// ========== CHART COLORS ==========
const pColors={
"OpenAI":"#10a37f","Anthropic":"#d97706","Google":"#4285f4",
"xAI":"#1d9bf0","Alibaba":"#f97316","DeepSeek":"#6366f1",
"Moonshot":"#8b5cf6","Zhipu AI":"#14b8a6","Meta":"#0081fb","Mistral":"#ff7043",
"Microsoft":"#00a4ef",
"LG AI Research":"#c9002b","SK Telecom":"#e8002d","Upstage":"#005baa","Motif Technologies":"#2d6be4","KT":"#e60012","Nanbeige":"#f43f5e",
"MiniMax":"#ff6b35","StepFun":"#7c3aed"
};
const gridC='rgba(15,23,42,.06)';
const tickC='#94a3b8';
function initCharts(){
chartsInit=true;
// 1. ARC-AGI-2 VERTICAL BAR
const arcData=D.filter(r=>r[11]!==null).map(r=>({n:r[0],v:r[11],c:pColors[r[1]]||'#6366f1'})).sort((a,b)=>b.v-a.v);
new Chart(document.getElementById('cArc'),{
type:'bar',
data:{labels:arcData.map(x=>x.n.length>10?x.n.substr(0,9)+'…':x.n),datasets:[{
label:'ARC-AGI-2 (%)',data:arcData.map(x=>x.v),
backgroundColor:arcData.map(x=>x.c+'bb'),borderColor:arcData.map(x=>x.c),
borderWidth:1.5,borderRadius:5,borderSkipped:false
}]},
options:{plugins:{legend:{display:false},tooltip:{callbacks:{label:c=>`ARC-AGI-2: ${c.raw}% — ${arcData[c.dataIndex].n}`}}},
scales:{y:{min:0,max:100,grid:{color:gridC},ticks:{color:tickC,font:{family:'JetBrains Mono',size:9}}},
x:{grid:{display:false},ticks:{color:tickC,font:{family:'JetBrains Mono',size:8},maxRotation:35}}}}
});
// 2. METACOG BASELINE vs METACOG (GROUPED VERTICAL)
const metaFull=[
{n:"Kimi K2.5",prov:"Moonshot",base:68.71,meta:78.54},
{n:"GPT-5.2",prov:"OpenAI",base:62.76,meta:75.5},
{n:"GLM-5",prov:"Zhipu AI",base:62.50,meta:75.0},
{n:"Gemini 3.1 Pro",prov:"Google",base:59.5,meta:77.08},
{n:"Claude Opus 4.6",prov:"Anthropic",base:56.04,meta:76.17},
].sort((a,b)=>b.base-a.base);
new Chart(document.getElementById('cMetaDelta'),{
type:'bar',
data:{
labels:metaFull.map(x=>x.n.length>12?x.n.substr(0,11)+'…':x.n),
datasets:[
{label:'Baseline Score',data:metaFull.map(x=>x.base),backgroundColor:metaFull.map(x=>pColors[x.prov]+'88'),borderColor:metaFull.map(x=>pColors[x.prov]),borderWidth:1.5,borderRadius:4},
{label:'MetaCog (self-corrected)',data:metaFull.map(x=>x.meta),backgroundColor:metaFull.map(x=>pColors[x.prov]+'33'),borderColor:metaFull.map(x=>pColors[x.prov]),borderWidth:2,borderRadius:4,borderDash:[4,2]}
]
},
options:{
plugins:{legend:{labels:{color:tickC,font:{family:'JetBrains Mono',size:8},boxWidth:10}},
tooltip:{callbacks:{afterBody:items=>{
if(items[0]){const i=items[0].dataIndex;return[` Δ gain: +${(metaFull[i].meta-metaFull[i].base).toFixed(2)}`];}
}}}
},
scales:{y:{min:45,max:85,grid:{color:gridC},ticks:{color:tickC,font:{family:'JetBrains Mono',size:9}}},
x:{grid:{display:false},ticks:{color:tickC,font:{family:'JetBrains Mono',size:8}}}}
}
});
// 3. RADAR TOP 6
const top6=["Claude Opus 4.6","GPT-5.2","Gemini 3.1 Pro","Kimi K2.5","Qwen3.5-397B","DeepSeek V3.2"];
const rColors=top6.map(n=>{const r=D.find(d=>d[0]===n);return r?pColors[r[1]]||'#6366f1':'#6366f1';});
const top6data=top6.map(n=>D.find(r=>r[0]===n));
const radarDatasets=top6data.map((r,i)=>({
label:r[0],
data:[r[7]||0,r[8]||0,r[9]||0,r[10]||0,Math.min((r[11]||0)*1.1,100),r[18]||0],
borderColor:rColors[i],backgroundColor:rColors[i]+'20',borderWidth:1.5,pointRadius:2.5,pointBackgroundColor:rColors[i]
}));
new Chart(document.getElementById('cRadar'),{
type:'radar',
data:{labels:['MMLU-Pro','GPQA◆','AIME25','HLE','ARC-AGI-2','MMMLU'],datasets:radarDatasets},
options:{plugins:{legend:{labels:{color:tickC,font:{family:'JetBrains Mono',size:7.5},boxWidth:10,padding:6}}},
scales:{r:{grid:{color:gridC},angleLines:{color:gridC},ticks:{display:false},
pointLabels:{color:tickC,font:{family:'JetBrains Mono',size:8.5}},suggestedMin:0,suggestedMax:100}}}
});
// 4. CAPABILITY DOMAIN BREAKDOWN
const domModels=["GPT-5.2","Claude Opus 4.6","Gemini 3.1 Pro","Kimi K2.5","Qwen3.5-397B","DeepSeek R1","GLM-5","Grok 4 Heavy"];
const domData=domModels.map(n=>{
const r=D.find(d=>d[0]===n);if(!r)return null;
const reasoning=[r[8],r[9],r[10]].filter(x=>x!==null);
const coding=[r[13],r[16]].filter(x=>x!==null);
const language=[r[7],r[18],r[15]].filter(x=>x!==null);
return{
n:n.length>12?n.substr(0,11)+'…':n,
c:pColors[r[1]]||'#6366f1',
reasoning:reasoning.length?Math.round(reasoning.reduce((a,b)=>a+b)/reasoning.length*10)/10:null,
coding:coding.length?Math.round(coding.reduce((a,b)=>a+b)/coding.length*10)/10:null,
language:language.length?Math.round(language.reduce((a,b)=>a+b)/language.length*10)/10:null
};
}).filter(Boolean);
new Chart(document.getElementById('cDomain'),{
type:'bar',
data:{
labels:domData.map(x=>x.n),
datasets:[
{label:'Reasoning (GPQA+AIME+HLE)',data:domData.map(x=>x.reasoning),backgroundColor:'rgba(99,102,241,.7)',borderColor:'#6366f1',borderWidth:1.5,borderRadius:3},
{label:'Coding (SWE-Pro+LCB)',data:domData.map(x=>x.coding),backgroundColor:'rgba(13,148,136,.7)',borderColor:'#0d9488',borderWidth:1.5,borderRadius:3},
{label:'Language (MMLU+MMMLU+IFEval)',data:domData.map(x=>x.language),backgroundColor:'rgba(217,119,6,.7)',borderColor:'#d97706',borderWidth:1.5,borderRadius:3}
]
},
options:{plugins:{legend:{labels:{color:tickC,font:{family:'JetBrains Mono',size:8},boxWidth:10,padding:5}}},
scales:{y:{min:0,max:100,grid:{color:gridC},ticks:{color:tickC,font:{family:'JetBrains Mono',size:9}}},
x:{grid:{display:false},ticks:{color:tickC,font:{family:'JetBrains Mono',size:8},maxRotation:30}}}}
});
// 5. PERF vs COST SCATTER
const scData=D.filter(r=>r[28]!==null&&r[28]!==undefined&&compScore(r)!==null).map(r=>({
n:r[0],x:r[28]===0?0.01:r[28],y:compScore(r),c:pColors[r[1]]||'#6366f1',prov:r[1]
}));
new Chart(document.getElementById('cScatter'),{
type:'scatter',
data:{datasets:[{
data:scData.map(x=>({x:x.x,y:x.y})),
backgroundColor:scData.map(x=>x.c+'cc'),borderColor:scData.map(x=>x.c),
pointRadius:scData.map((x,i)=>i<3?9:7),pointHoverRadius:11,borderWidth:1.5
}]},
options:{
plugins:{legend:{display:false},tooltip:{callbacks:{label:ctx=>{
const d=scData[ctx.dataIndex];return[`${d.n}`,`Score: ${d.y}`,`Price: $${d.x}/M`];
}}}},
scales:{
x:{title:{display:true,text:'Input Price ($/M tokens) — log scale',color:tickC,font:{size:8.5,family:'JetBrains Mono'}},type:'logarithmic',
grid:{color:gridC},ticks:{color:tickC,font:{family:'JetBrains Mono',size:8.5}}},
y:{title:{display:true,text:'Composite Score',color:tickC,font:{size:8.5,family:'JetBrains Mono'}},min:40,
grid:{color:gridC},ticks:{color:tickC,font:{family:'JetBrains Mono',size:8.5}}}
}
}
});
// 6. PROVIDER AVERAGE SCORE
const provGroups={};
D.forEach(r=>{const cs=compScore(r);if(cs&&r[1]){if(!provGroups[r[1]])provGroups[r[1]]=[];provGroups[r[1]].push(cs);}});
const provAvg=Object.entries(provGroups).map(([p,arr])=>({
p,avg:Math.round(arr.reduce((a,b)=>a+b)/arr.length*10)/10,
cnt:arr.length,c:pColors[p]||'#6366f1',max:Math.max(...arr),min:Math.min(...arr)
})).sort((a,b)=>b.avg-a.avg);
new Chart(document.getElementById('cProvider'),{
type:'bar',
data:{
labels:provAvg.map(x=>x.p),
datasets:[
{label:'Avg Score',data:provAvg.map(x=>x.avg),backgroundColor:provAvg.map(x=>x.c+'bb'),borderColor:provAvg.map(x=>x.c),borderWidth:1.5,borderRadius:5,borderSkipped:false},
{label:'Best Model',data:provAvg.map(x=>x.max),type:'line',borderColor:provAvg.map(x=>x.c),pointBackgroundColor:provAvg.map(x=>x.c),pointRadius:5,fill:false,tension:.3,borderWidth:2}
]
},
options:{plugins:{legend:{labels:{color:tickC,font:{family:'JetBrains Mono',size:8},boxWidth:10}}},
scales:{y:{min:40,grid:{color:gridC},ticks:{color:tickC,font:{family:'JetBrains Mono',size:9}}},
x:{grid:{display:false},ticks:{color:tickC,font:{family:'JetBrains Mono',size:9},maxRotation:30}}}}
});
// 7. INTELLIGENCE TIMELINE BUBBLE
const dateMap={"2025.01":1,"2025.04":4,"2025.06":6,"2025.07":7,"2025.08":8,"2025.09":9,"2025.10":10,"2025.11":11,"2025.12":12,"2026.01":13,"2026.02":14,"2026.03":15};
const tlData=D.filter(r=>compScore(r)!==null).map(r=>({
n:r[0],x:dateMap[r[6]]||1,y:compScore(r),c:pColors[r[1]]||'#6366f1',
r:Math.log10((r[19]||100)*1000+1)*4+4
}));
new Chart(document.getElementById('cTimeline'),{
type:'bubble',
data:{datasets:[{
data:tlData.map(x=>({x:x.x,y:x.y,r:x.r})),
backgroundColor:tlData.map(x=>x.c+'88'),borderColor:tlData.map(x=>x.c),borderWidth:1.5
}]},
options:{plugins:{legend:{display:false},tooltip:{callbacks:{label:ctx=>{
const d=tlData[ctx.dataIndex];return[d.n,`Score: ${d.y}`];
}}}},
scales:{
x:{title:{display:true,text:'Release Timeline (months from Jan 2025)',color:tickC,font:{size:8.5,family:'JetBrains Mono'}},min:0,max:16,
grid:{color:gridC},ticks:{color:tickC,font:{family:'JetBrains Mono',size:8.5},callback:v=>['','Jan 25','','','Apr 25','','Jun','Jul','Aug','Sep','Oct','Nov 25','Dec 25','Jan 26','Feb 26','Mar 26'][v]||''}},
y:{title:{display:true,text:'Composite Score',color:tickC,font:{size:8.5,family:'JetBrains Mono'}},min:40,
grid:{color:gridC},ticks:{color:tickC,font:{family:'JetBrains Mono',size:8.5}}}
}
}
});
// 8. OPEN vs CLOSED DISTRIBUTION
const openScores=D.filter(r=>r[3]==='open'&&compScore(r)!==null).map(r=>compScore(r)).sort((a,b)=>a-b);
const closedScores=D.filter(r=>r[3]==='closed'&&compScore(r)!==null).map(r=>compScore(r)).sort((a,b)=>a-b);
const allOpen=D.filter(r=>r[3]==='open'&&compScore(r)!==null).map(r=>({n:r[0],s:compScore(r),c:pColors[r[1]]||'#16a34a'}));
const allClosed=D.filter(r=>r[3]==='closed'&&compScore(r)!==null).map(r=>({n:r[0],s:compScore(r),c:pColors[r[1]]||'#6366f1'}));
new Chart(document.getElementById('cOpenClosed'),{
type:'scatter',
data:{datasets:[
{label:'Open-weight',data:allOpen.map((x,i)=>({x:0.2+Math.random()*.6,y:x.s})),
backgroundColor:allOpen.map(x=>x.c+'cc'),pointRadius:7,borderWidth:1.5,borderColor:allOpen.map(x=>x.c)},
{label:'Closed API',data:allClosed.map((x,i)=>({x:1.2+Math.random()*.6,y:x.s})),
backgroundColor:allClosed.map(x=>x.c+'cc'),pointRadius:8,borderWidth:1.5,borderColor:allClosed.map(x=>x.c)}
]},
options:{plugins:{legend:{labels:{color:tickC,font:{family:'JetBrains Mono',size:9},boxWidth:12}},
tooltip:{callbacks:{label:ctx=>{
const arr=ctx.datasetIndex===0?allOpen:allClosed;return arr[ctx.dataIndex]?`${arr[ctx.dataIndex].n}: ${arr[ctx.dataIndex].s}`:'';
}}}},
scales:{
x:{min:0,max:2,grid:{display:false},ticks:{color:tickC,font:{family:'JetBrains Mono',size:9},callback:v=>v===0.5?'Open-weight':v===1.5?'Closed API':''}},
y:{min:40,max:100,grid:{color:gridC},ticks:{color:tickC,font:{family:'JetBrains Mono',size:9}},title:{display:true,text:'Composite Score',color:tickC,font:{size:8.5,family:'JetBrains Mono'}}}
}
}
});
// 9. BENCHMARK VARIANCE (min/max/mean)
const benchDefs=[
{k:7,l:'MMLU-Pro'},{k:8,l:'GPQA◆'},{k:9,l:'AIME25'},{k:10,l:'HLE'},
{k:11,l:'ARC-AGI-2'},{k:12,l:'Metacog'},{k:13,l:'SWE-Pro'},
{k:14,l:'BFCL'},{k:15,l:'IFEval'},{k:16,l:'LCB'},{k:18,l:'MMMLU'},{k:35,l:'TB2.0'}
];
const varData=benchDefs.map(b=>{
const vals=D.map(r=>r[b.k]).filter(x=>x!==null&&x!==undefined);
if(!vals.length)return null;
const mn=Math.round(Math.min(...vals)*10)/10,mx=Math.round(Math.max(...vals)*10)/10;
const avg=Math.round(vals.reduce((a,v)=>a+v)/vals.length*10)/10;
return{l:b.l,mn,mx,avg,range:mx-mn};
}).filter(Boolean);
new Chart(document.getElementById('cVariance'),{
type:'bar',
data:{
labels:varData.map(x=>x.l),
datasets:[
{label:'Min',data:varData.map(x=>x.mn),backgroundColor:'rgba(225,29,72,.55)',borderColor:'#e11d48',borderWidth:1.2,borderRadius:2},
{label:'Mean',data:varData.map(x=>x.avg),backgroundColor:'rgba(99,102,241,.65)',borderColor:'#6366f1',borderWidth:1.2,borderRadius:2},
{label:'Max',data:varData.map(x=>x.mx),backgroundColor:'rgba(13,148,136,.55)',borderColor:'#0d9488',borderWidth:1.2,borderRadius:2}
]
},
options:{plugins:{legend:{labels:{color:tickC,font:{family:'JetBrains Mono',size:8},boxWidth:10}}},
scales:{y:{min:0,max:100,grid:{color:gridC},ticks:{color:tickC,font:{family:'JetBrains Mono',size:8.5}}},
x:{grid:{display:false},ticks:{color:tickC,font:{family:'JetBrains Mono',size:8},maxRotation:30}}}}
});
// 10. HEATMAP — full width
const heatCols=['MMLU-P','GPQA','AIME25','HLE','ARC-AGI-2','Metacog','SWE-Pro','BFCL','IFEval','LCB','MMMLU','TB2.0'];
const heatKeys=[7,8,9,10,11,12,13,14,15,16,18,35];
const canvas=document.getElementById('cHeat');
const parentCard=canvas.closest('.chart-card');
// Use parent card's full inner width (accounting for padding)
const cardPad=36; // 18px padding × 2
const cW=parentCard ? (parentCard.clientWidth - cardPad) : (window.innerWidth - 80);
const nRows=D.length; // 31 models
const hH=34; // header height
const bH=38; // row height — taller for readability
const mW=130; // model name column width
const totalH=hH+nRows*bH+10;
canvas.width=cW;
canvas.height=totalH;
canvas.style.height=totalH+'px';
const ctx2=canvas.getContext('2d');
ctx2.clearRect(0,0,cW,totalH);
const bW=(cW-mW)/heatCols.length;
// Background alternating rows
D.forEach((r,i)=>{
const y=hH+i*bH;
ctx2.fillStyle=i%2===0?'rgba(248,249,252,0.8)':'rgba(255,255,255,0.6)';
ctx2.fillRect(0,y,cW,bH);
});
// Column headers
heatCols.forEach((h,j)=>{
const x=mW+(j+0.5)*bW;
// Column bg stripe
ctx2.fillStyle=j%2===0?'rgba(99,102,241,.04)':'rgba(99,102,241,.01)';
ctx2.fillRect(mW+j*bW,0,bW,totalH);
// Header text
ctx2.save();
ctx2.translate(x,hH-6);
ctx2.rotate(-Math.PI/6);
ctx2.font='700 9px JetBrains Mono';
ctx2.fillStyle='#6366f1';
ctx2.textAlign='right';
ctx2.fillText(h,0,0);
ctx2.restore();
});
// Vertical grid lines
ctx2.strokeStyle='rgba(226,229,240,0.8)';ctx2.lineWidth=1;
for(let j=0;j<=heatCols.length;j++){
ctx2.beginPath();ctx2.moveTo(mW+j*bW,0);ctx2.lineTo(mW+j*bW,totalH);ctx2.stroke();
}
// Horizontal grid lines
for(let i=0;i<=nRows;i++){
const y=hH+i*bH;
ctx2.strokeStyle='rgba(226,229,240,0.6)';ctx2.lineWidth=0.8;
ctx2.beginPath();ctx2.moveTo(0,y);ctx2.lineTo(cW,y);ctx2.stroke();
}
D.forEach((r,i)=>{
const y=hH+i*bH;
// Provider color bar on left edge
ctx2.fillStyle=pColors[r[1]]||'#6366f1';
ctx2.fillRect(0,y+1,4,bH-2);
// Row number
ctx2.font='600 8px JetBrains Mono';ctx2.fillStyle='#94a3b8';ctx2.textAlign='center';
ctx2.fillText(i+1,14,y+bH/2+3);
// Model name
ctx2.font='600 10px Sora,sans-serif';ctx2.fillStyle='#0f172a';ctx2.textAlign='left';
const nm=r[0].length>17?r[0].substr(0,16)+'…':r[0];
ctx2.fillText(nm,22,y+bH/2+3);
heatKeys.forEach((ki,j)=>{
const v=r[ki];
const cx=mW+j*bW;
if(v!==null&&v!==undefined){
const norm=Math.min(v/100,1);
const alpha=0.07+norm*0.85;
// Cell fill
ctx2.fillStyle=`rgba(99,102,241,${alpha})`;
ctx2.fillRect(cx+1,y+2,bW-2,bH-4);
// Score text
const fontSize=bW>50?10:bW>38?9:8;
ctx2.font=`700 ${fontSize}px JetBrains Mono`;
ctx2.fillStyle=alpha>0.52?'#3730a3':'#475569';
ctx2.textAlign='center';
ctx2.fillText(v,cx+bW/2,y+bH/2+3.5);
} else {
ctx2.fillStyle='rgba(241,245,249,0.9)';
ctx2.fillRect(cx+1,y+2,bW-2,bH-4);
ctx2.font='8px JetBrains Mono';
ctx2.fillStyle='#cbd5e1';ctx2.textAlign='center';
ctx2.fillText('—',cx+bW/2,y+bH/2+3);
}
});
});
}
// ========== INTERACTIVE TOOLS SUB-TABS ==========
function show3DSub(id,el){
document.querySelectorAll('.sub3d').forEach(s=>s.style.display='none');
document.querySelectorAll('#three .tab').forEach(t=>t.classList.remove('on'));
document.getElementById('sub_'+id).style.display='block';
el.classList.add('on');
if(id==='finder')initFinder();
if(id==='h2h')initH2H();
if(id==='coverage')initCoverage();
if(id==='barrace')initBarRace();
}
// ========== 1. MODEL FINDER — Price vs Performance Scatter ==========
let finderInit=false,finderData=[],finderFlt='all';
function initFinder(){
const box=document.getElementById('finderBox');if(!box)return;
const c=document.getElementById('finderCanvas');
const tt=document.getElementById('finderTip');
const W=box.clientWidth,H=box.clientHeight;
c.width=W*2;c.height=H*2;c.style.width=W+'px';c.style.height=H+'px';
const ctx=c.getContext('2d');ctx.scale(2,2);
// Build data
finderData=D.filter(r=>compScore(r)!==null).map(r=>({
n:r[0],prov:r[1],c:pColors[r[1]]||'#6366f1',score:compScore(r),
price:r[28]===null?-1:r[28],type:r[3],free:r[28]===0
}));
drawFinder(ctx,W,H,tt);
c.onmousemove=e=>{
const mx=e.offsetX,my=e.offsetY;let found=null;
finderData.forEach(d=>{
if(finderFlt==='open'&&d.type!=='open')return;
if(finderFlt==='closed'&&d.type!=='closed')return;
if(finderFlt==='cheap'&&(d.price<0||d.price>=1))return;
if(finderFlt==='free'&&!d.free)return;
const px=priceToX(d.price,W),py=scoreToY(d.score,H);
if(Math.abs(px-mx)<10&&Math.abs(py-my)<10)found=d;
});
if(found){tt.style.display='block';tt.style.left=Math.min(mx+12,W-200)+'px';tt.style.top=Math.min(my-50,H-80)+'px';
tt.innerHTML=`<b style="color:${found.c}">${found.n}</b><br>${found.prov} · ${found.type}<br>Score: <b>${found.score}</b><br>Price: <b>${found.price<0?'N/A':found.free?'FREE':'$'+found.price+'/M'}</b>`;
}else tt.style.display='none';
};
finderInit=true;
}
function priceToX(p,W){if(p<0)return 40;if(p===0)return 60;return 60+Math.log10(p+0.01)*((W-100)/2.5)+100;}
function scoreToY(s,H){return H-40-(s-20)*((H-80)/70);}
window.finderFilter=function(f,btn){
finderFlt=f;
document.querySelectorAll('#sub_finder .fb').forEach(b=>b.classList.remove('on'));
btn.classList.add('on');
const box=document.getElementById('finderBox');const c=document.getElementById('finderCanvas');
const ctx=c.getContext('2d');ctx.setTransform(2,0,0,2,0,0);
drawFinder(ctx,box.clientWidth,box.clientHeight,document.getElementById('finderTip'));
};
function drawFinder(ctx,W,H,tt){
ctx.fillStyle='#fafbff';ctx.fillRect(0,0,W,H);
// Grid
ctx.strokeStyle='rgba(99,102,241,0.08)';ctx.lineWidth=0.5;
for(let s=30;s<=90;s+=10){const y=scoreToY(s,H);ctx.beginPath();ctx.moveTo(40,y);ctx.lineTo(W-20,y);ctx.stroke();
ctx.font='500 8px JetBrains Mono';ctx.fillStyle='#94a3b8';ctx.textAlign='right';ctx.fillText(s,36,y+3);}
// Value zone
ctx.fillStyle='rgba(22,163,74,0.04)';ctx.fillRect(40,scoreToY(90,H),priceToX(1,W)-40,scoreToY(60,H)-scoreToY(90,H));
ctx.font='600 9px JetBrains Mono';ctx.fillStyle='rgba(22,163,74,0.3)';ctx.textAlign='left';ctx.fillText('★ VALUE ZONE',46,scoreToY(87,H));
// Axis labels
ctx.font='600 8px JetBrains Mono';ctx.fillStyle='#64748b';ctx.textAlign='center';
ctx.fillText('← FREE Price ($/M input tokens) EXPENSIVE →',W/2,H-8);
ctx.save();ctx.translate(10,H/2);ctx.rotate(-Math.PI/2);ctx.fillText('Composite Score →',0,0);ctx.restore();
// Price ticks
[0,0.1,0.5,1,2,5,10].forEach(p=>{const x=priceToX(p,W);ctx.font='500 7px JetBrains Mono';ctx.fillStyle='#94a3b8';ctx.textAlign='center';ctx.fillText(p===0?'Free':'$'+p,x,H-24);});
// Points
finderData.forEach(d=>{
if(finderFlt==='open'&&d.type!=='open')return;
if(finderFlt==='closed'&&d.type!=='closed')return;
if(finderFlt==='cheap'&&(d.price<0||d.price>=1))return;
if(finderFlt==='free'&&!d.free)return;
const px=priceToX(d.price,W),py=scoreToY(d.score,H);
// Glow
const g=ctx.createRadialGradient(px,py,0,px,py,14);g.addColorStop(0,d.c+'44');g.addColorStop(1,'transparent');ctx.fillStyle=g;ctx.fillRect(px-14,py-14,28,28);
// Dot
ctx.beginPath();ctx.arc(px,py,5,0,Math.PI*2);ctx.fillStyle=d.c+'cc';ctx.fill();ctx.strokeStyle=d.c;ctx.lineWidth=1.2;ctx.stroke();
// Label
ctx.font='600 7.5px Sora';ctx.fillStyle='#475569';ctx.textAlign='center';
ctx.fillText(d.n.length>13?d.n.substr(0,12)+'…':d.n,px,py-9);
});
}
// ========== 2. HEAD-TO-HEAD COMPARISON ==========
let h2hInit=false;
function initH2H(){
if(h2hInit)return;h2hInit=true;
const selA=document.getElementById('h2hA'),selB=document.getElementById('h2hB');
const models=D.filter(r=>compScore(r)!==null).sort((a,b)=>(compScore(b)||0)-(compScore(a)||0));
models.forEach((r,i)=>{
const o1=document.createElement('option');o1.value=i;o1.textContent=r[0];selA.appendChild(o1);
const o2=document.createElement('option');o2.value=i;o2.textContent=r[0];selB.appendChild(o2);
});
selA.value=0;selB.value=Math.min(1,models.length-1);
window._h2hModels=models;
drawH2H();
}
window.drawH2H=function(){
const models=window._h2hModels;if(!models)return;
const box=document.getElementById('h2hBox');const c=document.getElementById('h2hCanvas');
const W=box.clientWidth,H=box.clientHeight;
c.width=W*2;c.height=H*2;c.style.width=W+'px';c.style.height=H+'px';
const ctx=c.getContext('2d');ctx.scale(2,2);
const a=models[document.getElementById('h2hA').value];
const b=models[document.getElementById('h2hB').value];
if(!a||!b)return;
const cA=pColors[a[1]]||'#6366f1',cB=pColors[b[1]]||'#e11d48';
ctx.fillStyle='#fafbff';ctx.fillRect(0,0,W,H);
// Header
ctx.font='700 13px Sora';ctx.fillStyle=cA;ctx.textAlign='right';ctx.fillText(a[0],W/2-20,28);
ctx.fillStyle='#94a3b8';ctx.textAlign='center';ctx.fillText('VS',W/2,28);
ctx.fillStyle=cB;ctx.textAlign='left';ctx.fillText(b[0],W/2+20,28);
const benchmarks=[
{k:7,l:'MMLU-Pro'},{k:8,l:'GPQA Diamond'},{k:9,l:'AIME 2025'},{k:10,l:'HLE'},
{k:11,l:'ARC-AGI-2'},{k:12,l:'Metacognitive'},{k:13,l:'SWE-Pro'},
{k:14,l:'BFCL v4'},{k:15,l:'IFEval'},{k:16,l:'LiveCodeBench'},
{k:18,l:'MMMLU'},{k:35,l:'Terminal-Bench'},{k:36,l:'SciCode'}
];
const bH=28,startY=50,midX=W/2,maxBarW=(W/2-80);
let winsA=0,winsB=0;
benchmarks.forEach((bm,i)=>{
const va=a[bm.k],vb=b[bm.k];
const y=startY+i*(bH+3);
// Label
ctx.font='600 8px JetBrains Mono';ctx.fillStyle='#64748b';ctx.textAlign='center';
ctx.fillText(bm.l,midX,y+bH/2+3);
if(va!==null&&va!==undefined){
const bw=Math.max(2,(va/100)*maxBarW);
const winner=vb!==null&&va>=vb;if(winner)winsA++;
ctx.fillStyle=winner?cA+'cc':cA+'44';
ctx.fillRect(midX-60-bw,y+4,bw,bH-8);
ctx.font='700 9px JetBrains Mono';ctx.fillStyle=winner?cA:'#94a3b8';ctx.textAlign='right';
ctx.fillText(va,midX-60-bw-4,y+bH/2+3);
}
if(vb!==null&&vb!==undefined){
const bw=Math.max(2,(vb/100)*maxBarW);
const winner=va!==null&&vb>va;if(winner)winsB++;
ctx.fillStyle=winner?cB+'cc':cB+'44';
ctx.fillRect(midX+60,y+4,bw,bH-8);
ctx.font='700 9px JetBrains Mono';ctx.fillStyle=winner?cB:'#94a3b8';ctx.textAlign='left';
ctx.fillText(vb,midX+60+bw+4,y+bH/2+3);
}
});
// Win summary
const sumY=startY+benchmarks.length*(bH+3)+10;
ctx.font='800 16px JetBrains Mono';
ctx.fillStyle=cA;ctx.textAlign='right';ctx.fillText(winsA+' wins',midX-20,sumY+8);
ctx.fillStyle=cB;ctx.textAlign='left';ctx.fillText(winsB+' wins',midX+20,sumY+8);
ctx.fillStyle='#94a3b8';ctx.textAlign='center';ctx.fillText(':',midX,sumY+8);
};
// ========== 3. COVERAGE TRUST MAP ==========
let covInit=false;
function initCoverage(){
if(covInit)return;covInit=true;
const box=document.getElementById('covBox');const c=document.getElementById('covCanvas');
const benchNames=['MMLU-P','GPQA','AIME','HLE','ARC-AGI','Metacog','SWE-P','BFCL','IFEval','LCB','MMMLU','TB2.0','SciCode'];
const benchKeys=[7,8,9,10,11,12,13,14,15,16,18,35,36];
const models=D.filter(r=>compScore(r)!==null).sort((a,b)=>(compScore(b)||0)-(compScore(a)||0)).slice(0,25);
const cellW=52,cellH=24,labelW=130,headerH=50;
const W=labelW+benchNames.length*cellW+10,H=headerH+models.length*cellH+10;
c.width=W*2;c.height=H*2;c.style.width=W+'px';c.style.height=H+'px';
const ctx=c.getContext('2d');ctx.scale(2,2);
ctx.fillStyle='#fafbff';ctx.fillRect(0,0,W,H);
// Headers
benchNames.forEach((b,j)=>{
ctx.save();ctx.translate(labelW+j*cellW+cellW/2,headerH-6);ctx.rotate(-Math.PI/5);
ctx.font='700 8px JetBrains Mono';ctx.fillStyle='#6366f1';ctx.textAlign='right';ctx.fillText(b,0,0);ctx.restore();
});
models.forEach((r,i)=>{
const y=headerH+i*cellH;
ctx.fillStyle=i%2===0?'rgba(248,249,252,0.8)':'#fff';ctx.fillRect(0,y,W,cellH);
// Model name
ctx.font='600 9px Sora';ctx.fillStyle='#0f172a';ctx.textAlign='left';
const nm=r[0].length>16?r[0].substr(0,15)+'…':r[0];ctx.fillText(nm,6,y+cellH/2+3);
// Coverage count
let cnt=0;
benchKeys.forEach((k,j)=>{
const v=r[k];const cx=labelW+j*cellW;
if(v!==null&&v!==undefined){
cnt++;
const norm=Math.min(v/100,1);
ctx.fillStyle=`rgba(99,102,241,${0.15+norm*0.6})`;
ctx.fillRect(cx+1,y+1,cellW-2,cellH-2);
ctx.font='700 8px JetBrains Mono';ctx.fillStyle=norm>0.6?'#312e81':'#475569';
ctx.textAlign='center';ctx.fillText(v,cx+cellW/2,y+cellH/2+3);
} else {
ctx.fillStyle='rgba(241,245,249,0.9)';ctx.fillRect(cx+1,y+1,cellW-2,cellH-2);
ctx.font='8px JetBrains Mono';ctx.fillStyle='#cbd5e1';ctx.textAlign='center';ctx.fillText('—',cx+cellW/2,y+cellH/2+3);
}
});
// Coverage bar
const barC=cnt>=10?'#16a34a':cnt>=7?'#0d9488':cnt>=4?'#d97706':'#e11d48';
ctx.fillStyle=barC;ctx.fillRect(labelW-20,y+4,3,cellH-8);
});
// Grid lines
ctx.strokeStyle='rgba(226,229,240,0.6)';ctx.lineWidth=0.5;
for(let j=0;j<=benchNames.length;j++){ctx.beginPath();ctx.moveTo(labelW+j*cellW,headerH);ctx.lineTo(labelW+j*cellW,H);ctx.stroke();}
}
// ========== 4. BAR RACE (improved) ==========
let brInit=false,brPlaying=false,brTime=0;
function initBarRace(){
if(brInit)return;brInit=true;
const box=document.getElementById('barraceBox');const c=document.getElementById('barraceCanvas');
const W=box.clientWidth,H=box.clientHeight;
c.width=W*2;c.height=H*2;c.style.width=W+'px';c.style.height=H+'px';
const ctx=c.getContext('2d');ctx.scale(2,2);
const timeline=[
{t:'2025.01',models:[{n:'DeepSeek R1',s:62,c:'#6366f1'}]},
{t:'2025.04',models:[{n:'DeepSeek R1',s:62,c:'#6366f1'},{n:'Llama 4 Scout',s:35,c:'#0081fb'}]},
{t:'2025.08',models:[{n:'DeepSeek R1',s:62,c:'#6366f1'},{n:'GPT-5',s:68,c:'#10a37f'},{n:'Llama 4',s:35,c:'#0081fb'}]},
{t:'2025.10',models:[{n:'DeepSeek R1',s:62,c:'#6366f1'},{n:'GPT-5',s:68,c:'#10a37f'},{n:'Opus 4.6',s:72,c:'#d97706'},{n:'Kimi K2.5',s:68,c:'#8b5cf6'}]},
{t:'2025.12',models:[{n:'GPT-5',s:68,c:'#10a37f'},{n:'Opus 4.6',s:72,c:'#d97706'},{n:'Kimi K2.5',s:68,c:'#8b5cf6'},{n:'Gem 3 Flash',s:70,c:'#34a853'},{n:'GPT-OSS',s:52,c:'#059669'},{n:'DS V3.2',s:54,c:'#6366f1'}]},
{t:'2026.01',models:[{n:'GPT-5.2',s:74,c:'#10a37f'},{n:'Opus 4.6',s:72,c:'#d97706'},{n:'Gem 3.1 Pro',s:76,c:'#4285f4'},{n:'Kimi K2.5',s:68,c:'#8b5cf6'},{n:'Qwen 397B',s:69,c:'#f97316'},{n:'GLM-5',s:67,c:'#14b8a6'},{n:'Gem 3 Flash',s:70,c:'#34a853'}]},
{t:'2026.03',models:[{n:'GPT-5.4',s:72,c:'#10a37f'},{n:'GPT-5.2',s:74,c:'#10a37f'},{n:'Opus 4.6',s:72,c:'#d97706'},{n:'Gem 3.1 Pro',s:76,c:'#4285f4'},{n:'Kimi K2.5',s:68,c:'#8b5cf6'},{n:'Qwen 397B',s:69,c:'#f97316'},{n:'GLM-5',s:67,c:'#14b8a6'},{n:'Gem 3 Flash',s:70,c:'#34a853'}]}
];
function drawFrame(ti){
ctx.fillStyle='#0f172a';ctx.fillRect(0,0,W,H);
const frame=timeline[Math.min(Math.floor(ti),timeline.length-1)];
const sorted=[...frame.models].sort((a,b)=>b.s-a.s);
const barH=Math.min(38,((H-80)/sorted.length));const maxS=85;
document.getElementById('brYear').textContent=frame.t;
sorted.forEach((m,i)=>{
const y=50+i*(barH+4);const bw=Math.max(5,(m.s/maxS)*(W-160));
const grad=ctx.createLinearGradient(80,y,80+bw,y);grad.addColorStop(0,m.c+'cc');grad.addColorStop(1,m.c+'66');
ctx.fillStyle=grad;
ctx.beginPath();ctx.roundRect(80,y,bw,barH-2,4);ctx.fill();
ctx.font='700 '+Math.min(11,barH*0.42)+'px Sora';ctx.fillStyle='#e2e8f0';ctx.textAlign='right';ctx.fillText(m.n,75,y+barH/2+4);
ctx.font='700 '+Math.min(13,barH*0.48)+'px JetBrains Mono';ctx.fillStyle=m.c;ctx.textAlign='left';ctx.fillText(m.s,80+bw+8,y+barH/2+4);
});
}
drawFrame(0);
window.startBarRace=function(){
if(brPlaying)return;brPlaying=true;brTime=0;
function anim(){brTime+=0.03;drawFrame(brTime);if(brTime<timeline.length-0.5)requestAnimationFrame(anim);else brPlaying=false;}
anim();
};
}
// ========== v2.1: REPORT GENERATOR ==========
let reportInit=false;
function generateReport(){
if(reportInit)return;reportInit=true;
const sorted=[...D].map(r=>({r,s:compScore(r),n:compCoverage(r)})).filter(x=>x.s!==null).sort((a,b)=>b.s-a.s);
const top=sorted[0],top3=sorted.slice(0,3),top10=sorted.slice(0,10);
const openModels=sorted.filter(x=>x.r[3]==='open');
const cheapest=sorted.filter(x=>x.r[28]!==null&&x.r[28]>0).sort((a,b)=>a.r[28]-b.r[28]);
document.getElementById('rptDate').textContent='March 2026 · v2.1';
document.getElementById('rptVerified').textContent=VERIFIED_DATE;
const bestValue=cheapest.length?cheapest[0]:null;
const bestOpen=openModels.length?openModels[0]:null;
// Executive Summary
document.getElementById('rptSummary').innerHTML=`
<div style="display:flex;gap:12px;margin-bottom:14px;flex-wrap:wrap">
<div style="flex:1;min-width:130px;background:linear-gradient(135deg,rgba(99,102,241,.08),transparent);border-radius:12px;padding:14px;text-align:center">
<div style="font-size:28px;font-weight:900;color:#6366f1;letter-spacing:-1px">${top.s}</div>
<div style="font-size:8px;color:var(--text-muted);font-family:var(--font-mono);text-transform:uppercase;letter-spacing:1px">Highest Score</div>
<div style="font-size:12px;font-weight:700;color:var(--text);margin-top:2px">${top.r[0]}</div>
</div>
<div style="flex:1;min-width:130px;background:linear-gradient(135deg,rgba(22,163,74,.08),transparent);border-radius:12px;padding:14px;text-align:center">
<div style="font-size:28px;font-weight:900;color:#16a34a;letter-spacing:-1px">${D.length}</div>
<div style="font-size:8px;color:var(--text-muted);font-family:var(--font-mono);text-transform:uppercase;letter-spacing:1px">LLMs Tracked</div>
<div style="font-size:12px;font-weight:700;color:var(--text);margin-top:2px">6 Modalities</div>
</div>
<div style="flex:1;min-width:130px;background:linear-gradient(135deg,rgba(217,119,6,.08),transparent);border-radius:12px;padding:14px;text-align:center">
<div style="font-size:28px;font-weight:900;color:#d97706;letter-spacing:-1px">$${bestValue?bestValue.r[28]:'—'}</div>
<div style="font-size:8px;color:var(--text-muted);font-family:var(--font-mono);text-transform:uppercase;letter-spacing:1px">Best Value $/M In</div>
<div style="font-size:12px;font-weight:700;color:var(--text);margin-top:2px">${bestValue?bestValue.r[0]:'—'}</div>
</div>
<div style="flex:1;min-width:130px;background:linear-gradient(135deg,rgba(225,29,72,.08),transparent);border-radius:12px;padding:14px;text-align:center">
<div style="font-size:28px;font-weight:900;color:#e11d48;letter-spacing:-1px">${bestOpen?bestOpen.s:'—'}</div>
<div style="font-size:8px;color:var(--text-muted);font-family:var(--font-mono);text-transform:uppercase;letter-spacing:1px">Best Open-Source</div>
<div style="font-size:12px;font-weight:700;color:var(--text);margin-top:2px">${bestOpen?bestOpen.r[0]:'—'}</div>
</div>
</div>
<div style="background:var(--surface-alt);border-radius:8px;padding:10px;font-size:10px;line-height:1.8">
<b>🏆 Top 3:</b> ${top3.map((x,i)=>['🥇','🥈','🥉'][i]+' '+x.r[0]+' <span style="font-family:var(--font-mono);font-weight:800;color:var(--ac)">'+x.s+'</span>').join(' · ')}<br>
<b>📊 Coverage:</b> ${sorted.filter(x=>x.n>=7).length} models with Full data (7+ benchmarks) · <b>Formula:</b> Avg(confirmed) × √(N/10)
</div>`;
// Category Winners
const findBest=(filterFn,sortFn)=>{const f=sorted.filter(filterFn);f.sort(sortFn);return f[0]||null;};
const avgKeys=(x,ks)=>{const vs=ks.map(k=>x.r[k]).filter(v=>v!==null);return vs.length?vs.reduce((a,b)=>a+b)/vs.length:0;};
const cats=[
{l:'🧠 Reasoning',desc:'GPQA+AIME+HLE',w:findBest(()=>true,(a,b)=>avgKeys(b,[8,9,10])-avgKeys(a,[8,9,10])),v:x=>Math.round(avgKeys(x,[8,9,10])*10)/10},
{l:'🧩 Abstract',desc:'ARC-AGI-2',w:findBest(x=>x.r[11]!==null,(a,b)=>(b.r[11]||0)-(a.r[11]||0)),v:x=>x.r[11]+'%'},
{l:'🧬 Metacognition',desc:'FINAL Bench',w:findBest(x=>x.r[12]!==null,(a,b)=>(b.r[12]||0)-(a.r[12]||0)),v:x=>x.r[12]},
{l:'💻 Coding',desc:'SWE-Pro+LCB',w:findBest(()=>true,(a,b)=>avgKeys(b,[13,16])-avgKeys(a,[13,16])),v:x=>Math.round(avgKeys(x,[13,16])*10)/10},
{l:'📋 Instruction',desc:'IFEval',w:findBest(x=>x.r[15]!==null,(a,b)=>(b.r[15]||0)-(a.r[15]||0)),v:x=>x.r[15]},
{l:'💰 Best Value',desc:'Score÷Price',w:findBest(x=>x.r[28]>0,(a,b)=>(b.s/(b.r[28]||999))-(a.s/(a.r[28]||999))),v:x=>'$'+x.r[28]+'/M'},
{l:'🔓 Open-Source',desc:'Top open-weight',w:bestOpen,v:x=>x.s},
{l:'🇰🇷 Korean AI',desc:'Sovereign AI',w:findBest(x=>x.r[4]==='korean',(a,b)=>b.s-a.s),v:x=>x.s},
];
document.getElementById('rptWinners').innerHTML=cats.map(c=>{
if(!c.w)return'';
const pc=pColors[c.w.r[1]]||'#6366f1';
return`<div style="background:var(--surface-alt);border-radius:10px;padding:12px;border-left:3px solid ${pc}">
<div style="font-size:8px;color:var(--text-muted);font-family:var(--font-mono);letter-spacing:0.5px">${c.l}</div>
<div style="font-size:13px;font-weight:800;color:${pc};margin:2px 0">${c.w.r[0]}</div>
<div style="font-size:9px;color:var(--text-sec)">${c.desc}: <b style="font-family:var(--font-mono)">${c.v(c.w)}</b></div>
</div>`;
}).join('');
// Top 10 Table
document.getElementById('rptTbody').innerHTML=top10.map((x,i)=>{
const pc=pColors[x.r[1]]||'#6366f1';
const price=x.r[28]===null?'—':x.r[28]===0?'Free':'$'+x.r[28];
const covPct=Math.round(x.n/10*100);
const covC=covPct>=70?'#16a34a':covPct>=40?'#d97706':'#e11d48';
return`<tr style="border-bottom:1px solid var(--border)">
<td style="padding:6px 4px;font-weight:900;font-family:var(--font-mono);color:${i<3?pc:'var(--text-muted)'};font-size:${i<3?'13px':'10px'}">${i+1}</td>
<td style="padding:6px 4px"><span style="font-weight:700;color:${pc}">${x.r[0]}</span><br><span style="font-size:8px;color:var(--text-muted)">${x.r[1]} · ${x.r[3]}</span></td>
<td style="padding:6px 4px;text-align:center"><span style="font-family:var(--font-mono);font-weight:900;font-size:14px;color:${pc}">${x.s}</span></td>
<td style="padding:6px 4px;text-align:center"><div style="width:40px;height:4px;background:var(--border);border-radius:2px;margin:0 auto"><div style="width:${covPct}%;height:100%;background:${covC};border-radius:2px"></div></div><span style="font-size:8px;font-family:var(--font-mono);color:${covC}">${x.n}/10</span></td>
<td style="padding:6px 4px;text-align:center;font-size:10px">${x.r[3]==='open'?'🔓':'🔒'}</td>
<td style="padding:6px 4px;text-align:right;font-family:var(--font-mono);font-size:9px;color:var(--text-sec)">${price}</td>
</tr>`;
}).join('');
// Key Insights
const openAvg=openModels.length?Math.round(openModels.reduce((a,x)=>a+x.s,0)/openModels.length*10)/10:0;
const closedM=sorted.filter(x=>x.r[3]==='closed');
const closedAvg=closedM.length?Math.round(closedM.reduce((a,x)=>a+x.s,0)/closedM.length*10)/10:0;
const metacogCount=sorted.filter(x=>x.r[12]!==null).length;
document.getElementById('rptInsights').innerHTML=`
<div style="padding:10px 12px;background:linear-gradient(135deg,rgba(99,102,241,.06),transparent);border-left:3px solid #6366f1;border-radius:0 8px 8px 0;margin-bottom:8px">
<b style="color:#6366f1">1. Open vs Closed gap is narrowing</b><br>
<span style="font-size:10px">Open avg <b>${openAvg}</b> vs Closed avg <b>${closedAvg}</b> (Δ${Math.round((closedAvg-openAvg)*10)/10}). ${bestOpen?bestOpen.r[0]:''} at ${bestOpen?bestOpen.s:''} competes directly with closed flagships.</span>
</div>
<div style="padding:10px 12px;background:linear-gradient(135deg,rgba(13,148,136,.06),transparent);border-left:3px solid #0d9488;border-radius:0 8px 8px 0;margin-bottom:8px">
<b style="color:#0d9488">2. No single model dominates all 5 axes</b><br>
<span style="font-size:10px">${top.r[0]} leads overall, but different models win each axis. Routing strategies outperform single-model deployment for production use cases.</span>
</div>
<div style="padding:10px 12px;background:linear-gradient(135deg,rgba(217,119,6,.06),transparent);border-left:3px solid #d97706;border-radius:0 8px 8px 0;margin-bottom:8px">
<b style="color:#d97706">3. SWE-Verified deprecated → LiveCodeBench</b><br>
<span style="font-size:10px">59.4% tasks defective per OpenAI audit. ALL Bench uses LCB — continuously updated, contamination-resistant. High SWE-V + Low LCB = inflated coding metrics.</span>
</div>
<div style="padding:10px 12px;background:linear-gradient(135deg,rgba(225,29,72,.06),transparent);border-left:3px solid #e11d48;border-radius:0 8px 8px 0">
<b style="color:#e11d48">4. Metacognition: the new frontier</b><br>
<span style="font-size:10px">FINAL Bench measures self-correction. Only ${metacogCount} of ${D.length} models tested — a major blind spot. ${sorted.filter(x=>x.r[12]!==null).sort((a,b)=>(b.r[12]||0)-(a.r[12]||0))[0]?.r[0]||'—'} leads.</span>
</div>`;
}
// ========== v2.1: PDF DOWNLOAD ==========
async function downloadPDF(){
const el=document.getElementById('reportContent');
if(!el)return alert('Open Report tab first');
const isDark=document.body.classList.contains('dark');
if(isDark)document.body.classList.remove('dark');
el.style.background='#ffffff';el.style.color='#0f172a';el.style.padding='20px';
try{
const canvas=await html2canvas(el,{scale:2,backgroundColor:'#ffffff',useCORS:true,logging:false});
const{jsPDF}=window.jspdf;
const pdf=new jsPDF('p','mm','a4');
const w=pdf.internal.pageSize.getWidth()-20;
const h=canvas.height*w/canvas.width;
const pageH=pdf.internal.pageSize.getHeight()-20;
let pos=0;
while(pos<h){
if(pos>0)pdf.addPage();
pdf.addImage(canvas.toDataURL('image/png'),'PNG',10,10-pos,w,h);
pos+=pageH;
}
pdf.save('ALL_Bench_Report_2026_March.pdf');
}catch(e){alert('PDF generation error: '+e.message);}
el.style.background='';el.style.color='';el.style.padding='';
if(isDark)document.body.classList.add('dark');
}
// ========== v2.1: DOCX DOWNLOAD (Rich Text) ==========
function downloadDOCX(){
const sorted=[...D].map(r=>({r,s:compScore(r),n:compCoverage(r)})).filter(x=>x.s!==null).sort((a,b)=>b.s-a.s);
const top10=sorted.slice(0,10);
const openM=sorted.filter(x=>x.r[3]==='open');
const openAvg=openM.length?Math.round(openM.reduce((a,x)=>a+x.s,0)/openM.length*10)/10:0;
const closedM=sorted.filter(x=>x.r[3]==='closed');
const closedAvg=closedM.length?Math.round(closedM.reduce((a,x)=>a+x.s,0)/closedM.length*10)/10:0;
// Generate RTF for better formatting
let rtf='{\\rtf1\\ansi\\deff0{\\fonttbl{\\f0 Arial;}{\\f1 Courier New;}}';
rtf+='{\\colortbl;\\red99\\green102\\blue241;\\red22\\green163\\blue74;\\red217\\green119\\blue6;\\red225\\green29\\blue72;}';
rtf+='\\f0\\fs28\\b ALL BENCH INTELLIGENCE REPORT\\b0\\par';
rtf+='\\fs18 March 2026 \\bullet v2.1 \\bullet Last verified: '+VERIFIED_DATE+'\\par\\par';
rtf+='\\fs22\\b\\cf1 EXECUTIVE SUMMARY\\cf0\\b0\\par\\par';
rtf+='\\fs20\\b #1 '+sorted[0].r[0]+' \\f1 (Score: '+sorted[0].s+')\\f0\\b0\\par';
rtf+='Total: '+D.length+' LLMs tracked across 6 modalities (LLM, VLM, Agent, Image, Video, Music)\\par';
rtf+='Open avg: '+openAvg+' vs Closed avg: '+closedAvg+'\\par\\par';
rtf+='\\fs22\\b\\cf1 TOP 10 LLM RANKING\\cf0\\b0\\par\\par';
// Table
rtf+='\\trowd\\trgaph70\\trleft0';
rtf+='\\cellx500\\cellx3500\\cellx4500\\cellx5500\\cellx6500\\cellx7800';
rtf+='\\intbl\\b # \\cell Model \\cell Score \\cell Cov \\cell Type \\cell Price\\b0 \\cell\\row';
top10.forEach((x,i)=>{
rtf+='\\trowd\\trgaph70\\trleft0\\cellx500\\cellx3500\\cellx4500\\cellx5500\\cellx6500\\cellx7800';
const price=x.r[28]===null?'-':x.r[28]===0?'Free':'$'+x.r[28];
rtf+='\\intbl '+(i+1)+'\\cell '+x.r[0]+' ('+x.r[1]+')\\cell \\f1 '+x.s+'\\f0\\cell '+x.n+'/10\\cell '+x.r[3]+'\\cell '+price+'\\cell\\row';
});
rtf+='\\par\\par';
rtf+='\\fs22\\b\\cf1 KEY INSIGHTS\\cf0\\b0\\par\\par';
rtf+='\\fi-200\\li400 1. Open vs Closed gap: \\u916? '+Math.round((closedAvg-openAvg)*10)/10+' points. '+(openM[0]?openM[0].r[0]:'')+' leads open at '+(openM[0]?openM[0].s:'')+'\\par';
rtf+='2. No single model dominates all 5 axes. Routing strategies recommended.\\par';
rtf+='3. SWE-Verified deprecated (59.4% defective). LiveCodeBench replaces it.\\par';
rtf+='4. Metacognition (FINAL Bench): Only '+sorted.filter(x=>x.r[12]!==null).length+' of '+D.length+' models tested.\\par\\par';
rtf+='\\fs22\\b\\cf1 DATA CONFIDENCE\\cf0\\b0\\par\\par';
rtf+='\\cf2\\b\\u10003?\\u10003?\\cf0\\b0 Cross-verified (2+ independent sources)\\par';
rtf+='\\cf3\\b\\u10003?\\cf0\\b0 Single source (provider official)\\par';
rtf+='\\cf4\\b ~\\cf0\\b0 Self-reported / unverified\\par\\par';
rtf+='\\fs16\\i ALL Bench Leaderboard v2.1 | allbench.org\\par';
rtf+='}';
const blob=new Blob([rtf],{type:'application/rtf'});
const a=document.createElement('a');
a.href=URL.createObjectURL(blob);
a.download='ALL_Bench_Report_2026_March.rtf';
a.click();
}
</script>
</body>
</html>