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| <title>ALL Bench Leaderboard 2026 — Complete LLM Comparison</title> |
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|
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|
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| </style> |
| </head> |
| <body> |
| <div class="wrap"> |
| <header> |
| <div class="badge-row"> |
| <div class="badge"><div class="pulse"></div>LIVE · 2026.03.04 · v1.0</div> |
| </div> |
| <h1>ALL Bench Leaderboard 2026</h1> |
| <p class="sub"> |
| <b>31 Models × 25 Metrics</b> — The only leaderboard combining Metacognitive (FINAL Bench) + ARC-AGI-2 + 23 standard benchmarks<br> |
| <span style="color:#0d9488;font-weight:600">✓ Scores verified against Artificial Analysis Intelligence Index (2026.03.04) · AA #1 Gemini 3.1 Pro · AA #2 GPT-5.3 Codex · AA OpenSrc #1 GLM-5</span><br> |
| <span style="font-size:9px">Scale AI SEAL · artificialanalysis.ai · arcprize.org · FINAL-Bench/Metacognitive (HF Official) · Chatbot Arena · aimultiple.com</span> |
| </p> |
| </header> |
|
|
| <div class="stats"> |
| <div class="st"><div class="stn">31</div><div class="stl">Models</div></div> |
| <div class="st"><div class="stn">25</div><div class="stl">Metrics</div></div> |
| <div class="st"><div class="stn">10</div><div class="stl">Providers</div></div> |
| <div class="st"><div class="stn" style="color:#16a34a">17</div><div class="stl">Open Source</div></div> |
| <div class="st"><div class="stn" style="color:#6366f1">+6</div><div class="stl">v1.0 New</div></div> |
| </div> |
|
|
| |
| <div class="tab-bar"> |
| <div class="tab on" onclick="showTab('tbl',this)">📊 Leaderboard</div> |
| <div class="tab" onclick="showTab('charts',this)">📈 Charts</div> |
| <div class="tab" onclick="showTab('info',this)">📎 Benchmark Info</div> |
| </div> |
|
|
| |
| <div id="tbl" class="tpane on"> |
| |
| <div class="vrank-section"> |
| <div class="vrank-header"> |
| <div> |
| <div class="vrank-title">🏆 ALL Bench Composite Score Ranking</div> |
| <div class="vrank-desc">Average of all available benchmarks · MMLU-Pro · GPQA · AIME · HLE · ARC-AGI-2 · Metacog · SWE-Pro · BFCL · IFEval · SWE-V · Colored by provider</div> |
| </div> |
| </div> |
| <div class="vrank-chart-wrap"> |
| <canvas id="cVertRank" style="min-width:900px;height:200px;"></canvas> |
| </div> |
| <div class="vrank-legend" id="vrankLegend"></div> |
| </div> |
|
|
| <div class="toolbar" style="margin-top:12px"> |
| <div class="search-wrap"> |
| <input type="text" id="searchBox" placeholder="Search models..." oninput="doSearch(this.value)"> |
| </div> |
| <span class="flbl">Filter:</span> |
| <button class="fb on" onclick="flt('all',this)">All 21</button> |
| <button class="fb" onclick="flt('open',this)">🔓 Open</button> |
| <button class="fb" onclick="flt('closed',this)">🔒 Closed</button> |
| <button class="fb" onclick="flt('qwen',this)">🟠 Qwen3.5</button> |
| <button class="fb" onclick="flt('gptoss',this)">⬛ GPT-OSS</button> |
| <button class="fb" onclick="flt('reasoning',this)">🧠 Reasoning</button> |
| <button class="fb" onclick="flt('moe',this)">⚡ MoE</button> |
| <button class="fb" onclick="flt('vision',this)">👁 Vision</button> |
| <button class="fb" onclick="flt('value',this)">💚 Value</button> |
| <button class="fb" onclick="flt('flagship',this)">👑 Flagship</button> |
| <button class="fb" onclick="flt('korean',this)" style="background:linear-gradient(135deg,#c9002b,#003478);color:#fff;border-color:#c9002b;font-weight:700">🇰🇷 소버린 AI</button> |
| <div class="col-toggle-wrap" style="margin-left:auto"> |
| <button class="col-toggle-btn" onclick="toggleColMenu()" id="colBtn">⚙ Columns ▾</button> |
| <div class="col-dropdown" id="colMenu"></div> |
| </div> |
| </div> |
| <div class="tw"> |
| <table id="T"> |
| <thead> |
| <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="Average of available 100-pt benchmarks: MMLU-Pro·GPQA·AIME·HLE·ARC-AGI-2·Metacog·SWE-Pro·BFCL·IFEval·SWE-V" 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 & 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 & 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 & 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(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> |
| </thead> |
| <tbody id="TB"></tbody> |
| </table> |
| </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<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 (9 models measured)</span> |
| </div> |
| </div> |
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| <div id="charts" class="tpane"> |
|
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| <div class="charts-grid" style="margin-top:14px"> |
|
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| <div class="chart-card"> |
| <h3>🧩 ARC-AGI-2 — Abstract Reasoning Frontier</h3> |
| <p>Official arcprize.org · Vertical bars by score · Contamination-proof visual reasoning benchmark</p> |
| <canvas id="cArc" height="220"></canvas> |
| <div class="chart-insight"><b>Key:</b> Gemini 3.1 Pro dominates at <b>88.1%</b> (Feb 2026 leaderboard #1). GPT-5.2 52.9% · Claude Opus 4.6 ~37.6%. Kimi K2.5 surprisingly low at 12.1% despite HLE dominance — distinct capability axis.</div> |
| </div> |
|
|
| <div class="chart-card"> |
| <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> |
| </div> |
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| </div> |
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| |
| <div class="charts-grid"> |
|
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| <div class="chart-card"> |
| <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> |
| <canvas id="cRadar" height="260"></canvas> |
| <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> |
| </div> |
|
|
| <div class="chart-card"> |
| <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> |
| <canvas id="cDomain" height="260"></canvas> |
| <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> |
| </div> |
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| </div> |
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| <div class="charts-grid"> |
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| <div class="chart-card"> |
| <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> |
| <canvas id="cScatter" height="260"></canvas> |
| <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> |
| </div> |
|
|
| <div class="chart-card"> |
| <h3>🏭 Provider Strength — Average Score by Company</h3> |
| <p>Average composite score across all models per provider · Shows lab-level consistency</p> |
| <canvas id="cProvider" height="260"></canvas> |
| <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> |
| </div> |
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| </div> |
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| <div class="charts-grid"> |
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| <div class="chart-card"> |
| <h3>📅 Intelligence Timeline — Score vs Release Date</h3> |
| <p>Bubble size = context window (log scale) · Color = provider · Rapid capability gains 2025→2026</p> |
| <canvas id="cTimeline" height="260"></canvas> |
| <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> |
| </div> |
|
|
| <div class="chart-card"> |
| <h3>⚖ Open vs Closed — Distribution Comparison</h3> |
| <p>Score distribution: Open-weight (15 models) vs Closed-API (6 models) · Box plot style with individual points</p> |
| <canvas id="cOpenClosed" height="260"></canvas> |
| <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> |
| </div> |
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| </div> |
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| <div class="charts-grid"> |
|
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| <div class="chart-card"> |
| <h3>📐 Benchmark Score Variance — Consistency Analysis</h3> |
| <p>For each benchmark: show min/max/mean across all models · Reveals benchmark difficulty & discrimination power</p> |
| <canvas id="cVariance" height="220"></canvas> |
| <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> |
| </div> |
|
|
| <div class="chart-card full"> |
| <h3>🌡 Full Benchmark Heatmap — 31 Models × 11 Benchmarks</h3> |
| <p>Color intensity = score · White/light = unreported · Indigo = high · Reveals capability patterns across the entire landscape</p> |
| <canvas id="cHeat" style="width:100%;display:block;"></canvas> |
| </div> |
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| </div> |
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| </div> |
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| <div id="info" class="tpane"> |
| <div class="info-grid" style="margin-top:12px"> |
| <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 88.1% (Feb 2026 leaderboard 1st) · GPT-5.2 52.9% · Claude Opus 4.6 ~37.6% · 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>🇰🇷 한국 소버린 AI — 독자 AI 파운데이션 모델 (독파모) 현황</b><p> |
| 과기정통부 주관 '독자 AI 파운데이션 모델 프로젝트' 2026.02 기준 4개 정예팀: <b>LG AI연구원(K-EXAONE)</b> · <b>SK텔레콤(A.X K1)</b> · <b>업스테이지(Solar Open 100B)</b> · <b>모티프테크놀로지스</b>.<br> |
| • 1차 평가(2026.01.15): 5팀 → 3팀 (네이버클라우드 독자성 미달, NC AI 점수 미달 탈락)<br> |
| • 패자부활전(2026.02.20): 모티프테크놀로지스 추가 선정 → 4팀 체제<br> |
| • K-EXAONE: 1차 평가 1위 · 13개 벤치마크 평균 72점 · AA 오픈웨이트 톱10 · 236B MoE<br> |
| • Solar Open 100B: AIME 84.3% · 19.7T 토큰 · 100B MoE · arXiv 2601.07022<br> |
| • A.X K1: 국내 최초 500B 파라미터 · Apache 2.0 오픈소스<br> |
| • 목표: 글로벌 AI 모델 95% 이상 성능 확보 · 2027년 최종 2팀 선정 · 5,300억원 예산 |
| </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 · Gemini 59.5 · Opus 4.6 56.04 (rank 9). ER (error recovery) accounts for 94.8% of self-correction gains. 14 models = not evaluated (—).</p></div> |
| <div class="fni"><b>📊 Composite Score — 공정 가중 평균</b><p>10개 벤치마크(MMLU-Pro · GPQA · AIME · HLE · ARC-AGI-2 · Metacog · SWE-Pro · BFCL · IFEval · SWE-V) 총합÷10. <b style="color:#e11d48">미제출(null)=0점 패널티</b> → 벤치마크 회피 시 불이익. 막대 상단 n/10 = 실제 커버리지 표시. 이전 버전의 "null 제외 평균" 방식은 Grok 4.1 Fast(2개만 제출→86점), DeepSeek R2(4개→88점) 같은 허위 1위 오류를 유발함.</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"><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. <2s recommended for real-time apps.</p></div> |
| <div class="fni"><b>💰 Pricing</b><p>Input cost in $/million tokens. 0 = free open-weights. Qwen3.5-35B $0.10/M, DeepSeek V3.2 $0.14/M offer extreme value vs closed models. GPT-5.2 $1.75/M · Claude Opus 4.6 $5/M.</p></div> |
| </div> |
| <p class="upd">ALL Bench Leaderboard v1.0 · Updated March 4, 2026 · Scale AI SEAL · artificialanalysis.ai · arcprize.org · FINAL-Bench/Metacognitive (HF) · Chatbot Arena</p> |
| </div> |
|
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| </div> |
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| <script> |
| |
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| |
| |
| |
| |
| |
| const D=[ |
| ["GPT-5.2","OpenAI","#10a37f","closed","flagship","#10a37f","2026.02", |
| 80.0,93.2,100,35.4,52.9,62.76,29.9,null,90.5,null,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"], |
| |
| ["GPT-5.3 Codex","OpenAI","#10a37f","closed","flagship","#047857","2026.02", |
| 82.9,91.5,95.0,36.0,null,null,78.2,null,null,null,null,83, |
| 400,128,null,null,["Image"],"Dense","Reasoning(Coding)",1500,"Prop",7.50,30.0, |
| 4,"AA 전체 2위(54) · 코딩 전문 frontier",4,"Terminal-Bench 77.3% · SWE 78.2% · coding SOTA", |
| "https://openai.com/codex"], |
| |
| ["Claude Opus 4.6","Anthropic","#d97706","closed","flagship","#d97706","2025.10", |
| 78.5,91.3,100,36.7,37.6,56.04,45.0,null,93.1,null,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"], |
| |
| ["Gemini 3.1 Pro","Google","#4285f4","closed","flagship","#4285f4","2026.01", |
| 83.0,94.3,97,44.7,88.1,59.5,43.3,null,91.0,null,74.2,88, |
| 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"], |
| |
| ["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"], |
| |
| ["Claude Sonnet 4.6","Anthropic","#d97706","closed","flagship","#f59e0b","2025.10", |
| 76.0,84.5,93,22.0,null,null,43.6,null,92.0,null,72.7,79, |
| 200,64,null,2.0,["Image","Video"],"Dense","Reasoning",1482,"Prop",3.0,15.0, |
| 4,"Mid-tier best pick",4,"Most-used · balanced", |
| "https://anthropic.com/claude"], |
| |
| ["GPT-OSS-120B","OpenAI","#059669","open","gptoss","#059669","2025.12", |
| 90.0,80.9,97.9,12.0,null,null,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"], |
| |
| ["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"], |
| |
| ["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"], |
| |
| ["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"], |
| |
| ["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"], |
| |
| ["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"], |
| |
| ["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"], |
| |
| ["Qwen3.5-9B","Alibaba","#f97316","open","qwen","#c2410c","2026.01", |
| null,81.7,null,10.0,null,null,null,null,null,null,null,73, |
| 262,32,null,3.5,["Image","Video"],"Dense","Reasoning(9B)",1300,"Apache2",null,null, |
| 5,"9B beats 120B",5,"GPQA 81.7>80.9 · RTX 3060", |
| "https://huggingface.co/Qwen"], |
| |
| ["Qwen3.5-4B","Alibaba","#f97316","open","qwen","#9a3412","2026.01", |
| null,null,null,null,null,null,null,null,null,null,null,65, |
| 262,32,null,2.0,["Image","Video"],"Dense","Reasoning(4B)",null,"Apache2",null,null, |
| 5,"First 4B multimodal",4,"Video & minimal agent", |
| "https://huggingface.co/Qwen"], |
| |
| ["DeepSeek V3.2","DeepSeek","#6366f1","open","value","#6366f1","2025.12", |
| null,79.9,89.3,22.0,null,null,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"], |
| |
| ["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"], |
| |
| ["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,null,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"], |
| |
| ["GLM-5","Zhipu AI","#14b8a6","open","value","#0d9488","2026.02", |
| 87.0,86.0,92.7,30.5,null,null,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 오픈소스 1위(50) · ELO 1451 최고",5,"SWE 77.8% · Huawei Ascend only · 2026.02.11", |
| "https://huggingface.co/zai-org/GLM-5"], |
| |
| ["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"], |
| |
| ["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"], |
| |
| ["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/"], |
| |
| ["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"], |
| |
| ["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"], |
| |
| ["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"], |
| |
| ["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"], |
| |
| ["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"], |
| |
| |
| ["K-EXAONE","LG AI연구원","#a50034","open","korean","#c9002b","2025.12", |
| 83.0,77.0,87.0,null,null,null,null,null,null,null,null,72, |
| 260,16,null,null,["Image","Text"],"MoE","Reasoning(236B, K-독파모)",null,"Research",0,0, |
| 5,"독파모 1차 1위 · AA 오픈웨이트 톱10",5,"LG 236B · GPT-OSS 120B 대비 103% · 소버린AI", |
| "https://huggingface.co/LGAI-EXAONE"], |
| |
| ["A.X K1","SK텔레콤","#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-독파모)",null,"Apache2",0,0, |
| 4,"국내 최대 500B · 한국어·산업 특화",4,"SKT 국내 첫 5천억 파라미터 · 소버린AI", |
| "https://www.sktelecom.com"], |
| |
| ["Solar Open 100B","업스테이지","#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-독파모)",null,"Apache2",0,0, |
| 5,"100B · AIME 84.3 · 19.7T 학습",5,"업스테이지 · 수학·코딩 특화 · 소버린AI", |
| "https://huggingface.co/upstage/Solar-Open-100B"], |
| |
| ["모티프 AI","모티프테크놀로지스","#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-독파모)",null,"Prop",0,0, |
| 3,"패자부활전 선정 2026.02.20",3,"독파모 4번째 팀 · 벤치마크 공개 예정", |
| "https://motif.ai"] |
| ]; |
| |
| |
| function compScore(r){ |
| const keys=[r[7],r[8],r[9],r[10],r[11],r[12],r[13],r[14],r[15],r[17]]; |
| const nonNull=keys.filter(x=>x!==null&&x!==undefined); |
| if(!nonNull.length)return null; |
| |
| const sum=keys.reduce((a,b)=>a+(b||0),0); |
| return Math.round(sum/10*10)/10; |
| } |
| |
| |
| 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"; |
| } |
| |
| |
| 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:${c}"></div></div></div>`; |
| } |
| |
| |
| function metaCell(v){ |
| if(v===null||v===undefined)return`<span class="na">—</span>`; |
| const c=v>=65?"#6366f1":v>=55?"#0d9488":v>=45?"#d97706":"#e11d48"; |
| const lbl=v>=65?"S":v>=55?"A":v>=45?"B":"C"; |
| return`<div class="sc meta-col"><span class="sn" style="color:${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>`; |
| } |
| |
| |
| 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>`; |
| } |
| |
| |
| 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>`; |
| } |
| |
| |
| 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)">${d}</span></div>`; |
| } |
| |
| |
| function visCell(vis){ |
| if(!vis||vis.length===0)return`<span class="na">Text</span>`; |
| if(vis.includes("Text")&&vis.length===1)return`<span class="na">Text</span>`; |
| return`<div class="vis">${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>`; |
| } |
| |
| |
| 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>`; |
| } |
| |
| |
| 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>`; |
| } |
| |
| |
| function compCell(r){ |
| const cs=compScore(r); |
| if(cs===null)return`<span class="na">—</span>`; |
| const c=gc(cs,100); |
| return`<div class="comp"><span class="compN" style="color:${c}">${cs}</span><div class="compB"><div style="width:${Math.min(cs,100)}%;height:100%;background:${c};border-radius:2px;transition:width .9s"></div></div></div>`; |
| } |
| |
| |
| 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">${scoreCell(r[7],100)}</td> |
| <td data-col="5">${scoreCell(r[8],100)}</td> |
| <td data-col="6">${scoreCell(r[9],100)}</td> |
| <td data-col="7">${scoreCell(r[10],100)}</td> |
| <td data-col="8" class="arc-col">${arcCell(r[11])}</td> |
| <td data-col="9" class="meta-col">${metaCell(r[12])}</td> |
| <td data-col="10">${scoreCell(r[13],100)}</td> |
| <td data-col="11">${scoreCell(r[14],100)}</td> |
| <td data-col="12">${scoreCell(r[15],100)}</td> |
| <td data-col="13">${scoreCell(r[16],100)}</td> |
| <td data-col="14" style="opacity:.75">${scoreCell(r[17],100)}</td> |
| <td data-col="15">${scoreCell(r[18],100)}</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); |
| |
| |
| 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; |
| 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(); |
| } |
| |
| |
| (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); |
| } |
| |
| |
| 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'); |
| }); |
| } |
| |
| |
| 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",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)"> ${label}`; |
| menu.appendChild(d); |
| }); |
| } |
| buildColMenu(); |
| |
| function toggleColMenu(){ |
| const m=document.getElementById('colMenu'); |
| m.classList.toggle('open'); |
| } |
| document.addEventListener('click',e=>{ |
| if(!e.target.closest('.col-toggle-wrap'))document.getElementById('colMenu').classList.remove('open'); |
| }); |
| |
| function toggleCol(ci,show){ |
| const sel=`[data-col="${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)); |
| } |
| |
| |
| 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(); |
| } |
| let chartsInit=false; |
| |
| |
| window.addEventListener('load',()=>{initVertRank();}); |
| |
| |
| 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; |
| |
| |
| [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; |
| |
| const benchKeys=[7,8,9,10,11,12,13,14,15,17]; |
| const origR=D.find(r=>r[0]===d.n); |
| const covCnt=origR?benchKeys.filter(k=>origR[k]!==null&&origR[k]!==undefined).length:0; |
| |
| |
| 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(); |
| |
| |
| 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); |
| |
| ctx.font='500 7px JetBrains Mono';ctx.fillStyle='#94a3b8';ctx.textAlign='center'; |
| ctx.fillText(covCnt+'/10',x+barW/2,y-3); |
| |
| |
| 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); |
| |
| |
| 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(); |
| }); |
| |
| |
| 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(''); |
| } |
| } |
| |
| |
| 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 AI연구원":"#c9002b","SK텔레콤":"#e8002d","업스테이지":"#005baa","모티프테크놀로지스":"#2d6be4" |
| }; |
| const gridC='rgba(15,23,42,.06)'; |
| const tickC='#94a3b8'; |
| |
| function initCharts(){ |
| chartsInit=true; |
| |
| |
| 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}% — ${arcData[c.dataIndex].n}`}}}, |
| scales:{y:{min:0,max:100,grid:{color:gridC},ticks:{color:tickC,font:{family:'JetBrains Mono',size:9}}}, |
| x:{grid:{display:false},ticks:{color:tickC,font:{family:'JetBrains Mono',size:8},maxRotation:35}}}} |
| }); |
| |
| |
| const metaFull=[ |
| {n:"Kimi K2.5",prov:"Moonshot",base:68.71,meta:78.54}, |
| {n:"GPT-5.2",prov:"OpenAI",base:62.76,meta:75.5}, |
| {n:"GLM-5",prov:"Zhipu AI",base:62.50,meta:75.0}, |
| {n:"Gemini 3.1 Pro",prov:"Google",base:59.5,meta:77.08}, |
| {n:"Claude Opus 4.6",prov:"Anthropic",base:56.04,meta:76.17}, |
| ].sort((a,b)=>b.base-a.base); |
| new Chart(document.getElementById('cMetaDelta'),{ |
| type:'bar', |
| data:{ |
| labels:metaFull.map(x=>x.n.length>12?x.n.substr(0,11)+'…':x.n), |
| datasets:[ |
| {label:'Baseline Score',data:metaFull.map(x=>x.base),backgroundColor:metaFull.map(x=>pColors[x.prov]+'88'),borderColor:metaFull.map(x=>pColors[x.prov]),borderWidth:1.5,borderRadius:4}, |
| {label:'MetaCog (self-corrected)',data:metaFull.map(x=>x.meta),backgroundColor:metaFull.map(x=>pColors[x.prov]+'33'),borderColor:metaFull.map(x=>pColors[x.prov]),borderWidth:2,borderRadius:4,borderDash:[4,2]} |
| ] |
| }, |
| options:{ |
| plugins:{legend:{labels:{color:tickC,font:{family:'JetBrains Mono',size:8},boxWidth:10}}, |
| tooltip:{callbacks:{afterBody:items=>{ |
| if(items[0]){const i=items[0].dataIndex;return[` Δ gain: +${(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}}}} |
| } |
| }); |
| |
| |
| 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}}} |
| }); |
| |
| |
| 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}}}} |
| }); |
| |
| |
| 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}}} |
| } |
| } |
| }); |
| |
| |
| 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}}}} |
| }); |
| |
| |
| const dateMap={"2025.01":1,"2025.04":4,"2025.11":11,"2025.12":12,"2026.01":13,"2026.02":14}; |
| 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: ${d.y}`]; |
| }}}}, |
| scales:{ |
| x:{title:{display:true,text:'Release Timeline (months from Jan 2025)',color:tickC,font:{size:8.5,family:'JetBrains Mono'}},min:0,max:15, |
| grid:{color:gridC},ticks:{color:tickC,font:{family:'JetBrains Mono',size:8.5},callback:v=>['','Jan 25','','','Apr 25','','','','','','','Nov 25','Dec 25','Jan 26','Feb 26'][v]||''}}, |
| y:{title:{display:true,text:'Composite Score',color:tickC,font:{size:8.5,family:'JetBrains Mono'}},min:40, |
| grid:{color:gridC},ticks:{color:tickC,font:{family:'JetBrains Mono',size:8.5}}} |
| } |
| } |
| }); |
| |
| |
| const openScores=D.filter(r=>r[3]==='open'&&compScore(r)!==null).map(r=>compScore(r)).sort((a,b)=>a-b); |
| const closedScores=D.filter(r=>r[3]==='closed'&&compScore(r)!==null).map(r=>compScore(r)).sort((a,b)=>a-b); |
| const allOpen=D.filter(r=>r[3]==='open'&&compScore(r)!==null).map(r=>({n:r[0],s:compScore(r),c:pColors[r[1]]||'#16a34a'})); |
| const allClosed=D.filter(r=>r[3]==='closed'&&compScore(r)!==null).map(r=>({n:r[0],s:compScore(r),c:pColors[r[1]]||'#6366f1'})); |
| new Chart(document.getElementById('cOpenClosed'),{ |
| type:'scatter', |
| data:{datasets:[ |
| {label:'Open-weight',data:allOpen.map((x,i)=>({x:0.2+Math.random()*.6,y:x.s})), |
| backgroundColor:allOpen.map(x=>x.c+'cc'),pointRadius:7,borderWidth:1.5,borderColor:allOpen.map(x=>x.c)}, |
| {label:'Closed API',data:allClosed.map((x,i)=>({x:1.2+Math.random()*.6,y:x.s})), |
| backgroundColor:allClosed.map(x=>x.c+'cc'),pointRadius:8,borderWidth:1.5,borderColor:allClosed.map(x=>x.c)} |
| ]}, |
| options:{plugins:{legend:{labels:{color:tickC,font:{family:'JetBrains Mono',size:9},boxWidth:12}}, |
| tooltip:{callbacks:{label:ctx=>{ |
| const arr=ctx.datasetIndex===0?allOpen:allClosed;return arr[ctx.dataIndex]?`${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'}}} |
| } |
| } |
| }); |
| |
| |
| 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:17,l:'SWE-V'},{k:18,l:'MMMLU'} |
| ]; |
| 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}}}} |
| }); |
| |
| |
| const heatCols=['MMLU-P','GPQA','AIME25','HLE','ARC-AGI-2','Metacog','SWE-Pro','BFCL','IFEval','SWE-V','MMMLU']; |
| const heatKeys=[7,8,9,10,11,12,13,14,15,17,18]; |
| const canvas=document.getElementById('cHeat'); |
| const parentCard=canvas.closest('.chart-card'); |
| |
| const cardPad=36; |
| const cW=parentCard ? (parentCard.clientWidth - cardPad) : (window.innerWidth - 80); |
| const nRows=D.length; |
| const hH=34; |
| const bH=38; |
| const mW=130; |
| 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; |
| |
| |
| 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); |
| }); |
| |
| |
| heatCols.forEach((h,j)=>{ |
| const x=mW+(j+0.5)*bW; |
| |
| ctx2.fillStyle=j%2===0?'rgba(99,102,241,.04)':'rgba(99,102,241,.01)'; |
| ctx2.fillRect(mW+j*bW,0,bW,totalH); |
| |
| 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(); |
| }); |
| |
| |
| 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(); |
| } |
| |
| 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; |
| |
| ctx2.fillStyle=pColors[r[1]]||'#6366f1'; |
| ctx2.fillRect(0,y+1,4,bH-2); |
| |
| ctx2.font='600 8px JetBrains Mono';ctx2.fillStyle='#94a3b8';ctx2.textAlign='center'; |
| ctx2.fillText(i+1,14,y+bH/2+3); |
| |
| 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; |
| |
| ctx2.fillStyle=`rgba(99,102,241,${alpha})`; |
| ctx2.fillRect(cx+1,y+2,bW-2,bH-4); |
| |
| 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); |
| } |
| }); |
| }); |
| } |
| </script> |
| </body> |
| </html> |
