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README.md

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----
-license: apache-2.0
----
+---
+annotations_creators:
+  - expert-generated
+language:
+  - en
+license: mit
+multilinguality:
+  - monolingual
+pretty_name: "ALL Bench Leaderboard 2026"
+size_categories:
+  - n<1K
+source_datasets:
+  - original
+tags:
+  - benchmark
+  - leaderboard
+  - llm
+  - vlm
+  - ai-evaluation
+  - gpt-5
+  - claude
+  - gemini
+  - final-bench
+  - metacognition
+  - multimodal
+  - ai-agent
+  - image-generation
+  - video-generation
+  - music-generation
+task_categories:
+  - text-generation
+  - visual-question-answering
+  - text-to-image
+  - text-to-video
+  - text-to-audio
+dataset_info:
+  features:
+    - name: llm
+      dtype: list
+    - name: vlm
+      dtype: dict
+    - name: agent
+      dtype: list
+    - name: image
+      dtype: list
+    - name: video
+      dtype: list
+    - name: music
+      dtype: list
+    - name: confidence
+      dtype: dict
+---
+
+# 🏆 ALL Bench Leaderboard 2026
+
+**The only AI benchmark dataset covering LLM · VLM · Agent · Image · Video · Music in a single unified file.**
+
+<p align="center">
+  <a href="https://huggingface.co/spaces/FINAL-Bench/all-bench-leaderboard"><img src="https://img.shields.io/badge/🏆_Live_Leaderboard-ALL_Bench-6366f1?style=for-the-badge" alt="Live Leaderboard"></a>
+</p>
+
+<p align="center">
+  <a href="https://github.com/final-bench/ALL-Bench-Leaderboard"><img src="https://img.shields.io/badge/GitHub-Repo-black?style=flat-square&logo=github" alt="GitHub"></a>
+  <a href="https://huggingface.co/datasets/FINAL-Bench/Metacognitive"><img src="https://img.shields.io/badge/🧬_FINAL_Bench-Dataset-blueviolet?style=flat-square" alt="FINAL Bench"></a>
+  <a href="https://huggingface.co/spaces/FINAL-Bench/Leaderboard"><img src="https://img.shields.io/badge/🧬_FINAL_Bench-Leaderboard-teal?style=flat-square" alt="FINAL Leaderboard"></a>
+</p>
+
+## Dataset Summary
+
+ALL Bench Leaderboard aggregates and cross-verifies benchmark scores for **91 AI models** across 6 modalities. Every numerical score is tagged with a confidence level (`cross-verified`, `single-source`, or `self-reported`) and its original source. The dataset is designed for researchers, developers, and decision-makers who need a trustworthy, unified view of the AI model landscape.
+
+| Category | Models | Benchmarks | Description |
+|----------|--------|------------|-------------|
+| **LLM** | 42 | 31 fields | MMLU-Pro, GPQA, AIME, HLE, ARC-AGI-2, Metacog, SWE-Pro, IFEval, LCB, etc. |
+| **VLM Flagship** | 11 | 10 fields | MMMU, MMMU-Pro, MathVista, AI2D, OCRBench, MMStar, HallusionBench, etc. |
+| **VLM Lightweight** | 5 | 34 fields | Detailed Qwen-series edge model comparison across 3 sub-categories |
+| **Agent** | 10 | 8 fields | OSWorld, τ²-bench, BrowseComp, Terminal-Bench 2.0, GDPval-AA, SWE-Pro |
+| **Image Gen** | 10 | 7 fields | Photo realism, text rendering, instruction following, style, aesthetics |
+| **Video Gen** | 10 | 7 fields | Quality, motion, consistency, text rendering, duration, resolution |
+| **Music Gen** | 8 | 6 fields | Quality, vocals, instrumental, lyrics, duration |
+
+## Live Leaderboard
+
+👉 **[https://huggingface.co/spaces/FINAL-Bench/all-bench-leaderboard](https://huggingface.co/spaces/FINAL-Bench/all-bench-leaderboard)**
+
+Interactive features: composite ranking, dark mode, advanced search (`GPQA > 90 open`, `price < 1`), Model Finder, Head-to-Head comparison, Trust Map heatmap, Bar Race animation, and downloadable Intelligence Report (PDF/DOCX).
+
+## Data Structure
+
+```
+all_bench_leaderboard_v2.1.json
+├── metadata          # version, formula, links, model counts
+├── llm[42]           # 42 LLMs × 31 fields
+├── vlm
+│   ├── flagship[11]  # 11 flagship VLMs × 10 benchmarks
+│   └── lightweight[5]# 5 edge models × 34 benchmarks (3 sub-tables)
+├── agent[10]         # 10 agent models × 8 benchmarks
+├── image[10]         # 10 image gen models × S/A/B/C ratings
+├── video[10]         # 10 video gen models × S/A/B/C ratings
+├── music[8]          # 8 music gen models × S/A/B/C ratings
+└── confidence{42}    # per-model, per-benchmark source & trust level
+```
+
+## LLM Field Schema
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `name` | string | Model name |
+| `provider` | string | Organization |
+| `type` | string | `open` or `closed` |
+| `group` | string | `flagship`, `open`, `korean`, etc. |
+| `released` | string | Release date (YYYY.MM) |
+| `mmluPro` | float \| null | MMLU-Pro score (%) |
+| `gpqa` | float \| null | GPQA Diamond (%) |
+| `aime` | float \| null | AIME 2025 (%) |
+| `hle` | float \| null | Humanity's Last Exam (%) |
+| `arcAgi2` | float \| null | ARC-AGI-2 (%) |
+| `metacog` | float \| null | FINAL Bench Metacognitive score |
+| `swePro` | float \| null | SWE-bench Pro (%) |
+| `bfcl` | float \| null | Berkeley Function Calling (%) |
+| `ifeval` | float \| null | IFEval instruction following (%) |
+| `lcb` | float \| null | LiveCodeBench (%) |
+| `sweV` | float \| null | SWE-bench Verified (%) — deprecated |
+| `mmmlu` | float \| null | Multilingual MMLU (%) |
+| `termBench` | float \| null | Terminal-Bench 2.0 (%) |
+| `sciCode` | float \| null | SciCode (%) |
+| `priceIn` / `priceOut` | float \| null | USD per 1M tokens |
+| `elo` | int \| null | Arena Elo rating |
+| `license` | string | `Prop`, `Apache2`, `MIT`, `Open`, etc. |
+
+## Composite Score
+
+```
+Score = Avg(confirmed benchmarks) × √(N/10)
+```
+
+10 core benchmarks across the **5-Axis Intelligence Framework**: Knowledge · Expert Reasoning · Abstract Reasoning · Metacognition · Execution.
+
+## Confidence System
+
+Each benchmark score in the `confidence` object is tagged:
+
+| Level | Badge | Meaning |
+|-------|-------|---------|
+| `cross-verified` | ✓✓ | Confirmed by 2+ independent sources |
+| `single-source` | ✓ | One official or third-party source |
+| `self-reported` | ~ | Provider's own claim, unverified |
+
+Example:
+```json
+"Claude Opus 4.6": {
+  "gpqa": { "level": "cross-verified", "source": "Anthropic + Vellum + DataCamp" },
+  "arcAgi2": { "level": "cross-verified", "source": "Vellum + llm-stats + NxCode + DataCamp" },
+  "metacog": { "level": "single-source", "source": "FINAL Bench dataset" }
+}
+```
+
+## Usage
+
+```python
+import json
+from huggingface_hub import hf_hub_download
+
+path = hf_hub_download(
+    repo_id="FINAL-Bench/ALL-Bench-Leaderboard",
+    filename="all_bench_leaderboard_v2.1.json",
+    repo_type="dataset"
+)
+data = json.load(open(path))
+
+# Top 5 LLMs by GPQA
+ranked = sorted(data["llm"], key=lambda x: x["gpqa"] or 0, reverse=True)
+for m in ranked[:5]:
+    print(f"{m['name']:25s} GPQA={m['gpqa']}")
+
+# Check confidence for a score
+print(data["confidence"]["Gemini 3.1 Pro"]["gpqa"])
+# → {"level": "single-source", "source": "Google DeepMind model card"}
+```
+
+## FINAL Bench — Metacognitive Benchmark
+
+FINAL Bench measures AI self-correction ability. Error Recovery (ER) explains 94.8% of metacognitive performance variance. 9 frontier models evaluated.
+
+- 🧬 [FINAL-Bench/Metacognitive Dataset](https://huggingface.co/datasets/FINAL-Bench/Metacognitive)
+- 🏆 [FINAL-Bench/Leaderboard](https://huggingface.co/spaces/FINAL-Bench/Leaderboard)
+
+## Citation
+
+```bibtex
+@misc{allbench2026,
+    title={ALL Bench Leaderboard 2026: Unified Multi-Modal AI Evaluation},
+    author={ALL Bench Team},
+    year={2026},
+    url={https://huggingface.co/spaces/FINAL-Bench/all-bench-leaderboard}
+}
+```
+
+---
+
+`#AIBenchmark` `#LLMLeaderboard` `#GPT5` `#Claude` `#Gemini` `#ALLBench` `#FINALBench` `#Metacognition` `#VLM` `#AIAgent` `#MultiModal` `#HuggingFace` `#ARC-AGI` `#AIEvaluation`