README.md

---
---
annotations_creators:
  - expert-generated
language:
  - en
license: mit
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
  - union-eval
task_categories:
  - text-generation
  - visual-question-answering
  - text-to-image
  - text-to-video
  - text-to-audio
configs:
  - config_name: llm
    data_files:
      - split: train
        path: data/llm.jsonl
  - config_name: vlm_flagship
    data_files:
      - split: train
        path: data/vlm_flagship.jsonl
  - config_name: agent
    data_files:
      - split: train
        path: data/agent.jsonl
  - config_name: image
    data_files:
      - split: train
        path: data/image.jsonl
  - config_name: video
    data_files:
      - split: train
        path: data/video.jsonl
  - config_name: music
    data_files:
      - split: train
        path: data/music.jsonl
models:
  # LLM - Open Source
  - Qwen/Qwen3.5-122B-A10B
  - Qwen/Qwen3.5-27B
  - Qwen/Qwen3.5-35B-A3B
  - Qwen/Qwen3.5-9B
  - Qwen/Qwen3.5-4B
  - Qwen/Qwen3-Next-80B-A3B-Thinking
  - deepseek-ai/DeepSeek-V3
  - deepseek-ai/DeepSeek-R1
  - zai-org/GLM-5
  - meta-llama/Llama-4-Scout-17B-16E-Instruct
  - meta-llama/Llama-4-Maverick-17B-128E-Instruct
  - microsoft/phi-4
  - upstage/Solar-Open-100B
  - K-intelligence/Midm-2.0-Base-Instruct
  - Nanbeige/Nanbeige4.1-3B
  - MiniMaxAI/MiniMax-M2.5
  - stepfun-ai/Step-3.5-Flash
  # VLM - Open Source
  - OpenGVLab/InternVL3-78B
  - Qwen/Qwen2.5-VL-72B-Instruct
  - Qwen/Qwen3-VL-30B-A3B
  # Image Generation
  - black-forest-labs/FLUX.1-dev
  - stabilityai/stable-diffusion-3.5-large
  # Video Generation
  - Lightricks/LTX-Video
  # Music Generation
  - facebook/musicgen-large
  - facebook/jasco-chords-drums-melody-1B
---

# 🏆 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>

![ALL Bench Leaderboard](./1.png)

![ALL Bench Leaderboard](./2.png)


## Dataset Summary

ALL Bench Leaderboard aggregates and cross-verifies benchmark scores for **90+ 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** | 41 | 32 fields | MMLU-Pro, GPQA, AIME, HLE, ARC-AGI-2, Metacog, SWE-Pro, IFEval, LCB, **Union Eval**, etc. |
| **VLM Flagship** | 11 | 10 fields | MMMU, MMMU-Pro, MathVista, AI2D, OCRBench, MMStar, HallusionBench, etc. |
| **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 |

![ALL Bench Leaderboard](./3.png)

![ALL Bench Leaderboard](./4.png)

![ALL Bench Leaderboard](./5.png)


## What's New — v2.2.1

### 🏅 Union Eval ★NEW

**ALL Bench's proprietary integrated benchmark.** Fuses the discriminative core of 10 existing benchmarks (GPQA, AIME, HLE, MMLU-Pro, IFEval, LiveCodeBench, BFCL, ARC-AGI, SWE, FINAL Bench) into a single 1000-question pool with a season-based rotation system.

**Key features:**
- **100% JSON auto-graded** — every question requires mandatory JSON output with verifiable fields. Zero keyword matching.
- **Fuzzy JSON matching** — tolerates key name variants, fraction formats, text fallback when JSON parsing fails.
- **Season rotation** — 70% new questions each season, 30% anchor questions for cross-season IRT calibration.
- **8 rounds of empirical testing** — v2 (82.4%) → v3 (82.0%) → Final (79.5%) → S2 (81.8%) → S3 (75.0%) → Fuzzy (69.9/69.3%).

**Key discovery:** *"The bottleneck in benchmarking is not question difficulty — it's grading methodology."*

**Empirically confirmed LLM weakness map:**
- 🔴 Poetry + code cross-constraints: 18-28%
- 🔴 Complex JSON structure (10+ constraints): 0%
- 🔴 Pure series computation (Σk²/3ᵏ): 0%
- 🟢 Metacognitive reasoning (Bayes, proof errors): 95%
- 🟢 Revised science detection: 86%

**Current scores (S3, 20Q sample, Fuzzy JSON):**

| Model | Union Eval |
|-------|-----------|
| Claude Sonnet 4.6 | **69.9** |
| Claude Opus 4.6 | **69.3** |

### Other v2.2 changes
- Fair Coverage Correction: composite scoring ^0.5 → ^0.7
- +7 FINAL Bench scores (15 total)
- Columns sorted by fill rate
- Model Card popup (click model name) · FINAL Bench detail popup (click Metacog score)
- 🔥 Heatmap, 💰 Price vs Performance scatter tools


## 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, Model Card popup, FINAL Bench detail popup, and downloadable Intelligence Report (PDF/DOCX).

## Data Structure

```
data/
├── llm.jsonl           # 41 LLMs × 32 fields (incl. unionEval ★NEW)
├── vlm_flagship.jsonl  # 11 flagship VLMs × 10 benchmarks
├── agent.jsonl         # 10 agent models × 8 benchmarks
├── image.jsonl         # 10 image gen models × S/A/B/C ratings
├── video.jsonl         # 10 video gen models × S/A/B/C ratings
└── music.jsonl         # 8 music gen models × S/A/B/C ratings
```

## 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 (%) |
| `unionEval` | float \| null | **★NEW** Union Eval S3 — ALL Bench integrated benchmark (100% JSON auto-graded) |
| `priceIn` / `priceOut` | float \| null | USD per 1M tokens |
| `elo` | int \| null | Arena Elo rating |
| `license` | string | `Prop`, `Apache2`, `MIT`, `Open`, etc. |

![ALL Bench Leaderboard](./6.png)

![ALL Bench Leaderboard](./7.png)

![ALL Bench Leaderboard](./8.png)

## Composite Score

```
Score = Avg(confirmed benchmarks) × (N/10)^0.7
```

10 core benchmarks across the **5-Axis Intelligence Framework**: Knowledge · Expert Reasoning · Abstract Reasoning · Metacognition · Execution.

**v2.2 change:** Exponent adjusted from 0.5 to 0.7 for fairer coverage weighting. Models with 7/10 benchmarks receive ×0.79 (was ×0.84), while 4/10 receives ×0.53 (was ×0.63).

## 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" },
  "unionEval": { "level": "single-source", "source": "Union Eval S3 — ALL Bench official" }
}
```

## Usage

```python
from datasets import load_dataset

# Load LLM data
ds = load_dataset("FINAL-Bench/ALL-Bench-Leaderboard", "llm")
df = ds["train"].to_pandas()

# Top 5 LLMs by GPQA
ranked = df.dropna(subset=["gpqa"]).sort_values("gpqa", ascending=False)
for _, m in ranked.head(5).iterrows():
    print(f"{m['name']:25s} GPQA={m['gpqa']}")

# Union Eval scores
union = df.dropna(subset=["unionEval"]).sort_values("unionEval", ascending=False)
for _, m in union.iterrows():
    print(f"{m['name']:25s} Union Eval={m['unionEval']}")
```

![ALL Bench Leaderboard](./9.png)

![ALL Bench Leaderboard](./10.png)

![ALL Bench Leaderboard](./11.png)


## Union Eval — Integrated AI Assessment

Union Eval is ALL Bench's proprietary benchmark designed to address three fundamental problems with existing AI evaluations:

1. **Contamination** — Public benchmarks leak into training data. Union Eval rotates 70% of questions each season.
2. **Single-axis measurement** — AIME tests only math, IFEval only instruction-following. Union Eval integrates arithmetic, poetry constraints, metacognition, coding, calibration, and myth detection.
3. **Score inflation via keyword matching** — Traditional rubric grading gives 100% to "well-written" answers even if content is wrong. Union Eval enforces mandatory JSON output with zero keyword matching.

**Structure (S3 — 100 Questions from 1000 Pool):**

| Category | Questions | Role | Expected Score |
|----------|-----------|------|---------------|
| Pure Arithmetic | 10 | Confirmed Killer #1 | 0-57% |
| Poetry/Verse IFEval | 8 | Confirmed Killer #2 | 18-28% |
| Structured Data IFEval | 7 | JSON/CSV verification | 0-70% |
| FINAL Bench Metacognition | 20 | Core brand | 50-95% |
| Union Complex Synthesis | 15 | Extreme multi-domain | 40-73% |
| Revised Science / Myths | 5 | Calibration traps | 50-86% |
| Code I/O, GPQA, HLE | 19 | Expert + execution | 50-100% |
| BFCL Tool Use, Anchors | 16 | Cross-season calibration | varies |

Note: The 100-question dataset is **not publicly released** to prevent contamination. Only scores are published.


## FINAL Bench — Metacognitive Benchmark

FINAL Bench measures AI self-correction ability. Error Recovery (ER) explains 94.8% of metacognitive performance variance. 15 frontier models evaluated.

- 🧬 [FINAL-Bench/Metacognitive Dataset](https://huggingface.co/datasets/FINAL-Bench/Metacognitive)
- 🏆 [FINAL-Bench/Leaderboard](https://huggingface.co/spaces/FINAL-Bench/Leaderboard)


## Changelog

| Version | Date | Changes |
|---------|------|---------|
| **v2.2.1** | 2026-03-10 | 🏅 **Union Eval ★NEW** — integrated benchmark column (`unionEval` field). Claude Opus 4.6: 69.3 · Sonnet 4.6: 69.9 |
| v2.2 | 2026-03-10 | Fair Coverage (^0.7), +7 Metacog scores, Model Cards, FINAL Bench popup, Heatmap, Price-Perf |
| v2.1 | 2026-03-08 | Confidence badges, Intelligence Report, source tracking |
| v2.0 | 2026-03-07 | All blanks filled, Korean AI data, 42 LLMs cross-verified |
| v1.9 | 2026-03-05 | +3 LLMs, dark mode, mobile responsive |

## 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` `#UnionEval` `#VLM` `#AIAgent` `#MultiModal` `#HuggingFace` `#ARC-AGI` `#AIEvaluation` `#VIDRAFT.net`