---
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
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
---

# 🏆 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 **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 |

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

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

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


## 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. |

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

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

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

## 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"}
```

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

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

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


## 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` `#VIDRAFT.net`