FINAL-Bench/ALL-Bench-Leaderboard

🏆 ALL Bench Leaderboard 2026

The only AI benchmark dataset covering LLM · VLM · Agent · Image · Video · Music in a single unified file.

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

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

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.

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:

"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

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']}")

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
• 🏆 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

@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}
}

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