ALL Bench Leaderboard 2026: The First Unified Multi‑Modal AI Benchmark
FINAL-Bench/ALL-Bench-Leaderboard ↗
The **ALL Bench Leaderboard 2026** dataset, curated by FINAL‑Bench, aggregates benchmark scores for more than 90 AI models across six modalities—LLMs, VLMs, autonomous agents, image generation, video generation, and music generation. Each entry records dozens of performance fields (e.g., MMLU‑Pro, GPQA, Metacog, Union Eval) along with a confidence badge indicating whether the score is cross‑verified, single‑source, or self‑reported. The dataset is provided in JSONL files split by modality (llm, vlm_flagship, agent, image, video, music) and can be loaded directly with the 🤗 datasets library.
The most notable addition in version 2.2.1 is **Union Eval**, a proprietary 100‑question integrated benchmark that forces JSON‑structured answers and applies fuzzy matching to reduce keyword‑matching inflation. Union Eval scores are included in the `unionEval` column of the LLM config and are accompanied by confidence metadata. The leaderboard also surfaces price‑per‑million‑tokens information, Elo ratings, and a composite score that weights the number of confirmed benchmarks using a 0.7 exponent, offering a concise intelligence metric across the five‑axis framework (knowledge, expert reasoning, abstract reasoning, metacognition, execution).
Beyond raw numbers, the dataset supplies auxiliary columns for model metadata (provider, release date, licensing) and visual tools via the accompanying Hugging Face Space, such as heatmaps, price‑vs‑performance scatter plots, and model‑card pop‑ups. Researchers can quickly filter models by benchmark thresholds, compare confidence levels, or export the curated intelligence report in PDF/DOCX format. The dataset’s small size (under 1 K entries) and open‑source licensing (MIT) make it ideal for rapid prototyping of evaluation dashboards, model‑selection utilities, or meta‑analysis studies on emerging multimodal AI capabilities.
Overall, ALL Bench Leaderboard 2026 serves as a single source of truth for developers and decision‑makers who need a trustworthy, cross‑verified view of the rapidly evolving AI model landscape, spanning text, vision, audio, and agency tasks in one unified file.
Project Ideas
- Create an interactive web dashboard that lets users filter and rank models by any benchmark field (e.g., GPQA, Union Eval) and visualize price‑performance trade‑offs.
- Build a Python script that extracts the top‑3 models per modality based on composite scores and generates a concise markdown report for quick stakeholder review.
- Develop a recommendation engine that suggests the best model for a specific task (e.g., code generation, image realism) by matching required benchmark thresholds with the dataset entries.
- Design a research notebook that analyzes the correlation between confidence levels (cross‑verified vs. self‑reported) and actual performance across the six modalities.
- Implement a data‑driven model‑selection API that returns models meeting user‑defined criteria such as minimum Union Eval score, maximum token price, and specific licensing constraints.