MMMUval
Validation set for MMMU (Massive Multi-discipline Multimodal Understanding and Reasoning) benchmark, designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning across Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering.
Qwen3 VL 235B A22B Thinking from Alibaba Cloud / Qwen Team currently leads the MMMUval leaderboard with a score of 0.806 across 4 evaluated AI models.
What MMMUval measures
MMMUval is a multimodal benchmark that evaluates large language models on multimodal, reasoning, general, healthcare, and vision tasks. LLM Stats tracks 4 models on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.8, with the leader reaching 0.8.
Compare leaders on the best AI for multimodal, best AI for reasoning, best AI for general, best AI for healthcare and best AI for vision leaderboards.
Publication
- Paper
- MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
- Authors
- Xiang Yue, Yuansheng Ni, Kai Zhang, Tianyu Zheng, and 18 others
- Published
- arXiv
- 2311.16502
Abstract
We introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning. MMMU includes 11.5K meticulously collected multimodal questions from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering. These questions span 30 subjects and 183 subfields, comprising 30 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures. Unlike existing benchmarks, MMMU focuses on advanced perception and reasoning with domain-specific knowledge, challenging models to perform tasks akin to those faced by experts. The evaluation of 14 open-source LMMs as well as the proprietary GPT-4V(ision) and Gemini highlights the substantial challenges posed by MMMU. Even the advanced GPT-4V and Gemini Ultra only achieve accuracies of 56% and 59% respectively, indicating significant room for improvement. We believe MMMU will stimulate the community to build next-generation multimodal foundation models towards expert artificial general intelligence.
Qwen3 VL 235B A22B Thinking leads with 80.6%, followed by Qwen3 VL 235B A22B Instruct at 78.7% and 
Claude Sonnet 4.5 at 77.8%.
Progress Over Time
Interactive timeline showing model performance evolution on MMMUval
MMMUval Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  Alibaba Cloud / Qwen Team | 236B | — | — | ||
| 2 | Alibaba Cloud / Qwen Team | 236B | 262K | $0.30 / $1.50 | ||
| 3 | Anthropic | — | 200K | $3.00 / $15.00 | ||
| 4 | Alibaba Cloud / Qwen Team | 73B | — | — |
FAQ
Common questions about MMMUval.
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