MMMU

11.5K college-level, image-paired exam questions across six disciplines, testing expert knowledge that requires reading a figure, chart or diagram.

Also known as: Massive Multi-discipline Multimodal Understanding

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Categorymultimodal
Subcategoryexpert multi-discipline knowledge
Page statusactive
Metricaccuracy
Directionhigher_is_better
Unit%
Dataset size11500
Dataset licenceApache-2.0

What it measures

MMMU pairs text questions with images such as charts, diagrams, maps, tables, music sheets and chemical structures, drawn from real college exams, quizzes and textbooks. It spans six core disciplines (Art and Design, Business, Science, Health and Medicine, Humanities and Social Science, and Tech and Engineering) across 30 subjects and 183 subfields, and 30 distinct image types. The goal is to test whether a model can combine expert-level subject knowledge with genuine reading of the accompanying image, rather than treating the image as decoration for a question answerable from text alone.

Task format

Mostly four-option multiple-choice questions, each paired with one or more images; a smaller portion are open-ended.

Models reporting this benchmark

These figures come from the model cards, which carry one collection date per card and no per-score attribution. They are shown as reported, not as verified evidence.
ModelProviderScoreCard as of
Llama 4 Maverick 17B 128E InstructMeta73.42026-04
Llama-4-Maverick-17B-128E-Instruct-FP8Meta73.42026-04
Qwen3 VL 32B InstructAlibaba / Qwen Team72.82026-04
GPT-4OpenAI72.52026-04
GPT-4.1OpenAI72.52026-04
GPT-4.1 miniOpenAI72.52026-04
GPT-4.1 nanoOpenAI72.52026-04
Gemini 2.5 ProGoogle DeepMind72.12026-04
Gemini 2.5 Pro Preview 05-06Google DeepMind72.12026-04
Gemini 2.5 Pro Preview 06-05Google DeepMind72.12026-04
Gemini 2.5 Pro Preview TTSGoogle DeepMind72.12026-04
Claude Opus 4Anthropic70.82026-04
Claude Opus 4.6Anthropic70.82026-04
Llama 4 Scout 17B 16EMeta69.42026-04
Llama 4 Scout 17B 16E InstructMeta69.42026-04
Llama-4-Scout-17B-16E-Instruct-FP8Meta69.42026-04
GPT-4oOpenAI69.12026-04
GPT-4o (2024-05-13)OpenAI69.12026-04
GPT-4o (2024-08-06)OpenAI69.12026-04
GPT-4o (2024-11-20)OpenAI69.12026-04
GPT-4o miniOpenAI69.12026-04
Claude Sonnet 4Anthropic68.22026-04
Claude Sonnet 4.5Anthropic68.22026-04
Claude Sonnet 4.5 (latest)Anthropic68.22026-04
Claude Sonnet 3.5Anthropic65.92026-04
Claude Sonnet 3.5 v2Anthropic65.92026-04
Qwen2.5-VL 72B InstructAlibaba / Qwen Team64.52026-04
Gemini 1.5 ProGoogle DeepMind62.82026-04
Gemini 2.0 FlashGoogle DeepMind62.52026-04
Gemini 2.0 Flash LiteGoogle DeepMind62.52026-04
Qwen3 VL 8B InstructAlibaba / Qwen Team62.52026-04
Llama 3.2 90B VisionMeta60.32026-04
Llama 3.2 90B Vision InstructMeta60.32026-04
Gemma 4 31BGoogle DeepMind60.22026-04
gemma 4 31B itGoogle DeepMind60.22026-04
gemma 4 31B it GGUFUnsloth60.22026-04
Gemma 4 31B IT NVFP4NVIDIA60.22026-04
Gemma 3 12BGoogle DeepMind59.62026-04
Claude Opus 3Anthropic59.42026-04
GPT-4 TurboOpenAI59.42026-04
Pixtral Large (latest)Mistral AI58.82026-04
NVLM D 72BNVIDIA58.72026-04
Mistral Large (latest)Mistral AI56.82026-04
Mistral Large 3Mistral AI56.82026-04
Gemini 1.5 FlashGoogle DeepMind56.12026-04
Gemini 1.5 Flash-8BGoogle DeepMind56.12026-04
Grok 2xAI55.52026-04
Grok 2 LatestxAI55.52026-04
Grok 2 VisionxAI55.52026-04
Grok 2 Vision (1212)xAI55.52026-04
Grok 2 Vision LatestxAI55.52026-04
Gemma 3 27BGoogle DeepMind55.22026-04
Qwen2 VL 7B InstructAlibaba / Qwen Team54.12025-03
Qwen2 VL 7B Instruct AWQAlibaba / Qwen Team54.12025-03
Claude Sonnet 3Anthropic53.12026-04
Command A VisionCohere52.12026-04
MiniCPM V 4OpenBMB51.22026-04
MiniCPM V 4 5OpenBMB51.22026-04
MiniCPM V 4 5 ggufOpenBMB51.22026-04
Llama 3.2 11B VisionMeta50.72026-04
Llama 3.2 11B Vision InstructMeta50.72026-04
Claude Haiku 3Anthropic50.22026-04
Pixtral 12BMistral AI50.22026-04
Gemma 3 4BGoogle DeepMind48.82026-04
gemma 3 4B ptGoogle DeepMind48.82026-04
Qwen2.5-VL 7B InstructAlibaba / Qwen Team48.22026-04
Phi 3.5 vision instructMicrosoft43.02026-04
Qwen2.5 VL 3B InstructAlibaba / Qwen Team42.52026-04

Data

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