MMMU: Massive Multi-discipline Multimodal Understanding
11,500+ vision-language questions spanning 30 disciplines across six core areas (art, business, science, health, humanities, tech). Evaluates college-level multimodal reasoning. High benchmark score alone doesn't make a model the right pick — weigh it against pricing, API availability, and release date.
Models ranked
46
tracked on this benchmark
Score band
86.0 – 30.3
best → lowest tracked
Snapshot trend
-12.30
May 12 → Jun 7 · 6 models
Leaderboard
Tracked models ranked by Accuracy (higher is better).
Notes: Confidence: high. DAT-4172 May 12 /best/ refresh; llm-stats listed Qwen3.6 Plus at 86.0%.
Notes: DAT-5669: official o3 launch figure. Do not promote MMMU-Pro for this pair without official corroboration.
Notes: Confidence: medium. DAT-4172 May 12 /best/ refresh; queued for recheck in the next audit.
Notes: DAT-5669: official standard MMMU figure. Gemini 2.5 Pro Deep Think 84.0% is a separate variant and should not replace this comparable row.
How to read this benchmark
This benchmark scores models where higher is better. Scores are useful for directional filtering and shortlisting — not for universal quality ranking. Prefer benchmarks closest to your workload, then validate the linked model pages for pricing, context window, and provider availability.
Trust this score when
- There is a fresh timestamped snapshot (or multiple snapshots) for this benchmark.
- The model list covers the same version family you can actually deploy today.
- Top candidates overlap with your required routing and feature requirements.
Be cautious when
- There is only one benchmark snapshot or the dataset appears stale.
- The benchmark metric direction is opposite of your decision objective.
- The score difference between options is narrow and likely within implementation variance.
FAQ
What does the MMMU benchmark measure?
11,500+ vision-language questions spanning 30 disciplines across six core areas (art, business, science, health, humanities, tech). Evaluates college-level multimodal reasoning. On this page it lists 46 tracked model variants where higher is better.
Is a higher Massive Multi-discipline Multimodal Understanding score always better?
For this benchmark, higher is better. A high score helps you shortlist, but confirm pricing, context window, and provider availability on each model page before committing — the top scorer is not always the right pick for your workload or budget.
How current is this Massive Multi-discipline Multimodal Understanding data?
This benchmark was last reviewed on Apr 15, 2026. The tracked score average moved -12.30 points across the last 3 snapshots.
Last reviewed: Apr 15, 2026