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.
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. Use scores for directional filtering and shortlisting, not universal quality ranking; then validate pricing, context window, provider availability, and fit for your workload.
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.
Last reviewed: Apr 15, 2026