MMMU: Massive Multi-discipline Multimodal Understanding
About
11,500+ vision-language questions spanning 30 disciplines across six core areas (art, business, science, health, humanities, tech). Evaluates college-level multimodal reasoning.Compare model scores alongside pricing, API availability, and release date — high benchmark score alone doesn't make a model the right pick.
How to read this benchmark
This benchmark uses a scoring system where higher is better. Scores are useful for directional filtering and model shortlisting, not for universal quality ranking.
Interpretation checklist: prefer benchmarks that are closest to your workload style, then validate against the linked model pages for pricing, context window, and provider availability.
Trust when:
- There is a fresh timestamped snapshot (or multiple snapshots) for this benchmark.
- Model list covers the same version family you can actually deploy today.
- Top candidates overlap with your required routing and feature requirements.
Don't trust when:
- There is only one benchmark snapshot or the dataset appears stale.
- Benchmark metric direction is opposite of your decision objective.
- The score difference between options is narrow and likely within implementation variance.
Current modeled score band for tracked entries is roughly 86.0 – 83.6.
Window
Apr 27 to May 19
last 3 snapshots
Mean delta
-1.80
score points
Coverage
1
models in latest snapshot
Leaderboard preview (top 5)
- 1. Qwen3.6-Plus86.0
- 2. ByteDance Doubao Seed 2.0 Pro85.4
- 3. Qwen3.5-397B-A17B85.0
- 4. Gemini 2.5 Pro84.0
- 5. Gemini 3.5 Flash83.6