MMLU: Massive Multitask Language Understanding
Superseded by: mmlu-pro
About
Tests LLMs on undergraduate to professional level knowledge across 57 subjects with 15,908 multiple-choice questions. Top models now saturate at 86–90%; MMLU-Pro is the recommended harder successor.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 92.4 – 88.7.
Window
Apr 24 to May 25
last 3 snapshots
Mean delta
+2.30
score points
Coverage
1
models in latest snapshot
Leaderboard preview (top 5)
- 1. GPT-5.592.4
- 2. DeepSeek V4 Pro90.1
- 3. Xiaomi MiMo-V2.5-Pro89.4
- 4. Claude 3 Opus88.7
- 5. Claude 3.5 Sonnet88.7