MMLU
Massive Multitask Language Understanding measures knowledge across 57 academic subjects including STEM, humanities, social sciences, and more. It tests both world knowledge and problem-solving ability at varying difficulty levels from elementary to professional.
Metrics
Accuracy (%) across 57 subjects
Created By
Dan Hendrycks et al.
Paper
View paper →Website
Visit website →Top Model Scores
| Rank | Model | Score | Date |
|---|---|---|---|
| 1 | GPT-5.2 | 92.4% | 2026-03 |
| 2 | Claude Opus 4.6 | 91.8% | 2026-02 |
| 3 | Gemini 3 Ultra | 91.2% | 2026-01 |
| 4 | Grok 4 | 90.5% | 2026-02 |
| 5 | Llama 4 405B | 88.7% | 2026-01 |
Related Language Benchmarks
MT-Bench
MT-Bench evaluates multi-turn conversation ability using 80 high-quality multi-turn questions across 8 categories: writing, roleplay, extraction, reasoning, math, coding, knowledge, and STEM. Responses are judged by GPT-4 on a 1-10 scale.
Top: GPT-5.2 — 9.72
AlpacaEval 2.0
AlpacaEval 2.0 is an automatic evaluation benchmark that measures instruction-following ability. It uses a length-controlled win rate against a reference model, reducing length bias that affected the original version.
Top: Claude Opus 4.6 — 72.1%
WildBench
WildBench evaluates AI models on challenging real-world user queries collected from the wild. It focuses on complex, multi-constraint instructions that test practical model capabilities beyond academic benchmarks.
Top: Claude Opus 4.6 — 68.7%
IFEval
IFEval (Instruction Following Evaluation) tests how well models follow verifiable formatting instructions such as word count constraints, inclusion/exclusion of specific phrases, formatting requirements, and structural constraints.
Top: Claude Opus 4.6 — 91.2%