MMLU-Pro
Harder version of MMLU with 10-choice questions and tighter quality control. Replaces MMLU for frontier models.
Category: general ·
Metric: accuracy ·
Source: huggingface.co ↗
Leaderboard
| Rank | Model | Provider | Score | Measured | Source |
|---|---|---|---|---|---|
| 1 | GPT-5 | OpenAI | 88.4 | 2025-08-07 | ↗ |
| 2 | Gemini 2.5 Pro | 86.4 | 2025-03-25 | ↗ | |
| 3 | DeepSeek-V3 | DeepSeek | 75.9 | 2024-12-26 | ↗ |
What this benchmark measures, in detail
Harder version of MMLU with 10-choice questions and tighter quality control. Replaces MMLU for frontier models.
Different benchmarks measure different things. A model that excels on MMLU-Pro may underperform on real-world workloads if the benchmark's distribution doesn't match your data. Use benchmark scores as a triage signal — narrow to a shortlist — then evaluate on your actual workload before committing.
Methodology notes
Scores in the leaderboard are taken from the model's release announcement or model card, cited via the "Source" link. Where two sources disagree (which happens often for SWE-bench and IFEval), the linked primary source wins. Reproducibility for some benchmarks (notably anything graded by an LLM) varies by run — treat the score as ±2-3 points unless the source is a peer-reviewed result.