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A head-to-head benchmark comparison of Magistral Small 1.1 across 11 evaluations.
We introduce Magistral, Mistral’s first reasoning model and our own scalable reinforcement learning (RL) pipeline. Instead of relying on existing implementation.
Full Magistral Small 1.1 specs →| Benchmark | Magistral Small 1.1 |
|---|---|
| code | |
| LiveCodeBench v5 | 55.8% |
| LiveCodeBench v6 | 47.4% |
| math | |
| AIME 2024 | 83.3% |
| AIME 2025 | 76.7% |
| MATH-500 | 95.9% |
| multimodal | |
| MMMU | 66% |
| MMMU-Pro (Standard) | 51.8 |
| MMMU-Pro (Vision) | 39.2 |
| MathVista | 66.8% |
| reasoning | |
| GPQA Diamond | 68.2% |
| Humanity's Last Exam | 6.4% |
Best result per row highlighted in cyan. Each benchmark links to its definition and sources; each model links to its full scorecard.
Magistral Small 1.1 has the strongest result on LiveCodeBench v5 among the models compared here. See the code-category rows in the table for the full picture.
This comparison covers 11 benchmarks on which at least one of the selected models has a published score.
Scores are aggregated from official model cards, technical reports and standard public evaluations, and link back to each benchmark's source. They are updated as new results are published.
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