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A head-to-head benchmark comparison of Magistral Medium 1.1 across 12 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 Medium 1.1 specs →| Benchmark | Magistral Medium 1.1 |
|---|---|
| code | |
| Aider Polyglot | 47.1% |
| LiveCodeBench v5 | 59.4% |
| LiveCodeBench v6 | 50.3% |
| math | |
| AIME 2024 | 90% |
| AIME 2025 | 83.3% |
| MATH-500 | 94.3% |
| multimodal | |
| MMMU | 70% |
| MMMU-Pro (Standard) | 57.9 |
| MMMU-Pro (Vision) | 52.1 |
| MathVista | 70.1% |
| reasoning | |
| GPQA Diamond | 70.8% |
| Humanity's Last Exam | 9% |
Best result per row highlighted in cyan. Each benchmark links to its definition and sources; each model links to its full scorecard.
Magistral Medium 1.1 has the strongest result on Aider Polyglot among the models compared here. See the code-category rows in the table for the full picture.
This comparison covers 12 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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