Model comparison
Mistral Medium vs o1-pro
Mistral Medium is the stronger model overall, scoring 36.3 to 31.5 on the Noometry Index.
Last verified . 1 shared benchmarks.
Mistral AISummary
- They share 1 benchmark with published results for both. Mistral Medium scores higher in 1 category and o1-pro in 1 category; 2 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where o1-pro leads 29.7 to 25.0.
- Mistral Medium is cheaper at $1.50 / $7.50 per million input/output tokens, against $150 / $600 for o1-pro.
- Mistral Medium accepts more context: 262K tokens versus 200K.
- Mistral Medium has downloadable open weights; the other is API-only.
Side by side
| Mistral Medium | o1-pro | |
|---|---|---|
| Provider | Mistral AI | OpenAI |
| Noometry Index | 36.3 | 31.5 |
| Released | 2023-12-11 | 2025-03-19 |
| Weights | Open | Proprietary |
| Context window | 262K | 200K |
| Max output | 262K | 100K |
| Input $ / M tokens | $1.50 | $150 |
| Output $ / M tokens | $7.50 | $600 |
| Results tracked | 36 | 3 |
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Category by category
Coding Not comparable
Mistral Medium: 34.2 (#243), o1-pro: —
| Benchmark | Mistral Medium | o1-pro |
|---|---|---|
| FrontierCode | 8% | — |
| SciCode | 40.2% | — |
| WeirdML | 43.7% | — |
| LMArena Coding | 1434 | — |
| ALE-Bench | 763.98 | — |
Agentic & Tool Use Not comparable
Mistral Medium: 28.3 (#90), o1-pro: —
| Benchmark | Mistral Medium | o1-pro |
|---|---|---|
| Berkeley Function Calling Leaderboard | 37.7% | — |
Reasoning Mistral Medium leads
Mistral Medium: 24.0 (#167), o1-pro: 20.4 (#239)
| Benchmark | Mistral Medium | o1-pro |
|---|---|---|
| Kagi LLM Benchmark | 50% | — |
| ARC-AGI-1 | — | 23.3% |
| CritPt | 0% | — |
| EnigmaEval | — | 6.1% |
| LMArena Hard Prompts | 1426 | — |
| DTBench | 75.5% | — |
| LMCA | 26.1% | — |
| Surface Evolver Bench | 26.9% | — |
Math Not comparable
Mistral Medium: 28.1 (#245), o1-pro: —
| Benchmark | Mistral Medium | o1-pro |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 32.2% | — |
| ProofBench | 9% | — |
| LMArena Math | 1408 | — |
| MATH Level 5 | 81.6% | — |
| FrontierMath (Feb 2025 set) | 0.3% | — |
Knowledge o1-pro leads
Mistral Medium: 25.0 (#265), o1-pro: 29.7 (#234)
| Benchmark | Mistral Medium | o1-pro |
|---|---|---|
| Humanity's Last Exam | 4.5% | 8.1% |
| GPQA Diamond | 59.5% | — |
| Vectara Hallucination Rate | 22.7% | — |
| LMArena Expert | 1408 | — |
Multimodal Not comparable
Mistral Medium: 35.3 (#88), o1-pro: —
| Benchmark | Mistral Medium | o1-pro |
|---|---|---|
| LMArena Vision | 1172 | — |
Multilingual Not comparable
Mistral Medium: 52.1 (#91), o1-pro: —
| Benchmark | Mistral Medium | o1-pro |
|---|---|---|
| LMArena Non-English | 1408 | — |
| LMArena Chinese | 1447 | — |
| LMArena French | 1459 | — |
| LMArena German | 1432 | — |
| LMArena Japanese | 1378 | — |
| LMArena Korean | 1380 | — |
| LMArena Russian | 1411 | — |
| LMArena Spanish | 1433 | — |
Instruction Following Not comparable
Mistral Medium: 73.7 (#116), o1-pro: —
| Benchmark | Mistral Medium | o1-pro |
|---|---|---|
| LMArena Instruction Following | 1398 | — |
Long Context Not comparable
Mistral Medium: 42.9 (#114), o1-pro: —
| Benchmark | Mistral Medium | o1-pro |
|---|---|---|
| LMArena Longer Query | 1406 | — |
Writing & Preference Not comparable
Mistral Medium: 60.0 (#103), o1-pro: —
| Benchmark | Mistral Medium | o1-pro |
|---|---|---|
| LMArena Text | 1424 | — |
| LMArena Creative Writing | 1391 | — |
| Short-Story Creative Writing | 77.3% | — |
| LMArena Multi-Turn | 1418 | — |
Frequently asked questions
Is Mistral Medium better than o1-pro?
Mistral Medium is the stronger model overall, scoring 36.3 to 31.5 on the Noometry Index.
Which is cheaper, Mistral Medium or o1-pro?
Mistral Medium is cheaper. It lists at $1.50 per million input tokens and $7.50 per million output tokens; o1-pro lists at $150 and $600.
Which has the bigger context window?
Mistral Medium does, with 262K tokens against 200K.
How many benchmarks do Mistral Medium and o1-pro share?
1 benchmark has published results for both models. Mistral Medium has 36 scored results on Noometry and o1-pro has 3.