Model comparison
Mistral Large vs o3-pro
o3-pro is the stronger model overall, scoring 42.9 to 31.9 on the Noometry Index. Mistral Large costs 12× less per token, which makes it the better buy when o3-pro's lead doesn't matter for your workload.
Last verified . 6 shared benchmarks.
Mistral AISummary
- They share 6 benchmarks with published results for both. Mistral Large scores higher in 1 category and o3-pro in 4 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in long context, where o3-pro leads 72.2 to 38.3.
- The biggest single-benchmark swing is DTBench: 65.1% for Mistral Large and 86.9% for o3-pro.
- Mistral Large is cheaper at $2 / $6 per million input/output tokens, against $20 / $80 for o3-pro.
- o3-pro accepts more context: 200K tokens versus 131K.
- Mistral Large has downloadable open weights; the other is API-only.
Side by side
| Mistral Large | o3-pro | |
|---|---|---|
| Provider | Mistral AI | OpenAI |
| Noometry Index | 31.9 | 42.9 |
| Released | 2024-02-26 | 2025-06-10 |
| Weights | Open | Proprietary |
| Context window | 131K | 200K |
| Max output | 16K | 100K |
| Input $ / M tokens | $2 | $20 |
| Output $ / M tokens | $6 | $80 |
| Results tracked | 51 | 12 |
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Category by category
Coding o3-pro leads
Mistral Large: 34.3 (#240), o3-pro: 55.5 (#24)
| Benchmark | Mistral Large | o3-pro |
|---|---|---|
| Aider Polyglot | — | 84.9% |
| SciCode | 36.2% | — |
| WeirdML | — | 58.2% |
| BigCodeBench Instruct | 30% | — |
| LiveBench Coding | 47.1% | — |
| LMArena Coding | 1277 | — |
| BigCodeBench Complete | 38.3% | — |
| ALE-Bench | 264.7 | — |
| HumanEval+ | 62.2% | — |
| MBPP+ | 59.5% | — |
Agentic & Tool Use Not comparable
Mistral Large: 28.6 (#89), o3-pro: —
| Benchmark | Mistral Large | o3-pro |
|---|---|---|
| Berkeley Function Calling Leaderboard | 38.4% | — |
Reasoning o3-pro leads
Mistral Large: 15.8 (#310), o3-pro: 23.8 (#171)
| Benchmark | Mistral Large | o3-pro |
|---|---|---|
| DTBench | 65.1% | 86.9% |
| LMCA | 16.7% | 38.5% |
| Epoch Capabilities Index | 128.52 | 147.42 |
| ARC-AGI-2 | — | 4.9% |
| SimpleBench | 22.5% | — |
| Kagi LLM Benchmark | — | 72.1% |
| ARC-AGI-1 | — | 59.3% |
| CritPt | 0% | — |
| LiveBench Reasoning | 43.5% | — |
| LMArena Hard Prompts | 1257 | — |
| LiveBench Data Analysis | 50.1% | — |
| ForecastBench | 57.1 | — |
| LiveBench | 48.4% | — |
Math Not comparable
Mistral Large: 18.2 (#291), o3-pro: —
| Benchmark | Mistral Large | o3-pro |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 8.5% | — |
| Omni-MATH | 28.1% | — |
| LiveBench Math | 42.5% | — |
| LMArena Math | 1262 | — |
| MATH Level 5 | 50.3% | — |
| FrontierMath (Feb 2025 set) | 0.3% | — |
Knowledge Too close to call
Mistral Large: 30.1 (#230), o3-pro: 29.5 (#238)
| Benchmark | Mistral Large | o3-pro |
|---|---|---|
| Confabulations | 21.4% | 14.2% |
| Vectara Hallucination Rate | 4.5% | 23.3% |
| GPQA Diamond | 51.3% | — |
| MMLU-Pro | 59.9% | — |
| GPQA (HELM) | 43.5% | — |
| LMArena Expert | 1232 | — |
| MMLU | 80% | — |
Multilingual Not comparable
Mistral Large: 40.0 (#219), o3-pro: —
| Benchmark | Mistral Large | o3-pro |
|---|---|---|
| LMArena Non-English | 1237 | — |
| LMArena Chinese | 1240 | — |
| LMArena French | 1325 | — |
| LMArena German | 1254 | — |
| LMArena Japanese | 1188 | — |
| LMArena Korean | 1202 | — |
| LMArena Russian | 1257 | — |
| LMArena Spanish | 1268 | — |
Instruction Following Not comparable
Mistral Large: 67.9 (#191), o3-pro: —
| Benchmark | Mistral Large | o3-pro |
|---|---|---|
| LiveBench Instruction Following | 67.9% | — |
| IFEval | 87.7% | — |
| LMArena Instruction Following | 1249 | — |
Long Context o3-pro leads
Mistral Large: 38.3 (#199), o3-pro: 72.2 (#1)
| Benchmark | Mistral Large | o3-pro |
|---|---|---|
| Fiction.LiveBench | — | 97.2% |
| LMArena Longer Query | 1261 | — |
Writing & Preference o3-pro leads
Mistral Large: 40.7 (#242), o3-pro: 57.1 (#133)
| Benchmark | Mistral Large | o3-pro |
|---|---|---|
| Short-Story Creative Writing | 69% | 84.4% |
| LMArena Text | 1266 | — |
| LMArena Creative Writing | 1243 | — |
| EQ-Bench Creative Writing | 985 | — |
| WildBench | 80.1% | — |
| LMArena Multi-Turn | 1260 | — |
| LiveBench Language | 39.4% | — |
Frequently asked questions
Is Mistral Large better than o3-pro?
o3-pro is the stronger model overall, scoring 42.9 to 31.9 on the Noometry Index. Mistral Large costs 12× less per token, which makes it the better buy when o3-pro's lead doesn't matter for your workload.
Which is cheaper, Mistral Large or o3-pro?
Mistral Large is cheaper. It lists at $2 per million input tokens and $6 per million output tokens; o3-pro lists at $20 and $80.
Is Mistral Large or o3-pro better for coding?
o3-pro scores higher on coding benchmarks: 55.5 versus 34.3 in the Noometry coding category.
Which has the bigger context window?
o3-pro does, with 200K tokens against 131K.
How many benchmarks do Mistral Large and o3-pro share?
6 benchmarks have published results for both models. Mistral Large has 51 scored results on Noometry and o3-pro has 12.