Model comparison
Mistral Large vs o3
o3 is the stronger model overall, scoring 47.5 to 31.9 on the Noometry Index.
Last verified . 37 shared benchmarks.
Mistral AISummary
- They share 37 benchmarks with published results for both. Mistral Large scores higher in 0 categories and o3 in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where o3 leads 50.2 to 18.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 8.5% for Mistral Large and 84.4% for o3.
- Mistral Large is cheaper at $2 / $6 per million input/output tokens, against $2 / $8 for o3.
- o3 accepts more context: 200K tokens versus 131K.
- Mistral Large has downloadable open weights; the other is API-only.
Side by side
| Mistral Large | o3 | |
|---|---|---|
| Provider | Mistral AI | OpenAI |
| Noometry Index | 31.9 | 47.5 |
| Released | 2024-02-26 | 2025-04-16 |
| Weights | Open | Proprietary |
| Context window | 131K | 200K |
| Max output | 16K | 100K |
| Input $ / M tokens | $2 | $2 |
| Output $ / M tokens | $6 | $8 |
| Results tracked | 51 | 63 |
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Category by category
Coding o3 leads
Mistral Large: 34.3 (#240), o3: 46.8 (#64)
| Benchmark | Mistral Large | o3 |
|---|---|---|
| LMArena Coding | 1277 | 1408 |
| ALE-Bench | 264.7 | 933.55 |
| SWE-bench Verified | — | 62.3% |
| SWE-bench Verified (bash only) | — | 58.4% |
| Aider Polyglot | — | 81.3% |
| SciCode | 36.2% | — |
| GSO | — | 8.8% |
| WeirdML | — | 52.4% |
| BigCodeBench Instruct | 30% | — |
| LiveBench Coding | 47.1% | — |
| BigCodeBench Complete | 38.3% | — |
| CadEval | — | 74% |
| HumanEval+ | 62.2% | — |
| MBPP+ | 59.5% | — |
Agentic & Tool Use o3 leads
Mistral Large: 28.6 (#89), o3: 34.5 (#44)
| Benchmark | Mistral Large | o3 |
|---|---|---|
| Berkeley Function Calling Leaderboard | 38.4% | 63% |
| GDPval | — | 30.8% |
| DeepResearch Bench | — | 45.2% |
| OSWorld | — | 23% |
| LMArena Search | — | 1144 |
| METR Time Horizons | — | 65.4% |
Reasoning o3 leads
Mistral Large: 15.8 (#310), o3: 32.0 (#78)
| Benchmark | Mistral Large | o3 |
|---|---|---|
| SimpleBench | 22.5% | 53.1% |
| CritPt | 0% | 1.4% |
| LMArena Hard Prompts | 1257 | 1402 |
| DTBench | 65.1% | 84.8% |
| LMCA | 16.7% | 39.7% |
| Epoch Capabilities Index | 128.52 | 146.86 |
| ForecastBench | 57.1 | 62.5 |
| ARC-AGI-2 | — | 6.5% |
| Kagi LLM Benchmark | — | 67.6% |
| ARC-AGI-1 | — | 60.8% |
| Chess Puzzles | — | 38% |
| EnigmaEval | — | 13.1% |
| LiveBench Reasoning | 43.5% | — |
| Mystery Game Puzzles | — | 29% |
| LiveBench Data Analysis | 50.1% | — |
| LiveBench | 48.4% | — |
Math o3 leads
Mistral Large: 18.2 (#291), o3: 50.2 (#58)
| Benchmark | Mistral Large | o3 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 8.5% | 84.4% |
| Omni-MATH | 28.1% | 71.4% |
| LMArena Math | 1262 | 1426 |
| MATH Level 5 | 50.3% | 97.8% |
| FrontierMath (Feb 2025 set) | 0.3% | 18.7% |
| FrontierMath (Tiers 1-3) | — | 33.3% |
| LiveBench Math | 42.5% | — |
| FrontierMath Tier 4 (v1) | — | 2.1% |
Knowledge o3 leads
Mistral Large: 30.1 (#230), o3: 54.6 (#52)
| Benchmark | Mistral Large | o3 |
|---|---|---|
| GPQA Diamond | 51.3% | 81.8% |
| MMLU-Pro | 59.9% | 85.9% |
| Confabulations | 21.4% | 14.4% |
| GPQA (HELM) | 43.5% | 75.3% |
| LMArena Expert | 1232 | 1402 |
| Humanity's Last Exam | — | 20.3% |
| SimpleQA Verified | — | 49.4% |
| Vectara Hallucination Rate | 4.5% | — |
| MMLU | 80% | — |
Multimodal Not comparable
Mistral Large: —, o3: 41.4 (#36)
| Benchmark | Mistral Large | o3 |
|---|---|---|
| LMArena Vision | — | 1214 |
| GeoBench | — | 74% |
| VPCT | — | 52% |
Multilingual o3 leads
Mistral Large: 40.0 (#219), o3: 51.7 (#105)
| Benchmark | Mistral Large | o3 |
|---|---|---|
| LMArena Non-English | 1237 | 1401 |
| LMArena Chinese | 1240 | 1437 |
| LMArena French | 1325 | 1430 |
| LMArena German | 1254 | 1420 |
| LMArena Japanese | 1188 | 1403 |
| LMArena Korean | 1202 | 1370 |
| LMArena Russian | 1257 | 1406 |
| LMArena Spanish | 1268 | 1395 |
Instruction Following o3 leads
Mistral Large: 67.9 (#191), o3: 72.8 (#127)
| Benchmark | Mistral Large | o3 |
|---|---|---|
| IFEval | 87.7% | 86.9% |
| LMArena Instruction Following | 1249 | 1368 |
| LiveBench Instruction Following | 67.9% | — |
Long Context o3 leads
Mistral Large: 38.3 (#199), o3: 53.3 (#6)
| Benchmark | Mistral Large | o3 |
|---|---|---|
| LMArena Longer Query | 1261 | 1372 |
| Fiction.LiveBench | — | 88.9% |
| CL-bench | — | 17.8% |
Writing & Preference o3 leads
Mistral Large: 40.7 (#242), o3: 63.5 (#64)
| Benchmark | Mistral Large | o3 |
|---|---|---|
| LMArena Text | 1266 | 1410 |
| LMArena Creative Writing | 1243 | 1359 |
| Short-Story Creative Writing | 69% | 83.9% |
| EQ-Bench Creative Writing | 985 | 1676 |
| WildBench | 80.1% | 86.1% |
| LMArena Multi-Turn | 1260 | 1405 |
| LiveBench Language | 39.4% | — |
Frequently asked questions
Is Mistral Large better than o3?
o3 is the stronger model overall, scoring 47.5 to 31.9 on the Noometry Index.
Which is cheaper, Mistral Large or o3?
Mistral Large is cheaper. It lists at $2 per million input tokens and $6 per million output tokens; o3 lists at $2 and $8.
Is Mistral Large or o3 better for coding?
o3 scores higher on coding benchmarks: 46.8 versus 34.3 in the Noometry coding category.
Which has the bigger context window?
o3 does, with 200K tokens against 131K.
How many benchmarks do Mistral Large and o3 share?
37 benchmarks have published results for both models. Mistral Large has 51 scored results on Noometry and o3 has 63.