Model comparison
Mistral 7B vs o3-mini
o3-mini is the stronger model overall, scoring 36.7 to 23.0 on the Noometry Index. Mistral 7B costs 7.7× less per token, which makes it the better buy when o3-mini's lead doesn't matter for your workload.
Last verified . 22 shared benchmarks.
Mistral AISummary
- They share 22 benchmarks with published results for both. Mistral 7B scores higher in 0 categories and o3-mini in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where o3-mini leads 38.3 to 7.4.
- The biggest single-benchmark swing is MATH Level 5: 3.7% for Mistral 7B and 96.5% for o3-mini.
- Mistral 7B is cheaper at $0.25 / $0.25 per million input/output tokens, against $1.10 / $4.40 for o3-mini.
- o3-mini accepts more context: 200K tokens versus 8K.
- Mistral 7B has downloadable open weights; the other is API-only.
Side by side
| Mistral 7B | o3-mini | |
|---|---|---|
| Provider | Mistral AI | OpenAI |
| Noometry Index | 23.0 | 36.7 |
| Released | 2023-09-27 | 2024-12-20 |
| Weights | Open | Proprietary |
| Context window | 8K | 200K |
| Max output | 8K | 100K |
| Input $ / M tokens | $0.25 | $1.10 |
| Output $ / M tokens | $0.25 | $4.40 |
| Results tracked | 37 | 51 |
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Category by category
Coding o3-mini leads
Mistral 7B: 26.4 (#326), o3-mini: 40.8 (#132)
| Benchmark | Mistral 7B | o3-mini |
|---|---|---|
| LMArena Coding | 1082 | 1378 |
| Aider Polyglot | — | 60.4% |
| SciCode | — | 39.8% |
| GSO | — | 1.3% |
| WeirdML | — | 43.7% |
| BigCodeBench Instruct | 19.5% | — |
| LiveBench Coding | — | 82.7% |
| BigCodeBench Complete | 27.3% | — |
| CadEval | — | 54% |
| HumanEval+ | 36% | — |
| MBPP+ | 42.1% | — |
Agentic & Tool Use Not comparable
Mistral 7B: —, o3-mini: 29.6 (#84)
| Benchmark | Mistral 7B | o3-mini |
|---|---|---|
| Cybench | — | 22.5% |
Reasoning o3-mini leads
Mistral 7B: 13.1 (#336), o3-mini: 16.3 (#305)
| Benchmark | Mistral 7B | o3-mini |
|---|---|---|
| Chess Puzzles | 0% | 17% |
| LMArena Hard Prompts | 1067 | 1366 |
| DTBench | 42.5% | 68.8% |
| Epoch Capabilities Index | 112.21 | 140.34 |
| ARC-AGI-2 | — | 3% |
| SimpleBench | — | 22.8% |
| ARC-AGI-1 | — | 34.5% |
| CritPt | — | 0.3% |
| LiveBench Reasoning | — | 89.6% |
| Mystery Game Puzzles | — | 7% |
| LiveBench Data Analysis | — | 70.6% |
| LMCA | — | 19% |
| Adversarial NLI | 47.1% | — |
| BIG-Bench Hard | 56.1% | — |
| ForecastBench | — | 59.6 |
| HellaSwag | 81% | — |
| LiveBench | — | 75.9% |
| PIQA | 83% | — |
| WinoGrande | 75.3% | — |
Math o3-mini leads
Mistral 7B: 8.1 (#325), o3-mini: 28.1 (#244)
| Benchmark | Mistral 7B | o3-mini |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 0.3% | 76.9% |
| LMArena Math | 1085 | 1396 |
| MATH Level 5 | 3.7% | 96.5% |
| FrontierMath (Tiers 1-3) | — | 18.6% |
| FrontierMath Tier 4 | — | 0% |
| LiveBench Math | — | 77.3% |
| FrontierMath (Feb 2025 set) | — | 12.4% |
| FrontierMath Tier 4 (v1) | — | 4.2% |
| GSM8K | 54.4% | — |
Knowledge o3-mini leads
Mistral 7B: 7.4 (#311), o3-mini: 38.3 (#146)
| Benchmark | Mistral 7B | o3-mini |
|---|---|---|
| GPQA Diamond | 15.2% | 77% |
| LMArena Expert | 1036 | 1364 |
| SimpleQA Verified | — | 15.3% |
| Confabulations | — | 17.9% |
| ARC (AI2) Challenge | 78.6% | — |
| BoolQ | 87.4% | — |
| MMLU | 62.5% | — |
| OpenBookQA | 79.8% | — |
| TriviaQA | 75.2% | — |
Multilingual o3-mini leads
Mistral 7B: 25.8 (#283), o3-mini: 45.7 (#164)
| Benchmark | Mistral 7B | o3-mini |
|---|---|---|
| LMArena Non-English | 1012 | 1319 |
| LMArena Chinese | 1009 | 1379 |
| LMArena French | 1037 | 1334 |
| LMArena German | 987 | 1303 |
| LMArena Japanese | 878 | 1286 |
| LMArena Russian | 1018 | 1304 |
| LMArena Spanish | 1026 | 1321 |
| LMArena Korean | — | 1314 |
Instruction Following o3-mini leads
Mistral 7B: 54.2 (#280), o3-mini: 75.1 (#72)
| Benchmark | Mistral 7B | o3-mini |
|---|---|---|
| LMArena Instruction Following | 1060 | 1337 |
| LiveBench Instruction Following | — | 84.4% |
Long Context o3-mini leads
Mistral 7B: 32.2 (#271), o3-mini: 33.8 (#256)
| Benchmark | Mistral 7B | o3-mini |
|---|---|---|
| LMArena Longer Query | 1060 | 1343 |
| Fiction.LiveBench | — | 50% |
Writing & Preference o3-mini leads
Mistral 7B: 30.7 (#286), o3-mini: 50.3 (#182)
| Benchmark | Mistral 7B | o3-mini |
|---|---|---|
| LMArena Text | 1090 | 1337 |
| LMArena Creative Writing | 1068 | 1286 |
| LMArena Multi-Turn | 1062 | 1320 |
| Short-Story Creative Writing | — | 61.7% |
| LiveBench Language | — | 50.7% |
Frequently asked questions
Is Mistral 7B better than o3-mini?
o3-mini is the stronger model overall, scoring 36.7 to 23.0 on the Noometry Index. Mistral 7B costs 7.7× less per token, which makes it the better buy when o3-mini's lead doesn't matter for your workload.
Which is cheaper, Mistral 7B or o3-mini?
Mistral 7B is cheaper. It lists at $0.25 per million input tokens and $0.25 per million output tokens; o3-mini lists at $1.10 and $4.40.
Is Mistral 7B or o3-mini better for coding?
o3-mini scores higher on coding benchmarks: 40.8 versus 26.4 in the Noometry coding category.
Which has the bigger context window?
o3-mini does, with 200K tokens against 8K.
How many benchmarks do Mistral 7B and o3-mini share?
22 benchmarks have published results for both models. Mistral 7B has 37 scored results on Noometry and o3-mini has 51.