Model comparison
Mixtral 8x7B vs o3-mini
o3-mini is the stronger model overall, scoring 36.7 to 27.1 on the Noometry Index. Mixtral 8x7B costs 2.8× 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. Mixtral 8x7B scores higher in 1 category and o3-mini in 7 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where o3-mini leads 38.3 to 11.0.
- The biggest single-benchmark swing is MATH Level 5: 10% for Mixtral 8x7B and 96.5% for o3-mini.
- Mixtral 8x7B is cheaper at $0.70 / $0.70 per million input/output tokens, against $1.10 / $4.40 for o3-mini.
- o3-mini accepts more context: 200K tokens versus 32K.
- Mixtral 8x7B has downloadable open weights; the other is API-only.
Side by side
| Mixtral 8x7B | o3-mini | |
|---|---|---|
| Provider | Mistral AI | OpenAI |
| Noometry Index | 27.1 | 36.7 |
| Released | 2023-12-11 | 2024-12-20 |
| Weights | Open | Proprietary |
| Context window | 32K | 200K |
| Max output | 32K | 100K |
| Input $ / M tokens | $0.70 | $1.10 |
| Output $ / M tokens | $0.70 | $4.40 |
| Results tracked | 38 | 51 |
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Category by category
Coding o3-mini leads
Mixtral 8x7B: 32.8 (#269), o3-mini: 40.8 (#132)
| Benchmark | Mixtral 8x7B | o3-mini |
|---|---|---|
| LMArena Coding | 1126 | 1378 |
| Aider Polyglot | — | 60.4% |
| SciCode | — | 39.8% |
| GSO | — | 1.3% |
| WeirdML | — | 43.7% |
| LiveBench Coding | — | 82.7% |
| CadEval | — | 54% |
| HumanEval+ | 39.6% | — |
| MBPP+ | 49.7% | — |
Agentic & Tool Use Not comparable
Mixtral 8x7B: —, o3-mini: 29.6 (#84)
| Benchmark | Mixtral 8x7B | o3-mini |
|---|---|---|
| Cybench | — | 22.5% |
Reasoning Mixtral 8x7B leads
Mixtral 8x7B: 18.2 (#285), o3-mini: 16.3 (#305)
| Benchmark | Mixtral 8x7B | o3-mini |
|---|---|---|
| LMArena Hard Prompts | 1115 | 1366 |
| DTBench | 49.6% | 68.8% |
| Epoch Capabilities Index | 118.47 | 140.34 |
| ForecastBench | 56.3 | 59.6 |
| ARC-AGI-2 | — | 3% |
| SimpleBench | — | 22.8% |
| ARC-AGI-1 | — | 34.5% |
| CritPt | — | 0.3% |
| Chess Puzzles | — | 17% |
| LiveBench Reasoning | — | 89.6% |
| Mystery Game Puzzles | — | 7% |
| LiveBench Data Analysis | — | 70.6% |
| LMCA | — | 19% |
| Adversarial NLI | 55.2% | — |
| HellaSwag | 86.7% | — |
| LiveBench | — | 75.9% |
| PIQA | 83.6% | — |
| WinoGrande | 77.2% | — |
Math o3-mini leads
Mixtral 8x7B: 18.8 (#289), o3-mini: 28.1 (#244)
| Benchmark | Mixtral 8x7B | o3-mini |
|---|---|---|
| LMArena Math | 1147 | 1396 |
| MATH Level 5 | 10% | 96.5% |
| FrontierMath (Tiers 1-3) | — | 18.6% |
| FrontierMath Tier 4 | — | 0% |
| OTIS Mock AIME 2024-2025 | — | 76.9% |
| Omni-MATH | 10.5% | — |
| LiveBench Math | — | 77.3% |
| FrontierMath (Feb 2025 set) | — | 12.4% |
| FrontierMath Tier 4 (v1) | — | 4.2% |
| GSM8K | 74.4% | — |
Knowledge o3-mini leads
Mixtral 8x7B: 11.0 (#301), o3-mini: 38.3 (#146)
| Benchmark | Mixtral 8x7B | o3-mini |
|---|---|---|
| GPQA Diamond | 30.6% | 77% |
| LMArena Expert | 1088 | 1364 |
| SimpleQA Verified | — | 15.3% |
| MMLU-Pro | 33.5% | — |
| Confabulations | — | 17.9% |
| GPQA (HELM) | 29.6% | — |
| ARC (AI2) Challenge | 87.3% | — |
| MMLU | 70.6% | — |
| OpenBookQA | 85.8% | — |
| TriviaQA | 82.2% | — |
Multilingual o3-mini leads
Mixtral 8x7B: 29.6 (#266), o3-mini: 45.7 (#164)
| Benchmark | Mixtral 8x7B | o3-mini |
|---|---|---|
| LMArena Non-English | 1077 | 1319 |
| LMArena Chinese | 1055 | 1379 |
| LMArena French | 1166 | 1334 |
| LMArena German | 1114 | 1303 |
| LMArena Japanese | 931 | 1286 |
| LMArena Korean | 968 | 1314 |
| LMArena Russian | 1090 | 1304 |
| LMArena Spanish | 1111 | 1321 |
Instruction Following o3-mini leads
Mixtral 8x7B: 51.0 (#297), o3-mini: 75.1 (#72)
| Benchmark | Mixtral 8x7B | o3-mini |
|---|---|---|
| LMArena Instruction Following | 1109 | 1337 |
| LiveBench Instruction Following | — | 84.4% |
| IFEval | 57.5% | — |
Long Context Too close to call
Mixtral 8x7B: 33.4 (#260), o3-mini: 33.8 (#256)
| Benchmark | Mixtral 8x7B | o3-mini |
|---|---|---|
| LMArena Longer Query | 1103 | 1343 |
| Fiction.LiveBench | — | 50% |
Writing & Preference o3-mini leads
Mixtral 8x7B: 34.2 (#270), o3-mini: 50.3 (#182)
| Benchmark | Mixtral 8x7B | o3-mini |
|---|---|---|
| LMArena Text | 1132 | 1337 |
| LMArena Creative Writing | 1109 | 1286 |
| LMArena Multi-Turn | 1115 | 1320 |
| Short-Story Creative Writing | — | 61.7% |
| WildBench | 67.3% | — |
| LiveBench Language | — | 50.7% |
Frequently asked questions
Is Mixtral 8x7B better than o3-mini?
o3-mini is the stronger model overall, scoring 36.7 to 27.1 on the Noometry Index. Mixtral 8x7B costs 2.8× less per token, which makes it the better buy when o3-mini's lead doesn't matter for your workload.
Which is cheaper, Mixtral 8x7B or o3-mini?
Mixtral 8x7B is cheaper. It lists at $0.70 per million input tokens and $0.70 per million output tokens; o3-mini lists at $1.10 and $4.40.
Is Mixtral 8x7B or o3-mini better for coding?
o3-mini scores higher on coding benchmarks: 40.8 versus 32.8 in the Noometry coding category.
Which has the bigger context window?
o3-mini does, with 200K tokens against 32K.
How many benchmarks do Mixtral 8x7B and o3-mini share?
22 benchmarks have published results for both models. Mixtral 8x7B has 38 scored results on Noometry and o3-mini has 51.