Model comparison
Mixtral 8x7B vs o4-mini
o4-mini is the stronger model overall, scoring 41.6 to 27.1 on the Noometry Index. Mixtral 8x7B costs 2.8× less per token, which makes it the better buy when o4-mini's lead doesn't matter for your workload.
Last verified . 27 shared benchmarks.
Mistral AISummary
- They share 27 benchmarks with published results for both. Mixtral 8x7B scores higher in 0 categories and o4-mini in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where o4-mini leads 43.6 to 11.0.
- The biggest single-benchmark swing is MATH Level 5: 10% for Mixtral 8x7B and 97.8% for o4-mini.
- Mixtral 8x7B is cheaper at $0.70 / $0.70 per million input/output tokens, against $1.10 / $4.40 for o4-mini.
- o4-mini accepts more context: 200K tokens versus 32K.
- Mixtral 8x7B has downloadable open weights; the other is API-only.
Side by side
| Mixtral 8x7B | o4-mini | |
|---|---|---|
| Provider | Mistral AI | OpenAI |
| Noometry Index | 27.1 | 41.6 |
| Released | 2023-12-11 | 2025-04-16 |
| 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 | 60 |
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Category by category
Coding o4-mini leads
Mixtral 8x7B: 32.8 (#269), o4-mini: 40.9 (#127)
| Benchmark | Mixtral 8x7B | o4-mini |
|---|---|---|
| LMArena Coding | 1126 | 1368 |
| SWE-bench Verified (bash only) | — | 45% |
| Aider Polyglot | — | 72% |
| GSO | — | 3.6% |
| WeirdML | — | 52.6% |
| CadEval | — | 62% |
| ALE-Bench | — | 826.17 |
| AlgoTune | — | 1.72 |
| HumanEval+ | 39.6% | — |
| MBPP+ | 49.7% | — |
Agentic & Tool Use Not comparable
Mixtral 8x7B: —, o4-mini: 32.6 (#61)
| Benchmark | Mixtral 8x7B | o4-mini |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 53.2% |
| GDPval | — | 25.3% |
| METR Time Horizons | — | 63.9% |
Reasoning o4-mini leads
Mixtral 8x7B: 18.2 (#285), o4-mini: 24.6 (#162)
| Benchmark | Mixtral 8x7B | o4-mini |
|---|---|---|
| LMArena Hard Prompts | 1115 | 1351 |
| DTBench | 49.6% | 77.6% |
| Epoch Capabilities Index | 118.47 | 145.64 |
| ForecastBench | 56.3 | 61.8 |
| ARC-AGI-2 | — | 6.1% |
| SimpleBench | — | 38.7% |
| Kagi LLM Benchmark | — | 67.6% |
| ARC-AGI-1 | — | 58.7% |
| CritPt | — | 0.6% |
| Chess Puzzles | — | 26% |
| EnigmaEval | — | 9.2% |
| Mystery Game Puzzles | — | 5% |
| LMCA | — | 26.5% |
| Adversarial NLI | 55.2% | — |
| HellaSwag | 86.7% | — |
| PIQA | 83.6% | — |
| WinoGrande | 77.2% | — |
Math o4-mini leads
Mixtral 8x7B: 18.8 (#289), o4-mini: 40.8 (#89)
| Benchmark | Mixtral 8x7B | o4-mini |
|---|---|---|
| Omni-MATH | 10.5% | 72% |
| LMArena Math | 1147 | 1389 |
| MATH Level 5 | 10% | 97.8% |
| FrontierMath (Tiers 1-3) | — | 36.1% |
| FrontierMath Tier 4 | — | 4.9% |
| OTIS Mock AIME 2024-2025 | — | 81.7% |
| FrontierMath (Feb 2025 set) | — | 24.8% |
| FrontierMath Tier 4 (v1) | — | 6.3% |
| GSM8K | 74.4% | — |
Knowledge o4-mini leads
Mixtral 8x7B: 11.0 (#301), o4-mini: 43.6 (#91)
| Benchmark | Mixtral 8x7B | o4-mini |
|---|---|---|
| GPQA Diamond | 30.6% | 79.6% |
| MMLU-Pro | 33.5% | 82% |
| GPQA (HELM) | 29.6% | 73.5% |
| LMArena Expert | 1088 | 1343 |
| Humanity's Last Exam | — | 18.1% |
| SimpleQA Verified | — | 19.6% |
| Confabulations | — | 15.8% |
| Vectara Hallucination Rate | — | 18.6% |
| ARC (AI2) Challenge | 87.3% | — |
| MMLU | 70.6% | — |
| OpenBookQA | 85.8% | — |
| TriviaQA | 82.2% | — |
Multimodal Not comparable
Mixtral 8x7B: —, o4-mini: 40.2 (#49)
| Benchmark | Mixtral 8x7B | o4-mini |
|---|---|---|
| LMArena Vision | — | 1194 |
| GeoBench | — | 64% |
| VPCT | — | 57.5% |
Multilingual o4-mini leads
Mixtral 8x7B: 29.6 (#266), o4-mini: 47.0 (#154)
| Benchmark | Mixtral 8x7B | o4-mini |
|---|---|---|
| LMArena Non-English | 1077 | 1337 |
| LMArena Chinese | 1055 | 1354 |
| LMArena French | 1166 | 1364 |
| LMArena German | 1114 | 1336 |
| LMArena Japanese | 931 | 1308 |
| LMArena Korean | 968 | 1312 |
| LMArena Russian | 1090 | 1334 |
| LMArena Spanish | 1111 | 1347 |
Instruction Following o4-mini leads
Mixtral 8x7B: 51.0 (#297), o4-mini: 75.2 (#68)
| Benchmark | Mixtral 8x7B | o4-mini |
|---|---|---|
| IFEval | 57.5% | 92.8% |
| LMArena Instruction Following | 1109 | 1321 |
Long Context o4-mini leads
Mixtral 8x7B: 33.4 (#260), o4-mini: 45.5 (#33)
| Benchmark | Mixtral 8x7B | o4-mini |
|---|---|---|
| LMArena Longer Query | 1103 | 1315 |
| Fiction.LiveBench | — | 77.8% |
Writing & Preference o4-mini leads
Mixtral 8x7B: 34.2 (#270), o4-mini: 54.0 (#152)
| Benchmark | Mixtral 8x7B | o4-mini |
|---|---|---|
| LMArena Text | 1132 | 1353 |
| LMArena Creative Writing | 1109 | 1294 |
| WildBench | 67.3% | 85.4% |
| LMArena Multi-Turn | 1115 | 1350 |
| Short-Story Creative Writing | — | 75% |
Frequently asked questions
Is Mixtral 8x7B better than o4-mini?
o4-mini is the stronger model overall, scoring 41.6 to 27.1 on the Noometry Index. Mixtral 8x7B costs 2.8× less per token, which makes it the better buy when o4-mini's lead doesn't matter for your workload.
Which is cheaper, Mixtral 8x7B or o4-mini?
Mixtral 8x7B is cheaper. It lists at $0.70 per million input tokens and $0.70 per million output tokens; o4-mini lists at $1.10 and $4.40.
Is Mixtral 8x7B or o4-mini better for coding?
o4-mini scores higher on coding benchmarks: 40.9 versus 32.8 in the Noometry coding category.
Which has the bigger context window?
o4-mini does, with 200K tokens against 32K.
How many benchmarks do Mixtral 8x7B and o4-mini share?
27 benchmarks have published results for both models. Mixtral 8x7B has 38 scored results on Noometry and o4-mini has 60.