# Mixtral 8x7B vs o4-mini

> o4-mini is the stronger model overall, scoring 41.6 to 26.8 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.

- Canonical page: https://noometry.com/compare/mixtral-8x7b-vs-o4-mini
- Last updated: 2026-10-10
- Shared benchmarks: 27

## Summary

- 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 43.5 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.

## Snapshot

| | Mixtral 8x7B | o4-mini |
|---|---|---|
| Provider | Mistral AI | OpenAI |
| Noometry Index | 26.8 | 41.6 |
| Rank | 328 | 119 |
| Context | 32K | 200K |
| Input $/M | $0.70 | $1.10 |
| Output $/M | $0.70 | $4.40 |
| Weights | Open | Proprietary |

## Coding

- Mixtral 8x7B: 32.2 (#265)
- o4-mini: 41.6 (#110)

| Benchmark | Mixtral 8x7B | o4-mini |
|---|---|---|
| LMArena Coding | 1127 | 1369 |
| 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

- Mixtral 8x7B: —
- o4-mini: 32.3 (#57)

| Benchmark | Mixtral 8x7B | o4-mini |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 53.2% |
| GDPval | — | 25.3% |
| METR Time Horizons | — | 63.9% |

## Reasoning

- Mixtral 8x7B: 17.4 (#283)
- o4-mini: 24.6 (#143)

| Benchmark | Mixtral 8x7B | o4-mini |
|---|---|---|
| LMArena Hard Prompts | 1115 | 1351 |
| DTBench | 49.6% | 77.6% |
| Epoch Capabilities Index | 118.38 | 145.63 |
| 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

- Mixtral 8x7B: 17.2 (#284)
- o4-mini: 40.2 (#79)

| Benchmark | Mixtral 8x7B | o4-mini |
|---|---|---|
| Omni-MATH | 10.5% | 72% |
| LMArena Math | 1147 | 1390 |
| 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

- Mixtral 8x7B: 11.0 (#296)
- o4-mini: 43.5 (#83)

| 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 | 1344 |
| 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

- Mixtral 8x7B: —
- o4-mini: 39.5 (#46)

| Benchmark | Mixtral 8x7B | o4-mini |
|---|---|---|
| LMArena Vision | — | 1194 |
| GeoBench | — | 64% |
| VPCT | — | 57.5% |

## Multilingual

- Mixtral 8x7B: 29.8 (#261)
- o4-mini: 47.2 (#149)

| Benchmark | Mixtral 8x7B | o4-mini |
|---|---|---|
| LMArena Non-English | 1077 | 1336 |
| LMArena Chinese | 1055 | 1355 |
| LMArena French | 1165 | 1362 |
| LMArena German | 1114 | 1336 |
| LMArena Japanese | 931 | 1308 |
| LMArena Korean | 968 | 1313 |
| LMArena Russian | 1090 | 1334 |
| LMArena Spanish | 1110 | 1346 |

## Instruction Following

- Mixtral 8x7B: 51.2 (#292)
- o4-mini: 75.4 (#63)

| Benchmark | Mixtral 8x7B | o4-mini |
|---|---|---|
| IFEval | 57.5% | 92.8% |
| LMArena Instruction Following | 1110 | 1321 |

## Long Context

- Mixtral 8x7B: 33.4 (#255)
- o4-mini: 45.5 (#31)

| Benchmark | Mixtral 8x7B | o4-mini |
|---|---|---|
| LMArena Longer Query | 1103 | 1315 |
| Fiction.LiveBench | — | 77.8% |

## Writing & Preference

- Mixtral 8x7B: 34.4 (#265)
- o4-mini: 54.2 (#148)

| 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% |

## FAQ

### Is Mixtral 8x7B better than o4-mini?

o4-mini is the stronger model overall, scoring 41.6 to 26.8 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: 41.6 versus 32.2 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.