# Mixtral 8x7B vs o3

> o3 is the stronger model overall, scoring 47.4 to 26.8 on the Noometry Index. Mixtral 8x7B costs 5.0× less per token, which makes it the better buy when o3's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/mixtral-8x7b-vs-o3
- 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 o3 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where o3 leads 54.5 to 11.0.
- The biggest single-benchmark swing is MATH Level 5: 10% for Mixtral 8x7B and 97.8% for o3.
- Mixtral 8x7B is cheaper at $0.70 / $0.70 per million input/output tokens, against $2 / $8 for o3.
- o3 accepts more context: 200K tokens versus 32K.
- Mixtral 8x7B has downloadable open weights; the other is API-only.

## Snapshot

| | Mixtral 8x7B | o3 |
|---|---|---|
| Provider | Mistral AI | OpenAI |
| Noometry Index | 26.8 | 47.4 |
| Rank | 328 | 53 |
| Context | 32K | 200K |
| Input $/M | $0.70 | $2 |
| Output $/M | $0.70 | $8 |
| Weights | Open | Proprietary |

## Coding

- Mixtral 8x7B: 32.2 (#265)
- o3: 47.3 (#57)

| Benchmark | Mixtral 8x7B | o3 |
|---|---|---|
| LMArena Coding | 1127 | 1408 |
| SWE-bench Verified | — | 62.3% |
| SWE-bench Verified (bash only) | — | 58.4% |
| Aider Polyglot | — | 81.3% |
| GSO | — | 8.8% |
| WeirdML | — | 52.4% |
| CadEval | — | 74% |
| ALE-Bench | — | 933.55 |
| HumanEval+ | 39.6% | — |
| MBPP+ | 49.7% | — |

## Agentic & Tool Use

- Mixtral 8x7B: —
- o3: 34.3 (#42)

| Benchmark | Mixtral 8x7B | o3 |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 63% |
| GDPval | — | 30.8% |
| DeepResearch Bench | — | 45.2% |
| OSWorld | — | 23% |
| LMArena Search | — | 1144 |
| METR Time Horizons | — | 65.4% |

## Reasoning

- Mixtral 8x7B: 17.4 (#283)
- o3: 32.1 (#66)

| Benchmark | Mixtral 8x7B | o3 |
|---|---|---|
| LMArena Hard Prompts | 1115 | 1402 |
| DTBench | 49.6% | 84.8% |
| Epoch Capabilities Index | 118.38 | 146.88 |
| ForecastBench | 56.3 | 62.5 |
| ARC-AGI-2 | — | 6.5% |
| SimpleBench | — | 53.1% |
| Kagi LLM Benchmark | — | 67.6% |
| ARC-AGI-1 | — | 60.8% |
| CritPt | — | 1.4% |
| Chess Puzzles | — | 38% |
| EnigmaEval | — | 13.1% |
| Mystery Game Puzzles | — | 29% |
| LMCA | — | 39.7% |
| Adversarial NLI | 55.2% | — |
| HellaSwag | 86.7% | — |
| PIQA | 83.6% | — |
| WinoGrande | 77.2% | — |

## Math

- Mixtral 8x7B: 17.2 (#284)
- o3: 48.9 (#49)

| Benchmark | Mixtral 8x7B | o3 |
|---|---|---|
| Omni-MATH | 10.5% | 71.4% |
| LMArena Math | 1147 | 1426 |
| MATH Level 5 | 10% | 97.8% |
| FrontierMath (Tiers 1-3) | — | 33.3% |
| OTIS Mock AIME 2024-2025 | — | 84.4% |
| FrontierMath (Feb 2025 set) | — | 18.7% |
| FrontierMath Tier 4 (v1) | — | 2.1% |
| GSM8K | 74.4% | — |

## Knowledge

- Mixtral 8x7B: 11.0 (#296)
- o3: 54.5 (#47)

| Benchmark | Mixtral 8x7B | o3 |
|---|---|---|
| GPQA Diamond | 30.6% | 81.8% |
| MMLU-Pro | 33.5% | 85.9% |
| GPQA (HELM) | 29.6% | 75.3% |
| LMArena Expert | 1088 | 1403 |
| Humanity's Last Exam | — | 20.3% |
| SimpleQA Verified | — | 49.4% |
| Confabulations | — | 14.4% |
| ARC (AI2) Challenge | 87.3% | — |
| MMLU | 70.6% | — |
| OpenBookQA | 85.8% | — |
| TriviaQA | 82.2% | — |

## Multimodal

- Mixtral 8x7B: —
- o3: 40.7 (#35)

| Benchmark | Mixtral 8x7B | o3 |
|---|---|---|
| LMArena Vision | — | 1214 |
| GeoBench | — | 74% |
| VPCT | — | 52% |

## Multilingual

- Mixtral 8x7B: 29.8 (#261)
- o3: 51.9 (#99)

| Benchmark | Mixtral 8x7B | o3 |
|---|---|---|
| LMArena Non-English | 1077 | 1402 |
| LMArena Chinese | 1055 | 1438 |
| LMArena French | 1165 | 1430 |
| LMArena German | 1114 | 1420 |
| LMArena Japanese | 931 | 1404 |
| LMArena Korean | 968 | 1371 |
| LMArena Russian | 1090 | 1407 |
| LMArena Spanish | 1110 | 1395 |

## Instruction Following

- Mixtral 8x7B: 51.2 (#292)
- o3: 73.0 (#122)

| Benchmark | Mixtral 8x7B | o3 |
|---|---|---|
| IFEval | 57.5% | 86.9% |
| LMArena Instruction Following | 1110 | 1368 |

## Long Context

- Mixtral 8x7B: 33.4 (#255)
- o3: 53.2 (#6)

| Benchmark | Mixtral 8x7B | o3 |
|---|---|---|
| LMArena Longer Query | 1103 | 1372 |
| Fiction.LiveBench | — | 88.9% |
| CL-bench | — | 17.8% |

## Writing & Preference

- Mixtral 8x7B: 34.4 (#265)
- o3: 63.7 (#62)

| Benchmark | Mixtral 8x7B | o3 |
|---|---|---|
| LMArena Text | 1132 | 1410 |
| LMArena Creative Writing | 1109 | 1359 |
| WildBench | 67.3% | 86.1% |
| LMArena Multi-Turn | 1115 | 1405 |
| Short-Story Creative Writing | — | 83.9% |
| EQ-Bench Creative Writing | — | 1676 |

## FAQ

### Is Mixtral 8x7B better than o3?

o3 is the stronger model overall, scoring 47.4 to 26.8 on the Noometry Index. Mixtral 8x7B costs 5.0× less per token, which makes it the better buy when o3's lead doesn't matter for your workload.

### Which is cheaper, Mixtral 8x7B or o3?

Mixtral 8x7B is cheaper. It lists at $0.70 per million input tokens and $0.70 per million output tokens; o3 lists at $2 and $8.

### Is Mixtral 8x7B or o3 better for coding?

o3 scores higher on coding benchmarks: 47.3 versus 32.2 in the Noometry coding category.

### Which has the bigger context window?

o3 does, with 200K tokens against 32K.

### How many benchmarks do Mixtral 8x7B and o3 share?

27 benchmarks have published results for both models. Mixtral 8x7B has 38 scored results on Noometry and o3 has 63.