# GPT-6 Sol vs Mixtral 8x22B

> GPT-6 Sol is the stronger model overall, scoring 61.8 to 27.1 on the Noometry Index.

- Canonical page: https://noometry.com/compare/gpt-6-sol-vs-mixtral-8x22b
- Last updated: 2026-10-10
- Shared benchmarks: 20

## Summary

- They share 20 benchmarks with published results for both. GPT-6 Sol scores higher in 9 categories and Mixtral 8x22B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6 Sol leads 87.2 to 22.9.
- The biggest single-benchmark swing is GPQA Diamond: 94.3% for GPT-6 Sol and 34.1% for Mixtral 8x22B.
- Mixtral 8x22B is cheaper at $2 / $6 per million input/output tokens, against $2 / $10 for GPT-6 Sol.
- GPT-6 Sol accepts more context: 1.05M tokens versus 64K.
- Mixtral 8x22B has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-6 Sol | Mixtral 8x22B |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 61.8 | 27.1 |
| Rank | 12 | 333 |
| Context | 1.05M | 64K |
| Input $/M | $2 | $2 |
| Output $/M | $10 | $6 |
| Weights | Proprietary | Open |

## Coding

- GPT-6 Sol: 60.1 (#11)
- Mixtral 8x22B: 24.2 (#329)

| Benchmark | GPT-6 Sol | Mixtral 8x22B |
|---|---|---|
| LMArena Coding | 1447 | 1166 |
| DeepSWE | 68.8% | — |
| FrontierCode | 49.3% | — |
| LMArena WebDev | 1688 | — |
| SciCode | 57.6% | — |
| WeirdML | — | 3.2% |
| BigCodeBench Instruct | — | 40.6% |
| BigCodeBench Complete | — | 50.2% |
| ALE-Bench | 2,462 | — |
| HumanEval+ | — | 72% |
| MBPP+ | — | 64.3% |

## Agentic & Tool Use

- GPT-6 Sol: 37.2 (#36)
- Mixtral 8x22B: 23.1 (#127)

| Benchmark | GPT-6 Sol | Mixtral 8x22B |
|---|---|---|
| APEX-Agents | 54.3% | — |
| Cybench | — | 7.5% |
| GDP.pdf | 26.4% | — |
| Vending-Bench 2 | 14,428 | — |

## Reasoning

- GPT-6 Sol: 74.0 (#9)
- Mixtral 8x22B: 19.9 (#248)

| Benchmark | GPT-6 Sol | Mixtral 8x22B |
|---|---|---|
| LMArena Hard Prompts | 1418 | 1150 |
| DTBench | 97.3% | 55.1% |
| Epoch Capabilities Index | 162.72 | 122.03 |
| ARC-AGI-2 | 89.6% | — |
| NYT Connections (extended) | 90.1% | — |
| ARC-AGI-1 | 95.5% | — |
| CritPt | 30.9% | — |
| EBR-Bench | 53.3% | — |
| Mystery Game Puzzles | 56% | — |
| LMCA | 59.1% | — |
| ForecastBench | — | 56.3 |

## Math

- GPT-6 Sol: 87.2 (#7)
- Mixtral 8x22B: 22.9 (#275)

| Benchmark | GPT-6 Sol | Mixtral 8x22B |
|---|---|---|
| LMArena Math | 1402 | 1184 |
| FrontierMath (Tiers 1-3) | 89.8% | — |
| FrontierMath Tier 4 | 90% | — |
| OTIS Mock AIME 2024-2025 | 100% | — |
| ProofBench | 83% | — |
| Omni-MATH | — | 16.3% |
| MATH Level 5 | — | 24.2% |

## Knowledge

- GPT-6 Sol: 64.8 (#15)
- Mixtral 8x22B: 15.1 (#293)

| Benchmark | GPT-6 Sol | Mixtral 8x22B |
|---|---|---|
| GPQA Diamond | 94.3% | 34.1% |
| LMArena Expert | 1439 | 1113 |
| SimpleQA Verified | 60.7% | — |
| MMLU-Pro | — | 46% |
| Vectara Hallucination Rate | 6.5% | — |
| GPQA (HELM) | — | 33.4% |
| MMLU | — | 77.8% |

## Multimodal

- GPT-6 Sol: 47.6 (#10)
- Mixtral 8x22B: —

| Benchmark | GPT-6 Sol | Mixtral 8x22B |
|---|---|---|
| LMArena Vision | 1245 | — |
| Blueprint-Bench 2 | 36.9% | — |
| Furniture Assembly | 58.3% | — |

## Multilingual

- GPT-6 Sol: 50.5 (#118)
- Mixtral 8x22B: 32.8 (#255)

| Benchmark | GPT-6 Sol | Mixtral 8x22B |
|---|---|---|
| LMArena Non-English | 1385 | 1128 |
| LMArena Chinese | 1405 | 1116 |
| LMArena French | 1410 | 1166 |
| LMArena German | 1390 | 1141 |
| LMArena Japanese | 1385 | 1037 |
| LMArena Korean | 1341 | 1057 |
| LMArena Russian | 1401 | 1158 |
| LMArena Spanish | 1384 | 1151 |

## Instruction Following

- GPT-6 Sol: 74.5 (#94)
- Mixtral 8x22B: 57.7 (#266)

| Benchmark | GPT-6 Sol | Mixtral 8x22B |
|---|---|---|
| LMArena Instruction Following | 1412 | 1147 |
| IFEval | — | 72.4% |

## Long Context

- GPT-6 Sol: 43.1 (#108)
- Mixtral 8x22B: 34.7 (#247)

| Benchmark | GPT-6 Sol | Mixtral 8x22B |
|---|---|---|
| LMArena Longer Query | 1411 | 1144 |

## Writing & Preference

- GPT-6 Sol: 71.9 (#18)
- Mixtral 8x22B: 36.9 (#262)

| Benchmark | GPT-6 Sol | Mixtral 8x22B |
|---|---|---|
| LMArena Text | 1395 | 1162 |
| LMArena Creative Writing | 1378 | 1141 |
| LMArena Multi-Turn | 1412 | 1130 |
| EQ-Bench Creative Writing | 2125 | — |
| WildBench | — | 71.1% |

## FAQ

### Is GPT-6 Sol better than Mixtral 8x22B?

GPT-6 Sol is the stronger model overall, scoring 61.8 to 27.1 on the Noometry Index.

### Which is cheaper, GPT-6 Sol or Mixtral 8x22B?

Mixtral 8x22B is cheaper. It lists at $2 per million input tokens and $6 per million output tokens; GPT-6 Sol lists at $2 and $10.

### Is GPT-6 Sol or Mixtral 8x22B better for coding?

GPT-6 Sol scores higher on coding benchmarks: 60.1 versus 24.2 in the Noometry coding category.

### Which has the bigger context window?

GPT-6 Sol does, with 1.05M tokens against 64K.

### How many benchmarks do GPT-6 Sol and Mixtral 8x22B share?

20 benchmarks have published results for both models. GPT-6 Sol has 45 scored results on Noometry and Mixtral 8x22B has 34.