# GPT-5.6 Sol vs Mixtral 8x7B

> GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 26.8 on the Noometry Index. Mixtral 8x7B costs 11× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gpt-5-6-sol-vs-mixtral-8x7b
- Last updated: 2026-10-10
- Shared benchmarks: 20

## Summary

- They share 20 benchmarks with published results for both. GPT-5.6 Sol scores higher in 8 categories and Mixtral 8x7B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Sol leads 85.5 to 17.2.
- The biggest single-benchmark swing is GPQA Diamond: 93.5% for GPT-5.6 Sol and 30.6% for Mixtral 8x7B.
- Mixtral 8x7B is cheaper at $0.70 / $0.70 per million input/output tokens, against $4 / $20 for GPT-5.6 Sol.
- GPT-5.6 Sol accepts more context: 1.05M tokens versus 32K.
- Mixtral 8x7B has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-5.6 Sol | Mixtral 8x7B |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 64.4 | 26.8 |
| Rank | 6 | 328 |
| Context | 1.05M | 32K |
| Input $/M | $4 | $0.70 |
| Output $/M | $20 | $0.70 |
| Weights | Proprietary | Open |

## Coding

- GPT-5.6 Sol: 61.1 (#7)
- Mixtral 8x7B: 32.2 (#265)

| Benchmark | GPT-5.6 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Coding | 1493 | 1127 |
| DeepSWE | 72.7% | — |
| FrontierCode | 47.5% | — |
| CursorBench | 41.7% | — |
| LMArena WebDev | 1619 | — |
| FrontierSWE | 32.2% | — |
| SciCode | 57.1% | — |
| WeirdML | 89.4% | — |
| MirrorCode | 20% | — |
| ALE-Bench | 2,177 | — |
| HumanEval+ | — | 39.6% |
| MBPP+ | — | 49.7% |

## Agentic & Tool Use

- GPT-5.6 Sol: 50.2 (#7)
- Mixtral 8x7B: —

| Benchmark | GPT-5.6 Sol | Mixtral 8x7B |
|---|---|---|
| APEX-Agents | 51.4% | — |
| OSWorld 2.0 | 27.3% | — |
| τ²-bench Banking | 46.9% | — |
| PostTrainBench | 36.2% | — |
| BALROG | 60% | — |
| GBAEval | 52.6% | — |
| GDP.pdf | 30.7% | — |
| LMArena Search | 1257 | — |
| Vending-Bench 2 | 9,619 | — |

## Reasoning

- GPT-5.6 Sol: 74.7 (#6)
- Mixtral 8x7B: 17.4 (#283)

| Benchmark | GPT-5.6 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Hard Prompts | 1481 | 1115 |
| DTBench | 96% | 49.6% |
| Epoch Capabilities Index | 161.99 | 118.38 |
| ARC-AGI-2 | 92.5% | — |
| SimpleBench | 71.7% | — |
| Kagi LLM Benchmark | 67% | — |
| NYT Connections (extended) | 93.8% | — |
| ARC-AGI-1 | 97.5% | — |
| CritPt | 32.3% | — |
| Chess Puzzles | 64% | — |
| EnigmaEval | 37.1% | — |
| EBR-Bench | 44.8% | — |
| Mystery Game Puzzles | 58% | — |
| LMCA | 59.2% | — |
| Surface Evolver Bench | 93.1% | — |
| Adversarial NLI | — | 55.2% |
| Bench to the Future 3 | 0.14 | — |
| ForecastBench | — | 56.3 |
| HellaSwag | — | 86.7% |
| PIQA | — | 83.6% |
| WinoGrande | — | 77.2% |

## Math

- GPT-5.6 Sol: 85.5 (#7)
- Mixtral 8x7B: 17.2 (#284)

| Benchmark | GPT-5.6 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Math | 1472 | 1147 |
| FrontierMath (Tiers 1-3) | 89.1% | — |
| FrontierMath Tier 4 | 82.9% | — |
| OTIS Mock AIME 2024-2025 | 100% | — |
| ProofBench | 83% | — |
| Omni-MATH | — | 10.5% |
| MATH Level 5 | — | 10% |
| FrontierMath Erdős | 0% | — |
| GSM8K | — | 74.4% |

## Knowledge

- GPT-5.6 Sol: 64.3 (#16)
- Mixtral 8x7B: 11.0 (#296)

| Benchmark | GPT-5.6 Sol | Mixtral 8x7B |
|---|---|---|
| GPQA Diamond | 93.5% | 30.6% |
| LMArena Expert | 1517 | 1088 |
| SimpleQA Verified | 69.7% | — |
| MMLU-Pro | — | 33.5% |
| Vectara Hallucination Rate | 12.4% | — |
| GPQA (HELM) | — | 29.6% |
| ARC (AI2) Challenge | — | 87.3% |
| MMLU | — | 70.6% |
| OpenBookQA | — | 85.8% |
| TriviaQA | — | 82.2% |

## Multimodal

- GPT-5.6 Sol: 48.9 (#8)
- Mixtral 8x7B: —

| Benchmark | GPT-5.6 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Vision | 1281 | — |
| Blueprint-Bench 2 | 33.6% | — |
| Furniture Assembly | 56.7% | — |
| LMArena Document | 1483 | — |

## Multilingual

- GPT-5.6 Sol: 55.4 (#32)
- Mixtral 8x7B: 29.8 (#261)

| Benchmark | GPT-5.6 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Non-English | 1450 | 1077 |
| LMArena Chinese | 1523 | 1055 |
| LMArena French | 1475 | 1165 |
| LMArena German | 1476 | 1114 |
| LMArena Japanese | 1475 | 931 |
| LMArena Korean | 1434 | 968 |
| LMArena Russian | 1466 | 1090 |
| LMArena Spanish | 1449 | 1110 |

## Instruction Following

- GPT-5.6 Sol: 77.8 (#16)
- Mixtral 8x7B: 51.2 (#292)

| Benchmark | GPT-5.6 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Instruction Following | 1480 | 1110 |
| IFEval | — | 57.5% |

## Long Context

- GPT-5.6 Sol: 45.3 (#42)
- Mixtral 8x7B: 33.4 (#255)

| Benchmark | GPT-5.6 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Longer Query | 1479 | 1103 |

## Writing & Preference

- GPT-5.6 Sol: 73.5 (#12)
- Mixtral 8x7B: 34.4 (#265)

| Benchmark | GPT-5.6 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Text | 1456 | 1132 |
| LMArena Creative Writing | 1450 | 1109 |
| LMArena Multi-Turn | 1456 | 1115 |
| EQ-Bench Creative Writing | 1972 | — |
| WildBench | — | 67.3% |
| EQ-Bench 4 | 1250 | — |

## FAQ

### Is GPT-5.6 Sol better than Mixtral 8x7B?

GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 26.8 on the Noometry Index. Mixtral 8x7B costs 11× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.

### Which is cheaper, GPT-5.6 Sol or Mixtral 8x7B?

Mixtral 8x7B is cheaper. It lists at $0.70 per million input tokens and $0.70 per million output tokens; GPT-5.6 Sol lists at $4 and $20.

### Is GPT-5.6 Sol or Mixtral 8x7B better for coding?

GPT-5.6 Sol scores higher on coding benchmarks: 61.1 versus 32.2 in the Noometry coding category.

### Which has the bigger context window?

GPT-5.6 Sol does, with 1.05M tokens against 32K.

### How many benchmarks do GPT-5.6 Sol and Mixtral 8x7B share?

20 benchmarks have published results for both models. GPT-5.6 Sol has 64 scored results on Noometry and Mixtral 8x7B has 38.