# DeepSeek-V3.2-Speciale vs Mixtral 8x7B

> DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.1 to 26.8 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v3-2-speciale-vs-mixtral-8x7b
- Last updated: 2026-10-10
- Shared benchmarks: 0

## Summary

- The widest gap is in reasoning, where DeepSeek-V3.2-Speciale leads 32.1 to 17.4.
- Mixtral 8x7B is cheaper at $0.70 / $0.70 per million input/output tokens, against $0.58 / $1.68 for DeepSeek-V3.2-Speciale.
- DeepSeek-V3.2-Speciale accepts more context: 128K tokens versus 32K.

## Snapshot

| | DeepSeek-V3.2-Speciale | Mixtral 8x7B |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 39.1 | 26.8 |
| Rank | 159 | 328 |
| Context | 128K | 32K |
| Input $/M | $0.58 | $0.70 |
| Output $/M | $1.68 | $0.70 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.2-Speciale: 39.9 (#136)
- Mixtral 8x7B: 32.2 (#265)

| Benchmark | DeepSeek-V3.2-Speciale | Mixtral 8x7B |
|---|---|---|
| WeirdML | 46.7% | — |
| LMArena Coding | — | 1127 |
| HumanEval+ | — | 39.6% |
| MBPP+ | — | 49.7% |

## Reasoning

- DeepSeek-V3.2-Speciale: 32.1 (#65)
- Mixtral 8x7B: 17.4 (#283)

| Benchmark | DeepSeek-V3.2-Speciale | Mixtral 8x7B |
|---|---|---|
| SimpleBench | 52.6% | — |
| LMArena Hard Prompts | — | 1115 |
| DTBench | — | 49.6% |
| Adversarial NLI | — | 55.2% |
| Epoch Capabilities Index | — | 118.38 |
| ForecastBench | — | 56.3 |
| HellaSwag | — | 86.7% |
| PIQA | — | 83.6% |
| WinoGrande | — | 77.2% |

## Math

- DeepSeek-V3.2-Speciale: —
- Mixtral 8x7B: 17.2 (#284)

| Benchmark | DeepSeek-V3.2-Speciale | Mixtral 8x7B |
|---|---|---|
| Omni-MATH | — | 10.5% |
| LMArena Math | — | 1147 |
| MATH Level 5 | — | 10% |
| GSM8K | — | 74.4% |

## Knowledge

- DeepSeek-V3.2-Speciale: —
- Mixtral 8x7B: 11.0 (#296)

| Benchmark | DeepSeek-V3.2-Speciale | Mixtral 8x7B |
|---|---|---|
| GPQA Diamond | — | 30.6% |
| MMLU-Pro | — | 33.5% |
| GPQA (HELM) | — | 29.6% |
| LMArena Expert | — | 1088 |
| ARC (AI2) Challenge | — | 87.3% |
| MMLU | — | 70.6% |
| OpenBookQA | — | 85.8% |
| TriviaQA | — | 82.2% |

## Multilingual

- DeepSeek-V3.2-Speciale: —
- Mixtral 8x7B: 29.8 (#261)

| Benchmark | DeepSeek-V3.2-Speciale | Mixtral 8x7B |
|---|---|---|
| LMArena Non-English | — | 1077 |
| LMArena Chinese | — | 1055 |
| LMArena French | — | 1165 |
| LMArena German | — | 1114 |
| LMArena Japanese | — | 931 |
| LMArena Korean | — | 968 |
| LMArena Russian | — | 1090 |
| LMArena Spanish | — | 1110 |

## Instruction Following

- DeepSeek-V3.2-Speciale: —
- Mixtral 8x7B: 51.2 (#292)

| Benchmark | DeepSeek-V3.2-Speciale | Mixtral 8x7B |
|---|---|---|
| IFEval | — | 57.5% |
| LMArena Instruction Following | — | 1110 |

## Long Context

- DeepSeek-V3.2-Speciale: —
- Mixtral 8x7B: 33.4 (#255)

| Benchmark | DeepSeek-V3.2-Speciale | Mixtral 8x7B |
|---|---|---|
| LMArena Longer Query | — | 1103 |

## Writing & Preference

- DeepSeek-V3.2-Speciale: 46.1 (#217)
- Mixtral 8x7B: 34.4 (#265)

| Benchmark | DeepSeek-V3.2-Speciale | Mixtral 8x7B |
|---|---|---|
| LMArena Text | — | 1132 |
| LMArena Creative Writing | — | 1109 |
| EQ-Bench Creative Writing | 1276 | — |
| WildBench | — | 67.3% |
| LMArena Multi-Turn | — | 1115 |

## FAQ

### Is DeepSeek-V3.2-Speciale better than Mixtral 8x7B?

DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.1 to 26.8 on the Noometry Index.

### Which is cheaper, DeepSeek-V3.2-Speciale or Mixtral 8x7B?

Mixtral 8x7B is cheaper. It lists at $0.70 per million input tokens and $0.70 per million output tokens; DeepSeek-V3.2-Speciale lists at $0.58 and $1.68.

### Is DeepSeek-V3.2-Speciale or Mixtral 8x7B better for coding?

DeepSeek-V3.2-Speciale scores higher on coding benchmarks: 39.9 versus 32.2 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek-V3.2-Speciale does, with 128K tokens against 32K.

### How many benchmarks do DeepSeek-V3.2-Speciale and Mixtral 8x7B share?

0 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and Mixtral 8x7B has 38.