# DeepSeek-V3.1 vs Mixtral 8x7B

> DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 26.8 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v3-1-vs-mixtral-8x7b
- Last updated: 2026-10-10
- Shared benchmarks: 20

## Summary

- They share 20 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 8 categories and Mixtral 8x7B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.1 leads 43.0 to 11.0.
- The biggest single-benchmark swing is DTBench: 82.7% for DeepSeek-V3.1 and 49.6% for Mixtral 8x7B.
- DeepSeek-V3.1 is cheaper at $0.25 / $0.95 per million input/output tokens, against $0.70 / $0.70 for Mixtral 8x7B.
- DeepSeek-V3.1 accepts more context: 164K tokens versus 32K.

## Snapshot

| | DeepSeek-V3.1 | Mixtral 8x7B |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 42.2 | 26.8 |
| Rank | 106 | 328 |
| Context | 164K | 32K |
| Input $/M | $0.25 | $0.70 |
| Output $/M | $0.95 | $0.70 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.1: 39.8 (#140)
- Mixtral 8x7B: 32.2 (#265)

| Benchmark | DeepSeek-V3.1 | Mixtral 8x7B |
|---|---|---|
| LMArena Coding | 1417 | 1127 |
| WeirdML | 38.4% | — |
| HumanEval+ | — | 39.6% |
| MBPP+ | — | 49.7% |

## Reasoning

- DeepSeek-V3.1: 27.3 (#109)
- Mixtral 8x7B: 17.4 (#283)

| Benchmark | DeepSeek-V3.1 | Mixtral 8x7B |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1115 |
| DTBench | 82.7% | 49.6% |
| Epoch Capabilities Index | 139.92 | 118.38 |
| ForecastBench | 58 | 56.3 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| LMCA | 24.3% | — |
| Adversarial NLI | — | 55.2% |
| HellaSwag | — | 86.7% |
| PIQA | — | 83.6% |
| WinoGrande | — | 77.2% |

## Math

- DeepSeek-V3.1: 36.1 (#125)
- Mixtral 8x7B: 17.2 (#284)

| Benchmark | DeepSeek-V3.1 | Mixtral 8x7B |
|---|---|---|
| LMArena Math | 1420 | 1147 |
| Omni-MATH | — | 10.5% |
| MATH Level 5 | — | 10% |
| GSM8K | — | 74.4% |

## Knowledge

- DeepSeek-V3.1: 43.0 (#86)
- Mixtral 8x7B: 11.0 (#296)

| Benchmark | DeepSeek-V3.1 | Mixtral 8x7B |
|---|---|---|
| LMArena Expert | 1403 | 1088 |
| GPQA Diamond | — | 30.6% |
| MMLU-Pro | — | 33.5% |
| Vectara Hallucination Rate | 5.5% | — |
| GPQA (HELM) | — | 29.6% |
| ARC (AI2) Challenge | — | 87.3% |
| MMLU | — | 70.6% |
| OpenBookQA | — | 85.8% |
| TriviaQA | — | 82.2% |

## Multilingual

- DeepSeek-V3.1: 51.8 (#100)
- Mixtral 8x7B: 29.8 (#261)

| Benchmark | DeepSeek-V3.1 | Mixtral 8x7B |
|---|---|---|
| LMArena Non-English | 1400 | 1077 |
| LMArena Chinese | 1470 | 1055 |
| LMArena French | 1447 | 1165 |
| LMArena German | 1412 | 1114 |
| LMArena Japanese | 1374 | 931 |
| LMArena Korean | 1337 | 968 |
| LMArena Russian | 1405 | 1090 |
| LMArena Spanish | 1429 | 1110 |

## Instruction Following

- DeepSeek-V3.1: 74.1 (#105)
- Mixtral 8x7B: 51.2 (#292)

| Benchmark | DeepSeek-V3.1 | Mixtral 8x7B |
|---|---|---|
| LMArena Instruction Following | 1400 | 1110 |
| IFEval | — | 57.5% |

## Long Context

- DeepSeek-V3.1: 36.3 (#227)
- Mixtral 8x7B: 33.4 (#255)

| Benchmark | DeepSeek-V3.1 | Mixtral 8x7B |
|---|---|---|
| LMArena Longer Query | 1422 | 1103 |
| Fiction.LiveBench | 52.8% | — |

## Writing & Preference

- DeepSeek-V3.1: 60.5 (#93)
- Mixtral 8x7B: 34.4 (#265)

| Benchmark | DeepSeek-V3.1 | Mixtral 8x7B |
|---|---|---|
| LMArena Text | 1420 | 1132 |
| LMArena Creative Writing | 1401 | 1109 |
| LMArena Multi-Turn | 1408 | 1115 |
| EQ-Bench Creative Writing | 1436 | — |
| WildBench | — | 67.3% |

## FAQ

### Is DeepSeek-V3.1 better than Mixtral 8x7B?

DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 26.8 on the Noometry Index.

### Which is cheaper, DeepSeek-V3.1 or Mixtral 8x7B?

DeepSeek-V3.1 is cheaper. It lists at $0.25 per million input tokens and $0.95 per million output tokens; Mixtral 8x7B lists at $0.70 and $0.70.

### Is DeepSeek-V3.1 or Mixtral 8x7B better for coding?

DeepSeek-V3.1 scores higher on coding benchmarks: 39.8 versus 32.2 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek-V3.1 does, with 164K tokens against 32K.

### How many benchmarks do DeepSeek-V3.1 and Mixtral 8x7B share?

20 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Mixtral 8x7B has 38.