# DeepSeek-V3.1-Terminus vs Mixtral 8x7B

> DeepSeek-V3.1-Terminus is the stronger model overall, scoring 42.6 to 26.8 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v3-1-terminus-vs-mixtral-8x7b
- Last updated: 2026-10-10
- Shared benchmarks: 11

## Summary

- They share 11 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 7 categories and Mixtral 8x7B in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.1-Terminus leads 61.3 to 34.4.
- The biggest single-benchmark swing is DTBench: 81.3% for DeepSeek-V3.1-Terminus and 49.6% for Mixtral 8x7B.
- DeepSeek-V3.1-Terminus is cheaper at $0.30 / $1 per million input/output tokens, against $0.70 / $0.70 for Mixtral 8x7B.
- DeepSeek-V3.1-Terminus accepts more context: 164K tokens versus 32K.

## Snapshot

| | DeepSeek-V3.1-Terminus | Mixtral 8x7B |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 42.6 | 26.8 |
| Rank | 96 | 328 |
| Context | 164K | 32K |
| Input $/M | $0.30 | $0.70 |
| Output $/M | $1 | $0.70 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.1-Terminus: 41.5 (#114)
- Mixtral 8x7B: 32.2 (#265)

| Benchmark | DeepSeek-V3.1-Terminus | Mixtral 8x7B |
|---|---|---|
| LMArena Coding | 1426 | 1127 |
| SciCode | 40.6% | — |
| ALE-Bench | 745.17 | — |
| HumanEval+ | — | 39.6% |
| MBPP+ | — | 49.7% |

## Reasoning

- DeepSeek-V3.1-Terminus: 25.7 (#126)
- Mixtral 8x7B: 17.4 (#283)

| Benchmark | DeepSeek-V3.1-Terminus | Mixtral 8x7B |
|---|---|---|
| LMArena Hard Prompts | 1427 | 1115 |
| DTBench | 81.3% | 49.6% |
| Kagi LLM Benchmark | 57.4% | — |
| CritPt | 1.7% | — |
| LMCA | 28.6% | — |
| Adversarial NLI | — | 55.2% |
| Epoch Capabilities Index | — | 118.38 |
| ForecastBench | — | 56.3 |
| HellaSwag | — | 86.7% |
| PIQA | — | 83.6% |
| WinoGrande | — | 77.2% |

## Math

- DeepSeek-V3.1-Terminus: 35.7 (#138)
- Mixtral 8x7B: 17.2 (#284)

| Benchmark | DeepSeek-V3.1-Terminus | Mixtral 8x7B |
|---|---|---|
| LMArena Math | 1402 | 1147 |
| Omni-MATH | — | 10.5% |
| MATH Level 5 | — | 10% |
| GSM8K | — | 74.4% |

## Knowledge

- DeepSeek-V3.1-Terminus: —
- Mixtral 8x7B: 11.0 (#296)

| Benchmark | DeepSeek-V3.1-Terminus | 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-V3.1-Terminus: 52.3 (#87)
- Mixtral 8x7B: 29.8 (#261)

| Benchmark | DeepSeek-V3.1-Terminus | Mixtral 8x7B |
|---|---|---|
| LMArena Non-English | 1407 | 1077 |
| LMArena Russian | 1434 | 1090 |
| LMArena Chinese | — | 1055 |
| LMArena French | — | 1165 |
| LMArena German | — | 1114 |
| LMArena Japanese | — | 931 |
| LMArena Korean | — | 968 |
| LMArena Spanish | — | 1110 |

## Instruction Following

- DeepSeek-V3.1-Terminus: 74.4 (#96)
- Mixtral 8x7B: 51.2 (#292)

| Benchmark | DeepSeek-V3.1-Terminus | Mixtral 8x7B |
|---|---|---|
| LMArena Instruction Following | 1406 | 1110 |
| IFEval | — | 57.5% |

## Long Context

- DeepSeek-V3.1-Terminus: 43.5 (#88)
- Mixtral 8x7B: 33.4 (#255)

| Benchmark | DeepSeek-V3.1-Terminus | Mixtral 8x7B |
|---|---|---|
| LMArena Longer Query | 1426 | 1103 |

## Writing & Preference

- DeepSeek-V3.1-Terminus: 61.3 (#87)
- Mixtral 8x7B: 34.4 (#265)

| Benchmark | DeepSeek-V3.1-Terminus | Mixtral 8x7B |
|---|---|---|
| LMArena Text | 1420 | 1132 |
| LMArena Creative Writing | 1404 | 1109 |
| LMArena Multi-Turn | 1411 | 1115 |
| WildBench | — | 67.3% |

## FAQ

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

DeepSeek-V3.1-Terminus is the stronger model overall, scoring 42.6 to 26.8 on the Noometry Index.

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

DeepSeek-V3.1-Terminus is cheaper. It lists at $0.30 per million input tokens and $1 per million output tokens; Mixtral 8x7B lists at $0.70 and $0.70.

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

DeepSeek-V3.1-Terminus scores higher on coding benchmarks: 41.5 versus 32.2 in the Noometry coding category.

### Which has the bigger context window?

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

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

11 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and Mixtral 8x7B has 38.