# DeepSeek-V3.1 vs Mistral Nemo

> DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 26.4 on the Noometry Index. Mistral Nemo costs 2.8× less per token, which makes it the better buy when DeepSeek-V3.1's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v3-1-vs-mistral-nemo
- Last updated: 2026-10-12
- Shared benchmarks: 3

## Summary

- They share 3 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 4 categories and Mistral Nemo in 0 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.1 leads 60.3 to 28.5.
- The biggest single-benchmark swing is DTBench: 82.7% for DeepSeek-V3.1 and 48.6% for Mistral Nemo.
- Mistral Nemo is cheaper at $0.15 / $0.15 per million input/output tokens, against $0.25 / $0.95 for DeepSeek-V3.1.
- DeepSeek-V3.1 accepts more context: 164K tokens versus 128K.

## Snapshot

| | DeepSeek-V3.1 | Mistral Nemo |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 42.8 | 26.4 |
| Rank | 108 | 337 |
| Context | 164K | 128K |
| Input $/M | $0.25 | $0.15 |
| Output $/M | $0.95 | $0.15 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.1: 40.3 (#144)
- Mistral Nemo: —

| Benchmark | DeepSeek-V3.1 | Mistral Nemo |
|---|---|---|
| WeirdML | 38.4% | — |
| LMArena Coding | 1417 | — |

## Agentic & Tool Use

- DeepSeek-V3.1: —
- Mistral Nemo: 23.5 (#125)

| Benchmark | DeepSeek-V3.1 | Mistral Nemo |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 27.6% |
| BALROG | — | 17.6% |

## Reasoning

- DeepSeek-V3.1: 27.9 (#110)
- Mistral Nemo: 20.7 (#232)

| Benchmark | DeepSeek-V3.1 | Mistral Nemo |
|---|---|---|
| DTBench | 82.7% | 48.6% |
| Epoch Capabilities Index | 139.92 | 118.68 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| LMArena Hard Prompts | 1417 | — |
| LMCA | 24.3% | — |
| ForecastBench | 58 | — |
| PIQA | — | 83.5% |

## Math

- DeepSeek-V3.1: 38.9 (#122)
- Mistral Nemo: 25.5 (#268)

| Benchmark | DeepSeek-V3.1 | Mistral Nemo |
|---|---|---|
| LMArena Math | 1420 | — |
| MATH Level 5 | — | 10.8% |
| GSM8K | — | 84.2% |

## Knowledge

- DeepSeek-V3.1: 43.7 (#90)
- Mistral Nemo: 12.3 (#298)

| Benchmark | DeepSeek-V3.1 | Mistral Nemo |
|---|---|---|
| GPQA Diamond | — | 29.9% |
| Vectara Hallucination Rate | 5.5% | — |
| LMArena Expert | 1405 | — |
| BoolQ | — | 82.5% |

## Multilingual

- DeepSeek-V3.1: 51.6 (#106)
- Mistral Nemo: —

| Benchmark | DeepSeek-V3.1 | Mistral Nemo |
|---|---|---|
| LMArena Non-English | 1400 | — |
| LMArena Chinese | 1469 | — |
| LMArena French | 1447 | — |
| LMArena German | 1411 | — |
| LMArena Japanese | 1378 | — |
| LMArena Korean | 1337 | — |
| LMArena Russian | 1405 | — |
| LMArena Spanish | 1431 | — |

## Instruction Following

- DeepSeek-V3.1: 73.9 (#110)
- Mistral Nemo: —

| Benchmark | DeepSeek-V3.1 | Mistral Nemo |
|---|---|---|
| LMArena Instruction Following | 1400 | — |

## Long Context

- DeepSeek-V3.1: 36.3 (#232)
- Mistral Nemo: —

| Benchmark | DeepSeek-V3.1 | Mistral Nemo |
|---|---|---|
| Fiction.LiveBench | 52.8% | — |
| LMArena Longer Query | 1422 | — |

## Writing & Preference

- DeepSeek-V3.1: 60.3 (#98)
- Mistral Nemo: 28.5 (#296)

| Benchmark | DeepSeek-V3.1 | Mistral Nemo |
|---|---|---|
| EQ-Bench Creative Writing | 1436 | 881 |
| LMArena Text | 1420 | — |
| LMArena Creative Writing | 1401 | — |
| LMArena Multi-Turn | 1408 | — |

## FAQ

### Is DeepSeek-V3.1 better than Mistral Nemo?

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 26.4 on the Noometry Index. Mistral Nemo costs 2.8× less per token, which makes it the better buy when DeepSeek-V3.1's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-V3.1 or Mistral Nemo?

Mistral Nemo is cheaper. It lists at $0.15 per million input tokens and $0.15 per million output tokens; DeepSeek-V3.1 lists at $0.25 and $0.95.

### Which has the bigger context window?

DeepSeek-V3.1 does, with 164K tokens against 128K.

### How many benchmarks do DeepSeek-V3.1 and Mistral Nemo share?

3 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Mistral Nemo has 10.