# DeepSeek-V3.1 vs Mistral Small 3.2

> DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 30.8 on the Noometry Index. Mistral Small 3.2 costs 3.2× 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-small-3-2
- Last updated: 2026-10-10
- Shared benchmarks: 3

## Summary

- They share 3 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 4 categories and Mistral Small 3.2 in 0 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.1 leads 43.0 to 26.8.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 53.2% for DeepSeek-V3.1 and 40.4% for Mistral Small 3.2.
- Mistral Small 3.2 is cheaper at $0.0938 / $0.25 per million input/output tokens, against $0.25 / $0.95 for DeepSeek-V3.1.
- Mistral Small 3.2 accepts more context: 256K tokens versus 164K.

## Snapshot

| | DeepSeek-V3.1 | Mistral Small 3.2 |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 42.2 | 30.8 |
| Rank | 106 | 271 |
| Context | 164K | 256K |
| Input $/M | $0.25 | $0.0938 |
| Output $/M | $0.95 | $0.25 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.1: 39.8 (#140)
- Mistral Small 3.2: —

| Benchmark | DeepSeek-V3.1 | Mistral Small 3.2 |
|---|---|---|
| WeirdML | 38.4% | — |
| LMArena Coding | 1417 | — |

## Reasoning

- DeepSeek-V3.1: 27.3 (#109)
- Mistral Small 3.2: 17.5 (#282)

| Benchmark | DeepSeek-V3.1 | Mistral Small 3.2 |
|---|---|---|
| Kagi LLM Benchmark | 53.2% | 40.4% |
| Epoch Capabilities Index | 139.92 | 131.73 |
| SimpleBench | 40% | — |
| Chess Puzzles | — | 1% |
| LMArena Hard Prompts | 1417 | — |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| ForecastBench | 58 | — |

## Math

- DeepSeek-V3.1: 36.1 (#125)
- Mistral Small 3.2: 24.9 (#251)

| Benchmark | DeepSeek-V3.1 | Mistral Small 3.2 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 30.3% |
| LMArena Math | 1420 | — |

## Knowledge

- DeepSeek-V3.1: 43.0 (#86)
- Mistral Small 3.2: 26.8 (#249)

| Benchmark | DeepSeek-V3.1 | Mistral Small 3.2 |
|---|---|---|
| GPQA Diamond | — | 49.1% |
| Vectara Hallucination Rate | 5.5% | — |
| LMArena Expert | 1403 | — |

## Multilingual

- DeepSeek-V3.1: 51.8 (#100)
- Mistral Small 3.2: —

| Benchmark | DeepSeek-V3.1 | Mistral Small 3.2 |
|---|---|---|
| LMArena Non-English | 1400 | — |
| LMArena Chinese | 1470 | — |
| LMArena French | 1447 | — |
| LMArena German | 1412 | — |
| LMArena Japanese | 1374 | — |
| LMArena Korean | 1337 | — |
| LMArena Russian | 1405 | — |
| LMArena Spanish | 1429 | — |

## Instruction Following

- DeepSeek-V3.1: 74.1 (#105)
- Mistral Small 3.2: —

| Benchmark | DeepSeek-V3.1 | Mistral Small 3.2 |
|---|---|---|
| LMArena Instruction Following | 1400 | — |

## Long Context

- DeepSeek-V3.1: 36.3 (#227)
- Mistral Small 3.2: —

| Benchmark | DeepSeek-V3.1 | Mistral Small 3.2 |
|---|---|---|
| Fiction.LiveBench | 52.8% | — |
| LMArena Longer Query | 1422 | — |

## Writing & Preference

- DeepSeek-V3.1: 60.5 (#93)
- Mistral Small 3.2: 45.1 (#219)

| Benchmark | DeepSeek-V3.1 | Mistral Small 3.2 |
|---|---|---|
| EQ-Bench Creative Writing | 1436 | 1255 |
| LMArena Text | 1420 | — |
| LMArena Creative Writing | 1401 | — |
| LMArena Multi-Turn | 1408 | — |

## FAQ

### Is DeepSeek-V3.1 better than Mistral Small 3.2?

DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 30.8 on the Noometry Index. Mistral Small 3.2 costs 3.2× 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 Small 3.2?

Mistral Small 3.2 is cheaper. It lists at $0.0938 per million input tokens and $0.25 per million output tokens; DeepSeek-V3.1 lists at $0.25 and $0.95.

### Which has the bigger context window?

Mistral Small 3.2 does, with 256K tokens against 164K.

### How many benchmarks do DeepSeek-V3.1 and Mistral Small 3.2 share?

3 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Mistral Small 3.2 has 6.