# DeepSeek-V3.1 vs Mistral Small

> DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 33.4 on the Noometry Index. Mistral Small costs 1.6× 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
- Last updated: 2026-10-11
- Shared benchmarks: 21

## Summary

- They share 21 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 7 categories and Mistral Small in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek-V3.1 leads 38.9 to 16.4.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 53.2% for DeepSeek-V3.1 and 37.8% for Mistral Small.
- Mistral Small is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.25 / $0.95 for DeepSeek-V3.1.
- Mistral Small accepts more context: 262K tokens versus 164K.

## Snapshot

| | DeepSeek-V3.1 | Mistral Small |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 42.8 | 33.4 |
| Rank | 108 | 243 |
| Context | 164K | 262K |
| Input $/M | $0.25 | $0.15 |
| Output $/M | $0.95 | $0.60 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.1: 40.3 (#144)
- Mistral Small: 34.0 (#247)

| Benchmark | DeepSeek-V3.1 | Mistral Small |
|---|---|---|
| LMArena Coding | 1417 | 1362 |
| SciCode | — | 26.5% |
| WeirdML | 38.4% | — |
| BigCodeBench Instruct | — | 36.1% |
| LiveBench Coding | — | 36.2% |
| BigCodeBench Complete | — | 46.6% |
| ALE-Bench | — | 497.62 |

## Agentic & Tool Use

- DeepSeek-V3.1: —
- Mistral Small: 28.1 (#93)

| Benchmark | DeepSeek-V3.1 | Mistral Small |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 37.1% |

## Reasoning

- DeepSeek-V3.1: 27.9 (#110)
- Mistral Small: 19.8 (#250)

| Benchmark | DeepSeek-V3.1 | Mistral Small |
|---|---|---|
| Kagi LLM Benchmark | 53.2% | 37.8% |
| LMArena Hard Prompts | 1417 | 1335 |
| DTBench | 82.7% | 70.9% |
| LMCA | 24.3% | 20.6% |
| SimpleBench | 40% | — |
| CritPt | — | 0% |
| LiveBench Reasoning | — | 44.8% |
| LiveBench Data Analysis | — | 53.7% |
| Epoch Capabilities Index | 139.92 | — |
| ForecastBench | 58 | — |
| LiveBench | — | 44% |

## Math

- DeepSeek-V3.1: 38.9 (#122)
- Mistral Small: 16.4 (#293)

| Benchmark | DeepSeek-V3.1 | Mistral Small |
|---|---|---|
| LMArena Math | 1420 | 1341 |
| OTIS Mock AIME 2024-2025 | — | 5.8% |
| LiveBench Math | — | 39.9% |
| MATH Level 5 | — | 46.8% |

## Knowledge

- DeepSeek-V3.1: 43.7 (#90)
- Mistral Small: 31.0 (#222)

| Benchmark | DeepSeek-V3.1 | Mistral Small |
|---|---|---|
| Vectara Hallucination Rate | 5.5% | 5.1% |
| LMArena Expert | 1405 | 1291 |
| GPQA Diamond | — | 47.5% |
| MMLU | — | 68.7% |

## Multimodal

- DeepSeek-V3.1: —
- Mistral Small: 33.5 (#96)

| Benchmark | DeepSeek-V3.1 | Mistral Small |
|---|---|---|
| LMArena Vision | — | 1142 |

## Multilingual

- DeepSeek-V3.1: 51.6 (#106)
- Mistral Small: 45.5 (#169)

| Benchmark | DeepSeek-V3.1 | Mistral Small |
|---|---|---|
| LMArena Non-English | 1400 | 1315 |
| LMArena Chinese | 1469 | 1340 |
| LMArena French | 1447 | 1337 |
| LMArena German | 1411 | 1340 |
| LMArena Japanese | 1378 | 1275 |
| LMArena Korean | 1337 | 1259 |
| LMArena Russian | 1405 | 1324 |
| LMArena Spanish | 1431 | 1346 |

## Instruction Following

- DeepSeek-V3.1: 73.9 (#110)
- Mistral Small: 66.4 (#209)

| Benchmark | DeepSeek-V3.1 | Mistral Small |
|---|---|---|
| LMArena Instruction Following | 1400 | 1310 |
| LiveBench Instruction Following | — | 63.7% |

## Long Context

- DeepSeek-V3.1: 36.3 (#232)
- Mistral Small: 40.4 (#156)

| Benchmark | DeepSeek-V3.1 | Mistral Small |
|---|---|---|
| LMArena Longer Query | 1422 | 1327 |
| Fiction.LiveBench | 52.8% | — |

## Writing & Preference

- DeepSeek-V3.1: 60.3 (#98)
- Mistral Small: 52.5 (#171)

| Benchmark | DeepSeek-V3.1 | Mistral Small |
|---|---|---|
| LMArena Text | 1420 | 1338 |
| LMArena Creative Writing | 1401 | 1305 |
| LMArena Multi-Turn | 1408 | 1344 |
| EQ-Bench Creative Writing | 1436 | — |
| LiveBench Language | — | 30.5% |

## FAQ

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

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 33.4 on the Noometry Index. Mistral Small costs 1.6× 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?

Mistral Small is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; DeepSeek-V3.1 lists at $0.25 and $0.95.

### Is DeepSeek-V3.1 or Mistral Small better for coding?

DeepSeek-V3.1 scores higher on coding benchmarks: 40.3 versus 34.0 in the Noometry coding category.

### Which has the bigger context window?

Mistral Small does, with 262K tokens against 164K.

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

21 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Mistral Small has 39.