# DeepSeek-R1 vs Mistral Small 3.2

> DeepSeek-R1 is the stronger model overall, scoring 42.3 to 31.2 on the Noometry Index. Mistral Small 3.2 costs 6.9× less per token, which makes it the better buy when DeepSeek-R1's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-r1-vs-mistral-small-3-2
- Last updated: 2026-10-10
- Shared benchmarks: 5

## Summary

- They share 5 benchmarks with published results for both. DeepSeek-R1 scores higher in 4 categories and Mistral Small 3.2 in 0 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-R1 leads 44.5 to 26.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 66.4% for DeepSeek-R1 and 30.3% for Mistral Small 3.2.
- Mistral Small 3.2 is cheaper at $0.0938 / $0.25 per million input/output tokens, against $0.50 / $2.15 for DeepSeek-R1.
- Mistral Small 3.2 accepts more context: 256K tokens versus 164K.
- Mistral Small 3.2 has downloadable open weights; the other is API-only.

## Snapshot

| | DeepSeek-R1 | Mistral Small 3.2 |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 42.3 | 31.2 |
| Rank | 115 | 280 |
| Context | 164K | 256K |
| Input $/M | $0.50 | $0.0938 |
| Output $/M | $2.15 | $0.25 |
| Weights | Proprietary | Open |

## Coding

- DeepSeek-R1: 46.3 (#68)
- Mistral Small 3.2: —

| Benchmark | DeepSeek-R1 | Mistral Small 3.2 |
|---|---|---|
| Aider Polyglot | 71.4% | — |
| SciCode | 35.7% | — |
| WeirdML | 41.6% | — |
| LiveBench Coding | 66.7% | — |
| LMArena Coding | 1427 | — |
| ALE-Bench | 804.12 | — |
| AlgoTune | 1.7 | — |

## Agentic & Tool Use

- DeepSeek-R1: 30.7 (#75)
- Mistral Small 3.2: —

| Benchmark | DeepSeek-R1 | Mistral Small 3.2 |
|---|---|---|
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |

## Reasoning

- DeepSeek-R1: 18.6 (#278)
- Mistral Small 3.2: 18.1 (#287)

| Benchmark | DeepSeek-R1 | Mistral Small 3.2 |
|---|---|---|
| Kagi LLM Benchmark | 69.4% | 40.4% |
| Epoch Capabilities Index | 141.29 | 131.74 |
| ARC-AGI-2 | 1.3% | — |
| SimpleBench | 40.8% | — |
| ARC-AGI-1 | 21.2% | — |
| CritPt | 1.1% | — |
| Chess Puzzles | — | 1% |
| LiveBench Reasoning | 83.2% | — |
| LMArena Hard Prompts | 1416 | — |
| LiveBench Data Analysis | 69.8% | — |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |

## Math

- DeepSeek-R1: 43.8 (#79)
- Mistral Small 3.2: 26.3 (#260)

| Benchmark | DeepSeek-R1 | Mistral Small 3.2 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.4% | 30.3% |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| LMArena Math | 1400 | — |
| MATH Level 5 | 96.6% | — |

## Knowledge

- DeepSeek-R1: 44.5 (#87)
- Mistral Small 3.2: 26.7 (#256)

| Benchmark | DeepSeek-R1 | Mistral Small 3.2 |
|---|---|---|
| GPQA Diamond | 76.3% | 49.1% |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| Vectara Hallucination Rate | 11.3% | — |
| GPQA (HELM) | 66.6% | — |
| LMArena Expert | 1394 | — |

## Multilingual

- DeepSeek-R1: 52.4 (#85)
- Mistral Small 3.2: —

| Benchmark | DeepSeek-R1 | Mistral Small 3.2 |
|---|---|---|
| LMArena Non-English | 1412 | — |
| LMArena Chinese | 1442 | — |
| LMArena French | 1417 | — |
| LMArena German | 1404 | — |
| LMArena Japanese | 1391 | — |
| LMArena Korean | 1360 | — |
| LMArena Russian | 1423 | — |
| LMArena Spanish | 1411 | — |

## Instruction Following

- DeepSeek-R1: 72.0 (#143)
- Mistral Small 3.2: —

| Benchmark | DeepSeek-R1 | Mistral Small 3.2 |
|---|---|---|
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |
| LMArena Instruction Following | 1382 | — |

## Long Context

- DeepSeek-R1: 45.4 (#36)
- Mistral Small 3.2: —

| Benchmark | DeepSeek-R1 | Mistral Small 3.2 |
|---|---|---|
| Fiction.LiveBench | 75% | — |
| LMArena Longer Query | 1391 | — |

## Writing & Preference

- DeepSeek-R1: 61.4 (#88)
- Mistral Small 3.2: 45.0 (#224)

| Benchmark | DeepSeek-R1 | Mistral Small 3.2 |
|---|---|---|
| EQ-Bench Creative Writing | 1500 | 1255 |
| LMArena Text | 1428 | — |
| LMArena Creative Writing | 1405 | — |
| Short-Story Creative Writing | 83% | — |
| WildBench | 82.8% | — |
| LMArena Multi-Turn | 1405 | — |
| LiveBench Language | 48.5% | — |

## FAQ

### Is DeepSeek-R1 better than Mistral Small 3.2?

DeepSeek-R1 is the stronger model overall, scoring 42.3 to 31.2 on the Noometry Index. Mistral Small 3.2 costs 6.9× less per token, which makes it the better buy when DeepSeek-R1's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-R1 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-R1 lists at $0.50 and $2.15.

### Which has the bigger context window?

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

### How many benchmarks do DeepSeek-R1 and Mistral Small 3.2 share?

5 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and Mistral Small 3.2 has 6.