# DeepSeek V4 Pro vs Mistral Small 3.2

> DeepSeek V4 Pro is the stronger model overall, scoring 54.2 to 30.8 on the Noometry Index. Mistral Small 3.2 costs 4.1× less per token, which makes it the better buy when DeepSeek V4 Pro's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v4-pro-vs-mistral-small-3-2
- Last updated: 2026-10-10
- Shared benchmarks: 6

## Summary

- They share 6 benchmarks with published results for both. DeepSeek V4 Pro 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 math, where DeepSeek V4 Pro leads 64.6 to 24.9.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 98.6% for DeepSeek V4 Pro 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 $0.43 / $0.87 for DeepSeek V4 Pro.
- DeepSeek V4 Pro accepts more context: 1M tokens versus 256K.

## Snapshot

| | DeepSeek V4 Pro | Mistral Small 3.2 |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 54.2 | 30.8 |
| Rank | 28 | 271 |
| Context | 1M | 256K |
| Input $/M | $0.43 | $0.0938 |
| Output $/M | $0.87 | $0.25 |
| Weights | Open | Open |

## Coding

- DeepSeek V4 Pro: 52.2 (#31)
- Mistral Small 3.2: —

| Benchmark | DeepSeek V4 Pro | Mistral Small 3.2 |
|---|---|---|
| SWE-bench Verified | 77.6% | — |
| FrontierCode | 28.6% | — |
| LMArena WebDev | 1582 | — |
| SciCode | 51% | — |
| WeirdML | 66.2% | — |
| LMArena Coding | 1470 | — |
| ALE-Bench | 1,403 | — |

## Agentic & Tool Use

- DeepSeek V4 Pro: 32.7 (#54)
- Mistral Small 3.2: —

| Benchmark | DeepSeek V4 Pro | Mistral Small 3.2 |
|---|---|---|
| APEX-Agents | 47.3% | — |
| Vending-Bench 2 | 3,285 | — |

## Reasoning

- DeepSeek V4 Pro: 56.4 (#22)
- Mistral Small 3.2: 17.5 (#282)

| Benchmark | DeepSeek V4 Pro | Mistral Small 3.2 |
|---|---|---|
| Kagi LLM Benchmark | 53.5% | 40.4% |
| Chess Puzzles | 47% | 1% |
| Epoch Capabilities Index | 155.39 | 131.73 |
| ARC-AGI-2 | 61.3% | — |
| NYT Connections (extended) | 91.3% | — |
| ARC-AGI-1 | 90.5% | — |
| CritPt | 18% | — |
| LMArena Hard Prompts | 1461 | — |
| Mystery Game Puzzles | 43% | — |
| DTBench | 93.9% | — |
| LMCA | 45.5% | — |
| Surface Evolver Bench | 40% | — |
| ForecastBench | 56.1 | — |

## Math

- DeepSeek V4 Pro: 64.6 (#24)
- Mistral Small 3.2: 24.9 (#251)

| Benchmark | DeepSeek V4 Pro | Mistral Small 3.2 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 98.6% | 30.3% |
| FrontierMath (Tiers 1-3) | 64.6% | — |
| FrontierMath Tier 4 | 26.8% | — |
| MathArena Final-Answer Competitions | 76.6% | — |
| ProofBench | 50% | — |
| LMArena Math | 1455 | — |

## Knowledge

- DeepSeek V4 Pro: 59.5 (#28)
- Mistral Small 3.2: 26.8 (#249)

| Benchmark | DeepSeek V4 Pro | Mistral Small 3.2 |
|---|---|---|
| GPQA Diamond | 91.7% | 49.1% |
| SimpleQA Verified | 52.9% | — |
| Vectara Hallucination Rate | 8.6% | — |
| LMArena Expert | 1463 | — |

## Multilingual

- DeepSeek V4 Pro: 54.5 (#44)
- Mistral Small 3.2: —

| Benchmark | DeepSeek V4 Pro | Mistral Small 3.2 |
|---|---|---|
| LMArena Non-English | 1438 | — |
| LMArena Chinese | 1486 | — |
| LMArena French | 1473 | — |
| LMArena German | 1458 | — |
| LMArena Japanese | 1448 | — |
| LMArena Korean | 1452 | — |
| LMArena Russian | 1452 | — |
| LMArena Spanish | 1457 | — |

## Instruction Following

- DeepSeek V4 Pro: 76.3 (#44)
- Mistral Small 3.2: —

| Benchmark | DeepSeek V4 Pro | Mistral Small 3.2 |
|---|---|---|
| LMArena Instruction Following | 1448 | — |

## Long Context

- DeepSeek V4 Pro: 45.0 (#49)
- Mistral Small 3.2: —

| Benchmark | DeepSeek V4 Pro | Mistral Small 3.2 |
|---|---|---|
| CL-bench Life | 13.5% | — |
| LMArena Longer Query | 1459 | — |

## Writing & Preference

- DeepSeek V4 Pro: 65.6 (#45)
- Mistral Small 3.2: 45.1 (#219)

| Benchmark | DeepSeek V4 Pro | Mistral Small 3.2 |
|---|---|---|
| EQ-Bench Creative Writing | 1553 | 1255 |
| LMArena Text | 1451 | — |
| LMArena Creative Writing | 1446 | — |
| EQ-Bench 4 | 1166 | — |
| LMArena Multi-Turn | 1467 | — |

## FAQ

### Is DeepSeek V4 Pro better than Mistral Small 3.2?

DeepSeek V4 Pro is the stronger model overall, scoring 54.2 to 30.8 on the Noometry Index. Mistral Small 3.2 costs 4.1× less per token, which makes it the better buy when DeepSeek V4 Pro's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek V4 Pro 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 V4 Pro lists at $0.43 and $0.87.

### Which has the bigger context window?

DeepSeek V4 Pro does, with 1M tokens against 256K.

### How many benchmarks do DeepSeek V4 Pro and Mistral Small 3.2 share?

6 benchmarks have published results for both models. DeepSeek V4 Pro has 48 scored results on Noometry and Mistral Small 3.2 has 6.