# DeepSeek-V3.2-Exp vs Mistral Medium

> DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 36.3 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v3-2-exp-vs-mistral-medium
- Last updated: 2026-10-11
- Shared benchmarks: 29

## Summary

- They share 29 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 8 categories and Mistral Medium in 1 category; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.2-Exp leads 51.7 to 25.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 87.8% for DeepSeek-V3.2-Exp and 32.2% for Mistral Medium.
- DeepSeek-V3.2-Exp is cheaper at $0.26 / $0.38 per million input/output tokens, against $1.50 / $7.50 for Mistral Medium.
- Mistral Medium accepts more context: 262K tokens versus 164K.

## Snapshot

| | DeepSeek-V3.2-Exp | Mistral Medium |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 44.3 | 36.3 |
| Rank | 78 | 218 |
| Context | 164K | 262K |
| Input $/M | $0.26 | $1.50 |
| Output $/M | $0.38 | $7.50 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.2-Exp: 46.5 (#65)
- Mistral Medium: 34.2 (#243)

| Benchmark | DeepSeek-V3.2-Exp | Mistral Medium |
|---|---|---|
| SciCode | 38.9% | 40.2% |
| WeirdML | 39.5% | 43.7% |
| LMArena Coding | 1454 | 1434 |
| FrontierCode | — | 8% |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| ALE-Bench | — | 763.98 |

## Agentic & Tool Use

- DeepSeek-V3.2-Exp: 32.7 (#59)
- Mistral Medium: 28.3 (#90)

| Benchmark | DeepSeek-V3.2-Exp | Mistral Medium |
|---|---|---|
| Berkeley Function Calling Leaderboard | 56.7% | 37.7% |
| Terminal-Bench | 39.6% | — |
| APEX-Agents | 21.3% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |

## Reasoning

- DeepSeek-V3.2-Exp: 22.1 (#208)
- Mistral Medium: 24.0 (#167)

| Benchmark | DeepSeek-V3.2-Exp | Mistral Medium |
|---|---|---|
| Kagi LLM Benchmark | 52.2% | 50% |
| CritPt | 2.9% | 0% |
| LMArena Hard Prompts | 1434 | 1426 |
| DTBench | 87.7% | 75.5% |
| LMCA | 29.1% | 26.1% |
| ARC-AGI-2 | 4% | — |
| NYT Connections (extended) | 36.7% | — |
| ARC-AGI-1 | 57% | — |
| Chess Puzzles | 14% | — |
| Thematic Generalization | 65% | — |
| Surface Evolver Bench | — | 26.9% |
| Epoch Capabilities Index | 146.27 | — |

## Math

- DeepSeek-V3.2-Exp: 41.7 (#87)
- Mistral Medium: 28.1 (#245)

| Benchmark | DeepSeek-V3.2-Exp | Mistral Medium |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 87.8% | 32.2% |
| ProofBench | 8% | 9% |
| LMArena Math | 1435 | 1408 |
| FrontierMath (Feb 2025 set) | 22.1% | 0.3% |
| MathArena Final-Answer Competitions | 57.7% | — |
| MATH Level 5 | — | 81.6% |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- DeepSeek-V3.2-Exp: 51.7 (#66)
- Mistral Medium: 25.0 (#265)

| Benchmark | DeepSeek-V3.2-Exp | Mistral Medium |
|---|---|---|
| GPQA Diamond | 83.4% | 59.5% |
| Vectara Hallucination Rate | 5.3% | 22.7% |
| LMArena Expert | 1436 | 1408 |
| Humanity's Last Exam | — | 4.5% |

## Multimodal

- DeepSeek-V3.2-Exp: —
- Mistral Medium: 35.3 (#88)

| Benchmark | DeepSeek-V3.2-Exp | Mistral Medium |
|---|---|---|
| LMArena Vision | — | 1172 |

## Multilingual

- DeepSeek-V3.2-Exp: 52.2 (#90)
- Mistral Medium: 52.1 (#91)

| Benchmark | DeepSeek-V3.2-Exp | Mistral Medium |
|---|---|---|
| LMArena Non-English | 1409 | 1408 |
| LMArena Chinese | 1461 | 1447 |
| LMArena French | 1433 | 1459 |
| LMArena German | 1440 | 1432 |
| LMArena Japanese | 1374 | 1378 |
| LMArena Korean | 1371 | 1380 |
| LMArena Russian | 1424 | 1411 |
| LMArena Spanish | 1440 | 1433 |

## Instruction Following

- DeepSeek-V3.2-Exp: 74.5 (#93)
- Mistral Medium: 73.7 (#116)

| Benchmark | DeepSeek-V3.2-Exp | Mistral Medium |
|---|---|---|
| LMArena Instruction Following | 1413 | 1398 |

## Long Context

- DeepSeek-V3.2-Exp: 47.6 (#16)
- Mistral Medium: 42.9 (#114)

| Benchmark | DeepSeek-V3.2-Exp | Mistral Medium |
|---|---|---|
| LMArena Longer Query | 1428 | 1406 |
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |

## Writing & Preference

- DeepSeek-V3.2-Exp: 62.4 (#77)
- Mistral Medium: 60.0 (#103)

| Benchmark | DeepSeek-V3.2-Exp | Mistral Medium |
|---|---|---|
| LMArena Text | 1425 | 1424 |
| LMArena Creative Writing | 1403 | 1391 |
| LMArena Multi-Turn | 1427 | 1418 |
| Short-Story Creative Writing | — | 77.3% |
| EQ-Bench Creative Writing | 1515 | — |

## FAQ

### Is DeepSeek-V3.2-Exp better than Mistral Medium?

DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 36.3 on the Noometry Index.

### Which is cheaper, DeepSeek-V3.2-Exp or Mistral Medium?

DeepSeek-V3.2-Exp is cheaper. It lists at $0.26 per million input tokens and $0.38 per million output tokens; Mistral Medium lists at $1.50 and $7.50.

### Is DeepSeek-V3.2-Exp or Mistral Medium better for coding?

DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.5 versus 34.2 in the Noometry coding category.

### Which has the bigger context window?

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

### How many benchmarks do DeepSeek-V3.2-Exp and Mistral Medium share?

29 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and Mistral Medium has 36.