# DeepSeek-V3.2-Exp vs Pixtral Large

> DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 32.2 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v3-2-exp-vs-pixtral-large
- Last updated: 2026-10-10
- Shared benchmarks: 1

## Summary

- They share 1 benchmark with published results for both. DeepSeek-V3.2-Exp scores higher in 2 categories and Pixtral Large in 0 categories; one gap is clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.2-Exp leads 62.4 to 32.9.
- DeepSeek-V3.2-Exp is cheaper at $0.26 / $0.38 per million input/output tokens, against $2 / $6 for Pixtral Large.
- DeepSeek-V3.2-Exp accepts more context: 164K tokens versus 128K.

## Snapshot

| | DeepSeek-V3.2-Exp | Pixtral Large |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 44.3 | 32.2 |
| Rank | 78 | 259 |
| Context | 164K | 128K |
| Input $/M | $0.26 | $2 |
| Output $/M | $0.38 | $6 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.2-Exp: 46.5 (#65)
- Pixtral Large: —

| Benchmark | DeepSeek-V3.2-Exp | Pixtral Large |
|---|---|---|
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| SciCode | 38.9% | — |
| WeirdML | 39.5% | — |
| LMArena Coding | 1454 | — |

## Agentic & Tool Use

- DeepSeek-V3.2-Exp: 32.7 (#59)
- Pixtral Large: —

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

## Reasoning

- DeepSeek-V3.2-Exp: 22.1 (#208)
- Pixtral Large: 21.7 (#218)

| Benchmark | DeepSeek-V3.2-Exp | Pixtral Large |
|---|---|---|
| ARC-AGI-2 | 4% | — |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 36.7% | — |
| ARC-AGI-1 | 57% | — |
| CritPt | 2.9% | — |
| Chess Puzzles | 14% | — |
| EnigmaEval | — | 0.8% |
| Thematic Generalization | 65% | — |
| LMArena Hard Prompts | 1434 | — |
| DTBench | 87.7% | — |
| LMCA | 29.1% | — |
| Epoch Capabilities Index | 146.27 | — |

## Math

- DeepSeek-V3.2-Exp: 41.7 (#87)
- Pixtral Large: —

| Benchmark | DeepSeek-V3.2-Exp | Pixtral Large |
|---|---|---|
| MathArena Final-Answer Competitions | 57.7% | — |
| OTIS Mock AIME 2024-2025 | 87.8% | — |
| ProofBench | 8% | — |
| LMArena Math | 1435 | — |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- DeepSeek-V3.2-Exp: 51.7 (#66)
- Pixtral Large: —

| Benchmark | DeepSeek-V3.2-Exp | Pixtral Large |
|---|---|---|
| GPQA Diamond | 83.4% | — |
| Vectara Hallucination Rate | 5.3% | — |
| LMArena Expert | 1436 | — |

## Multimodal

- DeepSeek-V3.2-Exp: —
- Pixtral Large: 30.6 (#111)

| Benchmark | DeepSeek-V3.2-Exp | Pixtral Large |
|---|---|---|
| LMArena Vision | — | 1089 |

## Multilingual

- DeepSeek-V3.2-Exp: 52.2 (#90)
- Pixtral Large: —

| Benchmark | DeepSeek-V3.2-Exp | Pixtral Large |
|---|---|---|
| LMArena Non-English | 1409 | — |
| LMArena Chinese | 1461 | — |
| LMArena French | 1433 | — |
| LMArena German | 1440 | — |
| LMArena Japanese | 1374 | — |
| LMArena Korean | 1371 | — |
| LMArena Russian | 1424 | — |
| LMArena Spanish | 1440 | — |

## Instruction Following

- DeepSeek-V3.2-Exp: 74.5 (#93)
- Pixtral Large: —

| Benchmark | DeepSeek-V3.2-Exp | Pixtral Large |
|---|---|---|
| LMArena Instruction Following | 1413 | — |

## Long Context

- DeepSeek-V3.2-Exp: 47.6 (#16)
- Pixtral Large: —

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

## Writing & Preference

- DeepSeek-V3.2-Exp: 62.4 (#77)
- Pixtral Large: 32.9 (#278)

| Benchmark | DeepSeek-V3.2-Exp | Pixtral Large |
|---|---|---|
| EQ-Bench Creative Writing | 1515 | 988 |
| LMArena Text | 1425 | — |
| LMArena Creative Writing | 1403 | — |
| LMArena Multi-Turn | 1427 | — |

## FAQ

### Is DeepSeek-V3.2-Exp better than Pixtral Large?

DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 32.2 on the Noometry Index.

### Which is cheaper, DeepSeek-V3.2-Exp or Pixtral Large?

DeepSeek-V3.2-Exp is cheaper. It lists at $0.26 per million input tokens and $0.38 per million output tokens; Pixtral Large lists at $2 and $6.

### Which has the bigger context window?

DeepSeek-V3.2-Exp does, with 164K tokens against 128K.

### How many benchmarks do DeepSeek-V3.2-Exp and Pixtral Large share?

1 benchmark has published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and Pixtral Large has 3.