# DeepSeek-V3.1 vs Pixtral Large

> DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 32.2 on the Noometry Index.

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

## Summary

- They share 1 benchmark with published results for both. DeepSeek-V3.1 scores higher in 2 categories and Pixtral Large in 0 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.1 leads 60.3 to 32.9.
- DeepSeek-V3.1 is cheaper at $0.25 / $0.95 per million input/output tokens, against $2 / $6 for Pixtral Large.
- DeepSeek-V3.1 accepts more context: 164K tokens versus 128K.

## Snapshot

| | DeepSeek-V3.1 | Pixtral Large |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 42.8 | 32.2 |
| Rank | 108 | 259 |
| Context | 164K | 128K |
| Input $/M | $0.25 | $2 |
| Output $/M | $0.95 | $6 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.1: 40.3 (#144)
- Pixtral Large: —

| Benchmark | DeepSeek-V3.1 | Pixtral Large |
|---|---|---|
| WeirdML | 38.4% | — |
| LMArena Coding | 1417 | — |

## Reasoning

- DeepSeek-V3.1: 27.9 (#110)
- Pixtral Large: 21.7 (#218)

| Benchmark | DeepSeek-V3.1 | Pixtral Large |
|---|---|---|
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| EnigmaEval | — | 0.8% |
| LMArena Hard Prompts | 1417 | — |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| Epoch Capabilities Index | 139.92 | — |
| ForecastBench | 58 | — |

## Math

- DeepSeek-V3.1: 38.9 (#122)
- Pixtral Large: —

| Benchmark | DeepSeek-V3.1 | Pixtral Large |
|---|---|---|
| LMArena Math | 1420 | — |

## Knowledge

- DeepSeek-V3.1: 43.7 (#90)
- Pixtral Large: —

| Benchmark | DeepSeek-V3.1 | Pixtral Large |
|---|---|---|
| Vectara Hallucination Rate | 5.5% | — |
| LMArena Expert | 1405 | — |

## Multimodal

- DeepSeek-V3.1: —
- Pixtral Large: 30.6 (#111)

| Benchmark | DeepSeek-V3.1 | Pixtral Large |
|---|---|---|
| LMArena Vision | — | 1089 |

## Multilingual

- DeepSeek-V3.1: 51.6 (#106)
- Pixtral Large: —

| Benchmark | DeepSeek-V3.1 | Pixtral Large |
|---|---|---|
| LMArena Non-English | 1400 | — |
| LMArena Chinese | 1469 | — |
| LMArena French | 1447 | — |
| LMArena German | 1411 | — |
| LMArena Japanese | 1378 | — |
| LMArena Korean | 1337 | — |
| LMArena Russian | 1405 | — |
| LMArena Spanish | 1431 | — |

## Instruction Following

- DeepSeek-V3.1: 73.9 (#110)
- Pixtral Large: —

| Benchmark | DeepSeek-V3.1 | Pixtral Large |
|---|---|---|
| LMArena Instruction Following | 1400 | — |

## Long Context

- DeepSeek-V3.1: 36.3 (#232)
- Pixtral Large: —

| Benchmark | DeepSeek-V3.1 | Pixtral Large |
|---|---|---|
| Fiction.LiveBench | 52.8% | — |
| LMArena Longer Query | 1422 | — |

## Writing & Preference

- DeepSeek-V3.1: 60.3 (#98)
- Pixtral Large: 32.9 (#278)

| Benchmark | DeepSeek-V3.1 | Pixtral Large |
|---|---|---|
| EQ-Bench Creative Writing | 1436 | 988 |
| LMArena Text | 1420 | — |
| LMArena Creative Writing | 1401 | — |
| LMArena Multi-Turn | 1408 | — |

## FAQ

### Is DeepSeek-V3.1 better than Pixtral Large?

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 32.2 on the Noometry Index.

### Which is cheaper, DeepSeek-V3.1 or Pixtral Large?

DeepSeek-V3.1 is cheaper. It lists at $0.25 per million input tokens and $0.95 per million output tokens; Pixtral Large lists at $2 and $6.

### Which has the bigger context window?

DeepSeek-V3.1 does, with 164K tokens against 128K.

### How many benchmarks do DeepSeek-V3.1 and Pixtral Large share?

1 benchmark has published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Pixtral Large has 3.