Model comparison
DeepSeek-V3.1 vs Pixtral Large
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 32.2 on the Noometry Index.
Last verified . 1 shared benchmarks.
DeepSeek
Mistral AISummary
- 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.
Side by side
| DeepSeek-V3.1 | Pixtral Large | |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 42.8 | 32.2 |
| Released | 2025-08-21 | 2024-11-01 |
| Weights | Open | Open |
| Context window | 164K | 128K |
| Max output | 8K | 128K |
| Input $ / M tokens | $0.25 | $2 |
| Output $ / M tokens | $0.95 | $6 |
| Results tracked | 27 | 3 |
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Category by category
Coding Not comparable
DeepSeek-V3.1: 40.3 (#144), Pixtral Large: —
| Benchmark | DeepSeek-V3.1 | Pixtral Large |
|---|---|---|
| WeirdML | 38.4% | — |
| LMArena Coding | 1417 | — |
Reasoning DeepSeek-V3.1 leads
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 Not comparable
DeepSeek-V3.1: 38.9 (#122), Pixtral Large: —
| Benchmark | DeepSeek-V3.1 | Pixtral Large |
|---|---|---|
| LMArena Math | 1420 | — |
Knowledge Not comparable
DeepSeek-V3.1: 43.7 (#90), Pixtral Large: —
| Benchmark | DeepSeek-V3.1 | Pixtral Large |
|---|---|---|
| Vectara Hallucination Rate | 5.5% | — |
| LMArena Expert | 1405 | — |
Multimodal Not comparable
DeepSeek-V3.1: —, Pixtral Large: 30.6 (#111)
| Benchmark | DeepSeek-V3.1 | Pixtral Large |
|---|---|---|
| LMArena Vision | — | 1089 |
Multilingual Not comparable
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 Not comparable
DeepSeek-V3.1: 73.9 (#110), Pixtral Large: —
| Benchmark | DeepSeek-V3.1 | Pixtral Large |
|---|---|---|
| LMArena Instruction Following | 1400 | — |
Long Context Not comparable
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 leads
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 | — |
Frequently asked questions
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.