Model comparison
DeepSeek-V3.1-Terminus vs Pixtral Large
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 32.2 on the Noometry Index.
Last verified . 0 shared benchmarks.
DeepSeek
Mistral AISummary
- The widest gap is in writing & preference, where DeepSeek-V3.1-Terminus leads 61.0 to 32.9.
- DeepSeek-V3.1-Terminus is cheaper at $0.27 / $1 per million input/output tokens, against $2 / $6 for Pixtral Large.
- DeepSeek-V3.1-Terminus accepts more context: 164K tokens versus 128K.
Side by side
| DeepSeek-V3.1-Terminus | Pixtral Large | |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 43.1 | 32.2 |
| Released | 2025-09-22 | 2024-11-01 |
| Weights | Open | Open |
| Context window | 164K | 128K |
| Max output | 147K | 128K |
| Input $ / M tokens | $0.27 | $2 |
| Output $ / M tokens | $1 | $6 |
| Results tracked | 16 | 3 |
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Category by category
Coding Not comparable
DeepSeek-V3.1-Terminus: 42.0 (#113), Pixtral Large: —
| Benchmark | DeepSeek-V3.1-Terminus | Pixtral Large |
|---|---|---|
| SciCode | 40.6% | — |
| LMArena Coding | 1426 | — |
| ALE-Bench | 745.17 | — |
Reasoning DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 26.4 (#133), Pixtral Large: 21.7 (#218)
| Benchmark | DeepSeek-V3.1-Terminus | Pixtral Large |
|---|---|---|
| Kagi LLM Benchmark | 57.4% | — |
| CritPt | 1.7% | — |
| EnigmaEval | — | 0.8% |
| LMArena Hard Prompts | 1426 | — |
| DTBench | 81.3% | — |
| LMCA | 28.6% | — |
Math Not comparable
DeepSeek-V3.1-Terminus: 38.5 (#137), Pixtral Large: —
| Benchmark | DeepSeek-V3.1-Terminus | Pixtral Large |
|---|---|---|
| LMArena Math | 1402 | — |
Multimodal Not comparable
DeepSeek-V3.1-Terminus: —, Pixtral Large: 30.6 (#111)
| Benchmark | DeepSeek-V3.1-Terminus | Pixtral Large |
|---|---|---|
| LMArena Vision | — | 1089 |
Multilingual Not comparable
DeepSeek-V3.1-Terminus: 52.1 (#92), Pixtral Large: —
| Benchmark | DeepSeek-V3.1-Terminus | Pixtral Large |
|---|---|---|
| LMArena Non-English | 1407 | — |
| LMArena Russian | 1436 | — |
Instruction Following Not comparable
DeepSeek-V3.1-Terminus: 74.0 (#106), Pixtral Large: —
| Benchmark | DeepSeek-V3.1-Terminus | Pixtral Large |
|---|---|---|
| LMArena Instruction Following | 1404 | — |
Long Context Not comparable
DeepSeek-V3.1-Terminus: 43.4 (#97), Pixtral Large: —
| Benchmark | DeepSeek-V3.1-Terminus | Pixtral Large |
|---|---|---|
| LMArena Longer Query | 1421 | — |
Writing & Preference DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 61.0 (#92), Pixtral Large: 32.9 (#278)
| Benchmark | DeepSeek-V3.1-Terminus | Pixtral Large |
|---|---|---|
| LMArena Text | 1419 | — |
| LMArena Creative Writing | 1403 | — |
| EQ-Bench Creative Writing | — | 988 |
| LMArena Multi-Turn | 1411 | — |
Frequently asked questions
Is DeepSeek-V3.1-Terminus better than Pixtral Large?
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 32.2 on the Noometry Index.
Which is cheaper, DeepSeek-V3.1-Terminus or Pixtral Large?
DeepSeek-V3.1-Terminus is cheaper. It lists at $0.27 per million input tokens and $1 per million output tokens; Pixtral Large lists at $2 and $6.
Which has the bigger context window?
DeepSeek-V3.1-Terminus does, with 164K tokens against 128K.
How many benchmarks do DeepSeek-V3.1-Terminus and Pixtral Large share?
0 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and Pixtral Large has 3.