Model comparison
DeepSeek-V2.5 (Sep 2024) vs Pixtral Large
DeepSeek-V2.5 (Sep 2024) is the stronger model overall, scoring 37.6 to 32.2 on the Noometry Index.
Last verified . 0 shared benchmarks.
Summary
- The widest gap is in writing & preference, where DeepSeek-V2.5 (Sep 2024) leads 49.8 to 32.9.
Side by side
| DeepSeek-V2.5 (Sep 2024) | Pixtral Large | |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 37.6 | 32.2 |
| Released | 2024-09-06 | 2024-11-01 |
| Weights | Open | Open |
| Context window | — | 128K |
| Max output | — | 128K |
| Input $ / M tokens | — | $2 |
| Output $ / M tokens | — | $6 |
| Results tracked | 22 | 3 |
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Category by category
Coding Not comparable
DeepSeek-V2.5 (Sep 2024): 31.7 (#281), Pixtral Large: —
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Pixtral Large |
|---|---|---|
| Aider Polyglot | 17.8% | — |
| BigCodeBench Instruct | 48.6% | — |
| LMArena Coding | 1309 | — |
| BigCodeBench Complete | 53.2% | — |
| HumanEval+ | 83.5% | — |
| MBPP+ | 74.1% | — |
Reasoning DeepSeek-V2.5 (Sep 2024) leads
DeepSeek-V2.5 (Sep 2024): 25.6 (#145), Pixtral Large: 21.7 (#218)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Pixtral Large |
|---|---|---|
| EnigmaEval | — | 0.8% |
| LMArena Hard Prompts | 1289 | — |
Math Not comparable
DeepSeek-V2.5 (Sep 2024): 35.9 (#177), Pixtral Large: —
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Pixtral Large |
|---|---|---|
| LMArena Math | 1288 | — |
Knowledge Not comparable
DeepSeek-V2.5 (Sep 2024): 34.8 (#193), Pixtral Large: —
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Pixtral Large |
|---|---|---|
| LMArena Expert | 1266 | — |
Multimodal Not comparable
DeepSeek-V2.5 (Sep 2024): —, Pixtral Large: 30.6 (#111)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Pixtral Large |
|---|---|---|
| LMArena Vision | — | 1089 |
Multilingual Not comparable
DeepSeek-V2.5 (Sep 2024): 42.5 (#193), Pixtral Large: —
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Pixtral Large |
|---|---|---|
| LMArena Non-English | 1273 | — |
| LMArena Chinese | 1318 | — |
| LMArena French | 1289 | — |
| LMArena German | 1258 | — |
| LMArena Japanese | 1228 | — |
| LMArena Korean | 1209 | — |
| LMArena Russian | 1289 | — |
| LMArena Spanish | 1248 | — |
Instruction Following Not comparable
DeepSeek-V2.5 (Sep 2024): 67.5 (#194), Pixtral Large: —
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Pixtral Large |
|---|---|---|
| LMArena Instruction Following | 1280 | — |
Long Context Not comparable
DeepSeek-V2.5 (Sep 2024): 39.5 (#174), Pixtral Large: —
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Pixtral Large |
|---|---|---|
| LMArena Longer Query | 1301 | — |
Writing & Preference DeepSeek-V2.5 (Sep 2024) leads
DeepSeek-V2.5 (Sep 2024): 49.8 (#187), Pixtral Large: 32.9 (#278)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Pixtral Large |
|---|---|---|
| LMArena Text | 1294 | — |
| LMArena Creative Writing | 1285 | — |
| EQ-Bench Creative Writing | — | 988 |
| LMArena Multi-Turn | 1297 | — |
Frequently asked questions
Is DeepSeek-V2.5 (Sep 2024) better than Pixtral Large?
DeepSeek-V2.5 (Sep 2024) is the stronger model overall, scoring 37.6 to 32.2 on the Noometry Index.
How many benchmarks do DeepSeek-V2.5 (Sep 2024) and Pixtral Large share?
0 benchmarks have published results for both models. DeepSeek-V2.5 (Sep 2024) has 22 scored results on Noometry and Pixtral Large has 3.