Model comparison
DeepSeek-V3.2-Speciale vs Mistral
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 29.9 on the Noometry Index.
Last verified . 0 shared benchmarks.
DeepSeek
Mistral AISummary
- The widest gap is in reasoning, where DeepSeek-V3.2-Speciale leads 32.9 to 22.2.
- DeepSeek-V3.2-Speciale has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.2-Speciale | Mistral | |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 39.7 | 29.9 |
| Released | 2025-12-01 | — |
| Weights | Open | Proprietary |
| Context window | 128K | — |
| Max output | 128K | — |
| Input $ / M tokens | $0.58 | — |
| Output $ / M tokens | $1.68 | — |
| Results tracked | 3 | 22 |
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Category by category
Coding DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 40.4 (#140), Mistral: 33.8 (#250)
| Benchmark | DeepSeek-V3.2-Speciale | Mistral |
|---|---|---|
| WeirdML | 46.7% | — |
| LMArena Coding | — | 1162 |
Reasoning DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 32.9 (#73), Mistral: 22.2 (#200)
| Benchmark | DeepSeek-V3.2-Speciale | Mistral |
|---|---|---|
| SimpleBench | 52.6% | — |
| LMArena Hard Prompts | — | 1149 |
Math Not comparable
DeepSeek-V3.2-Speciale: —, Mistral: 22.3 (#278)
| Benchmark | DeepSeek-V3.2-Speciale | Mistral |
|---|---|---|
| Omni-MATH | — | 7.2% |
| LMArena Math | — | 1180 |
Knowledge Not comparable
DeepSeek-V3.2-Speciale: —, Mistral: 16.6 (#288)
| Benchmark | DeepSeek-V3.2-Speciale | Mistral |
|---|---|---|
| MMLU-Pro | — | 27.7% |
| GPQA (HELM) | — | 30.3% |
| LMArena Expert | — | 1125 |
Multilingual Not comparable
DeepSeek-V3.2-Speciale: —, Mistral: 32.8 (#254)
| Benchmark | DeepSeek-V3.2-Speciale | Mistral |
|---|---|---|
| LMArena Non-English | — | 1129 |
| LMArena Chinese | — | 1109 |
| LMArena French | — | 1180 |
| LMArena German | — | 1155 |
| LMArena Japanese | — | 1013 |
| LMArena Korean | — | 1032 |
| LMArena Russian | — | 1168 |
| LMArena Spanish | — | 1143 |
Instruction Following Not comparable
DeepSeek-V3.2-Speciale: —, Mistral: 52.6 (#288)
| Benchmark | DeepSeek-V3.2-Speciale | Mistral |
|---|---|---|
| IFEval | — | 56.8% |
| LMArena Instruction Following | — | 1152 |
Long Context Not comparable
DeepSeek-V3.2-Speciale: —, Mistral: 35.0 (#245)
| Benchmark | DeepSeek-V3.2-Speciale | Mistral |
|---|---|---|
| LMArena Longer Query | — | 1153 |
Writing & Preference DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 46.0 (#222), Mistral: 37.0 (#260)
| Benchmark | DeepSeek-V3.2-Speciale | Mistral |
|---|---|---|
| LMArena Text | — | 1165 |
| LMArena Creative Writing | — | 1158 |
| EQ-Bench Creative Writing | 1276 | — |
| WildBench | — | 66% |
| LMArena Multi-Turn | — | 1147 |
Frequently asked questions
Is DeepSeek-V3.2-Speciale better than Mistral?
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 29.9 on the Noometry Index.
Is DeepSeek-V3.2-Speciale or Mistral better for coding?
DeepSeek-V3.2-Speciale scores higher on coding benchmarks: 40.4 versus 33.8 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.2-Speciale and Mistral share?
0 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and Mistral has 22.