Model comparison
DeepSeek-V3.2-Speciale vs Mistral Small 3.2
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.1 to 30.8 on the Noometry Index. Mistral Small 3.2 costs 6.4× less per token, which makes it the better buy when DeepSeek-V3.2-Speciale's lead doesn't matter for your workload.
Last verified . 1 shared benchmarks.
DeepSeek
Mistral AISummary
- They share 1 benchmark with published results for both. DeepSeek-V3.2-Speciale scores higher in 2 categories and Mistral Small 3.2 in 0 categories; one gap is clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek-V3.2-Speciale leads 32.1 to 17.5.
- Mistral Small 3.2 is cheaper at $0.0938 / $0.25 per million input/output tokens, against $0.58 / $1.68 for DeepSeek-V3.2-Speciale.
- Mistral Small 3.2 accepts more context: 256K tokens versus 128K.
Side by side
| DeepSeek-V3.2-Speciale | Mistral Small 3.2 | |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 39.1 | 30.8 |
| Released | 2025-12-01 | 2025-06-20 |
| Weights | Open | Open |
| Context window | 128K | 256K |
| Max output | 128K | 16K |
| Input $ / M tokens | $0.58 | $0.0938 |
| Output $ / M tokens | $1.68 | $0.25 |
| Results tracked | 3 | 6 |
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Category by category
Coding Not comparable
DeepSeek-V3.2-Speciale: 39.9 (#136), Mistral Small 3.2: —
| Benchmark | DeepSeek-V3.2-Speciale | Mistral Small 3.2 |
|---|---|---|
| WeirdML | 46.7% | — |
Reasoning DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 32.1 (#65), Mistral Small 3.2: 17.5 (#282)
| Benchmark | DeepSeek-V3.2-Speciale | Mistral Small 3.2 |
|---|---|---|
| SimpleBench | 52.6% | — |
| Kagi LLM Benchmark | — | 40.4% |
| Chess Puzzles | — | 1% |
| Epoch Capabilities Index | — | 131.73 |
Math Not comparable
DeepSeek-V3.2-Speciale: —, Mistral Small 3.2: 24.9 (#251)
| Benchmark | DeepSeek-V3.2-Speciale | Mistral Small 3.2 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 30.3% |
Knowledge Not comparable
DeepSeek-V3.2-Speciale: —, Mistral Small 3.2: 26.8 (#249)
| Benchmark | DeepSeek-V3.2-Speciale | Mistral Small 3.2 |
|---|---|---|
| GPQA Diamond | — | 49.1% |
Writing & Preference Too close to call
DeepSeek-V3.2-Speciale: 46.1 (#217), Mistral Small 3.2: 45.1 (#219)
| Benchmark | DeepSeek-V3.2-Speciale | Mistral Small 3.2 |
|---|---|---|
| EQ-Bench Creative Writing | 1276 | 1255 |
Frequently asked questions
Is DeepSeek-V3.2-Speciale better than Mistral Small 3.2?
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.1 to 30.8 on the Noometry Index. Mistral Small 3.2 costs 6.4× less per token, which makes it the better buy when DeepSeek-V3.2-Speciale's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.2-Speciale or Mistral Small 3.2?
Mistral Small 3.2 is cheaper. It lists at $0.0938 per million input tokens and $0.25 per million output tokens; DeepSeek-V3.2-Speciale lists at $0.58 and $1.68.
Which has the bigger context window?
Mistral Small 3.2 does, with 256K tokens against 128K.
How many benchmarks do DeepSeek-V3.2-Speciale and Mistral Small 3.2 share?
1 benchmark has published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and Mistral Small 3.2 has 6.