Model comparison
DeepSeek-V3.2-Speciale vs Mistral Nemo
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 26.4 on the Noometry Index. Mistral Nemo costs 5.7× 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 Nemo in 0 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.2-Speciale leads 46.0 to 28.5.
- Mistral Nemo is cheaper at $0.15 / $0.15 per million input/output tokens, against $0.58 / $1.68 for DeepSeek-V3.2-Speciale.
Side by side
| DeepSeek-V3.2-Speciale | Mistral Nemo | |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 39.7 | 26.4 |
| Released | 2025-12-01 | 2024-07-01 |
| Weights | Open | Open |
| Context window | 128K | 128K |
| Max output | 128K | 128K |
| Input $ / M tokens | $0.58 | $0.15 |
| Output $ / M tokens | $1.68 | $0.15 |
| Results tracked | 3 | 10 |
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Category by category
Coding Not comparable
DeepSeek-V3.2-Speciale: 40.4 (#140), Mistral Nemo: —
| Benchmark | DeepSeek-V3.2-Speciale | Mistral Nemo |
|---|---|---|
| WeirdML | 46.7% | — |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Speciale: —, Mistral Nemo: 23.5 (#125)
| Benchmark | DeepSeek-V3.2-Speciale | Mistral Nemo |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 27.6% |
| BALROG | — | 17.6% |
Reasoning DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 32.9 (#73), Mistral Nemo: 20.7 (#232)
| Benchmark | DeepSeek-V3.2-Speciale | Mistral Nemo |
|---|---|---|
| SimpleBench | 52.6% | — |
| DTBench | — | 48.6% |
| Epoch Capabilities Index | — | 118.68 |
| PIQA | — | 83.5% |
Math Not comparable
DeepSeek-V3.2-Speciale: —, Mistral Nemo: 25.5 (#268)
| Benchmark | DeepSeek-V3.2-Speciale | Mistral Nemo |
|---|---|---|
| MATH Level 5 | — | 10.8% |
| GSM8K | — | 84.2% |
Knowledge Not comparable
DeepSeek-V3.2-Speciale: —, Mistral Nemo: 12.3 (#298)
| Benchmark | DeepSeek-V3.2-Speciale | Mistral Nemo |
|---|---|---|
| GPQA Diamond | — | 29.9% |
| BoolQ | — | 82.5% |
Writing & Preference DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 46.0 (#222), Mistral Nemo: 28.5 (#296)
| Benchmark | DeepSeek-V3.2-Speciale | Mistral Nemo |
|---|---|---|
| EQ-Bench Creative Writing | 1276 | 881 |
Frequently asked questions
Is DeepSeek-V3.2-Speciale better than Mistral Nemo?
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 26.4 on the Noometry Index. Mistral Nemo costs 5.7× 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 Nemo?
Mistral Nemo is cheaper. It lists at $0.15 per million input tokens and $0.15 per million output tokens; DeepSeek-V3.2-Speciale lists at $0.58 and $1.68.
Which has the bigger context window?
Both accept 128K tokens.
How many benchmarks do DeepSeek-V3.2-Speciale and Mistral Nemo share?
1 benchmark has published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and Mistral Nemo has 10.