Model comparison
DeepSeek-V3.2-Speciale vs Mixtral 8x22B
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 27.1 on the Noometry Index.
Last verified . 1 shared benchmarks.
DeepSeek
Mistral AISummary
- They share 1 benchmark with published results for both. DeepSeek-V3.2-Speciale scores higher in 3 categories and Mixtral 8x22B in 0 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in coding, where DeepSeek-V3.2-Speciale leads 40.4 to 24.2.
- The biggest single-benchmark swing is WeirdML: 46.7% for DeepSeek-V3.2-Speciale and 3.2% for Mixtral 8x22B.
- DeepSeek-V3.2-Speciale is cheaper at $0.58 / $1.68 per million input/output tokens, against $2 / $6 for Mixtral 8x22B.
- DeepSeek-V3.2-Speciale accepts more context: 128K tokens versus 64K.
Side by side
| DeepSeek-V3.2-Speciale | Mixtral 8x22B | |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 39.7 | 27.1 |
| Released | 2025-12-01 | 2024-04-17 |
| Weights | Open | Open |
| Context window | 128K | 64K |
| Max output | 128K | 64K |
| Input $ / M tokens | $0.58 | $2 |
| Output $ / M tokens | $1.68 | $6 |
| Results tracked | 3 | 34 |
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Category by category
Coding DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 40.4 (#140), Mixtral 8x22B: 24.2 (#329)
| Benchmark | DeepSeek-V3.2-Speciale | Mixtral 8x22B |
|---|---|---|
| WeirdML | 46.7% | 3.2% |
| BigCodeBench Instruct | — | 40.6% |
| LMArena Coding | — | 1166 |
| BigCodeBench Complete | — | 50.2% |
| HumanEval+ | — | 72% |
| MBPP+ | — | 64.3% |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Speciale: —, Mixtral 8x22B: 23.1 (#127)
| Benchmark | DeepSeek-V3.2-Speciale | Mixtral 8x22B |
|---|---|---|
| Cybench | — | 7.5% |
Reasoning DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 32.9 (#73), Mixtral 8x22B: 19.9 (#248)
| Benchmark | DeepSeek-V3.2-Speciale | Mixtral 8x22B |
|---|---|---|
| SimpleBench | 52.6% | — |
| LMArena Hard Prompts | — | 1150 |
| DTBench | — | 55.1% |
| Epoch Capabilities Index | — | 122.03 |
| ForecastBench | — | 56.3 |
Math Not comparable
DeepSeek-V3.2-Speciale: —, Mixtral 8x22B: 22.9 (#275)
| Benchmark | DeepSeek-V3.2-Speciale | Mixtral 8x22B |
|---|---|---|
| Omni-MATH | — | 16.3% |
| LMArena Math | — | 1184 |
| MATH Level 5 | — | 24.2% |
Knowledge Not comparable
DeepSeek-V3.2-Speciale: —, Mixtral 8x22B: 15.1 (#293)
| Benchmark | DeepSeek-V3.2-Speciale | Mixtral 8x22B |
|---|---|---|
| GPQA Diamond | — | 34.1% |
| MMLU-Pro | — | 46% |
| GPQA (HELM) | — | 33.4% |
| LMArena Expert | — | 1113 |
| MMLU | — | 77.8% |
Multilingual Not comparable
DeepSeek-V3.2-Speciale: —, Mixtral 8x22B: 32.8 (#255)
| Benchmark | DeepSeek-V3.2-Speciale | Mixtral 8x22B |
|---|---|---|
| LMArena Non-English | — | 1128 |
| LMArena Chinese | — | 1116 |
| LMArena French | — | 1166 |
| LMArena German | — | 1141 |
| LMArena Japanese | — | 1037 |
| LMArena Korean | — | 1057 |
| LMArena Russian | — | 1158 |
| LMArena Spanish | — | 1151 |
Instruction Following Not comparable
DeepSeek-V3.2-Speciale: —, Mixtral 8x22B: 57.7 (#266)
| Benchmark | DeepSeek-V3.2-Speciale | Mixtral 8x22B |
|---|---|---|
| IFEval | — | 72.4% |
| LMArena Instruction Following | — | 1147 |
Long Context Not comparable
DeepSeek-V3.2-Speciale: —, Mixtral 8x22B: 34.7 (#247)
| Benchmark | DeepSeek-V3.2-Speciale | Mixtral 8x22B |
|---|---|---|
| LMArena Longer Query | — | 1144 |
Writing & Preference DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 46.0 (#222), Mixtral 8x22B: 36.9 (#262)
| Benchmark | DeepSeek-V3.2-Speciale | Mixtral 8x22B |
|---|---|---|
| LMArena Text | — | 1162 |
| LMArena Creative Writing | — | 1141 |
| EQ-Bench Creative Writing | 1276 | — |
| WildBench | — | 71.1% |
| LMArena Multi-Turn | — | 1130 |
Frequently asked questions
Is DeepSeek-V3.2-Speciale better than Mixtral 8x22B?
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 27.1 on the Noometry Index.
Which is cheaper, DeepSeek-V3.2-Speciale or Mixtral 8x22B?
DeepSeek-V3.2-Speciale is cheaper. It lists at $0.58 per million input tokens and $1.68 per million output tokens; Mixtral 8x22B lists at $2 and $6.
Is DeepSeek-V3.2-Speciale or Mixtral 8x22B better for coding?
DeepSeek-V3.2-Speciale scores higher on coding benchmarks: 40.4 versus 24.2 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-V3.2-Speciale does, with 128K tokens against 64K.
How many benchmarks do DeepSeek-V3.2-Speciale and Mixtral 8x22B share?
1 benchmark has published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and Mixtral 8x22B has 34.