Model comparison
DeepSeek-V3.2-Speciale vs Qwen1.5-32B
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 30.5 on the Noometry Index.
Last verified . 0 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- The widest gap is in writing & preference, where DeepSeek-V3.2-Speciale leads 46.0 to 34.2.
Side by side
| DeepSeek-V3.2-Speciale | Qwen1.5-32B | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 39.7 | 30.5 |
| Released | 2025-12-01 | 2024-02-04 |
| Weights | Open | Open |
| Context window | 128K | — |
| Max output | 128K | — |
| Input $ / M tokens | $0.58 | — |
| Output $ / M tokens | $1.68 | — |
| Results tracked | 3 | 21 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 40.4 (#140), Qwen1.5-32B: 31.7 (#282)
| Benchmark | DeepSeek-V3.2-Speciale | Qwen1.5-32B |
|---|---|---|
| WeirdML | 46.7% | — |
| BigCodeBench Instruct | — | 32.3% |
| LMArena Coding | — | 1155 |
| BigCodeBench Complete | — | 42% |
Reasoning DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 32.9 (#73), Qwen1.5-32B: 21.8 (#212)
| Benchmark | DeepSeek-V3.2-Speciale | Qwen1.5-32B |
|---|---|---|
| SimpleBench | 52.6% | — |
| LMArena Hard Prompts | — | 1130 |
Math Not comparable
DeepSeek-V3.2-Speciale: —, Qwen1.5-32B: 33.0 (#207)
| Benchmark | DeepSeek-V3.2-Speciale | Qwen1.5-32B |
|---|---|---|
| LMArena Math | — | 1155 |
Knowledge Not comparable
DeepSeek-V3.2-Speciale: —, Qwen1.5-32B: 13.5 (#296)
| Benchmark | DeepSeek-V3.2-Speciale | Qwen1.5-32B |
|---|---|---|
| GPQA Diamond | — | 30.7% |
| LMArena Expert | — | 1126 |
| MMLU | — | 74.4% |
Multilingual Not comparable
DeepSeek-V3.2-Speciale: —, Qwen1.5-32B: 31.4 (#259)
| Benchmark | DeepSeek-V3.2-Speciale | Qwen1.5-32B |
|---|---|---|
| LMArena Non-English | — | 1106 |
| LMArena Chinese | — | 1177 |
| LMArena French | — | 1101 |
| LMArena German | — | 1058 |
| LMArena Japanese | — | 1027 |
| LMArena Korean | — | 1008 |
| LMArena Russian | — | 1073 |
| LMArena Spanish | — | 1089 |
Instruction Following Not comparable
DeepSeek-V3.2-Speciale: —, Qwen1.5-32B: 57.7 (#265)
| Benchmark | DeepSeek-V3.2-Speciale | Qwen1.5-32B |
|---|---|---|
| LMArena Instruction Following | — | 1116 |
Long Context Not comparable
DeepSeek-V3.2-Speciale: —, Qwen1.5-32B: 34.7 (#246)
| Benchmark | DeepSeek-V3.2-Speciale | Qwen1.5-32B |
|---|---|---|
| LMArena Longer Query | — | 1146 |
Writing & Preference DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 46.0 (#222), Qwen1.5-32B: 34.2 (#271)
| Benchmark | DeepSeek-V3.2-Speciale | Qwen1.5-32B |
|---|---|---|
| LMArena Text | — | 1137 |
| LMArena Creative Writing | — | 1083 |
| EQ-Bench Creative Writing | 1276 | — |
| LMArena Multi-Turn | — | 1140 |
Frequently asked questions
Is DeepSeek-V3.2-Speciale better than Qwen1.5-32B?
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 30.5 on the Noometry Index.
Is DeepSeek-V3.2-Speciale or Qwen1.5-32B better for coding?
DeepSeek-V3.2-Speciale scores higher on coding benchmarks: 40.4 versus 31.7 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.2-Speciale and Qwen1.5-32B share?
0 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and Qwen1.5-32B has 21.