Model comparison
DeepSeek-V3.2-Speciale vs Qwen1.5-72B
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 30.8 on the Noometry Index.
Last verified . 0 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- The widest gap is in reasoning, where DeepSeek-V3.2-Speciale leads 32.9 to 22.2.
Side by side
| DeepSeek-V3.2-Speciale | Qwen1.5-72B | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 39.7 | 30.8 |
| 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 | 22 |
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Category by category
Coding DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 40.4 (#140), Qwen1.5-72B: 31.9 (#277)
| Benchmark | DeepSeek-V3.2-Speciale | Qwen1.5-72B |
|---|---|---|
| WeirdML | 46.7% | — |
| BigCodeBench Instruct | — | 33.2% |
| LMArena Coding | — | 1165 |
| BigCodeBench Complete | — | 40.3% |
| HumanEval+ | — | 59.1% |
| MBPP+ | — | 61.6% |
Reasoning DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 32.9 (#73), Qwen1.5-72B: 22.2 (#203)
| Benchmark | DeepSeek-V3.2-Speciale | Qwen1.5-72B |
|---|---|---|
| SimpleBench | 52.6% | — |
| LMArena Hard Prompts | — | 1148 |
Math Not comparable
DeepSeek-V3.2-Speciale: —, Qwen1.5-72B: 33.2 (#205)
| Benchmark | DeepSeek-V3.2-Speciale | Qwen1.5-72B |
|---|---|---|
| LMArena Math | — | 1164 |
Knowledge Not comparable
DeepSeek-V3.2-Speciale: —, Qwen1.5-72B: 11.5 (#300)
| Benchmark | DeepSeek-V3.2-Speciale | Qwen1.5-72B |
|---|---|---|
| GPQA Diamond | — | 28.8% |
| LMArena Expert | — | 1136 |
Multilingual Not comparable
DeepSeek-V3.2-Speciale: —, Qwen1.5-72B: 33.2 (#253)
| Benchmark | DeepSeek-V3.2-Speciale | Qwen1.5-72B |
|---|---|---|
| LMArena Non-English | — | 1135 |
| LMArena Chinese | — | 1186 |
| LMArena French | — | 1159 |
| LMArena German | — | 1084 |
| LMArena Japanese | — | 1061 |
| LMArena Korean | — | 1050 |
| LMArena Russian | — | 1104 |
| LMArena Spanish | — | 1110 |
Instruction Following Not comparable
DeepSeek-V3.2-Speciale: —, Qwen1.5-72B: 59.3 (#256)
| Benchmark | DeepSeek-V3.2-Speciale | Qwen1.5-72B |
|---|---|---|
| LMArena Instruction Following | — | 1141 |
Long Context Not comparable
DeepSeek-V3.2-Speciale: —, Qwen1.5-72B: 35.1 (#243)
| Benchmark | DeepSeek-V3.2-Speciale | Qwen1.5-72B |
|---|---|---|
| LMArena Longer Query | — | 1157 |
Writing & Preference DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 46.0 (#222), Qwen1.5-72B: 37.3 (#258)
| Benchmark | DeepSeek-V3.2-Speciale | Qwen1.5-72B |
|---|---|---|
| LMArena Text | — | 1166 |
| LMArena Creative Writing | — | 1137 |
| EQ-Bench Creative Writing | 1276 | — |
| LMArena Multi-Turn | — | 1160 |
Frequently asked questions
Is DeepSeek-V3.2-Speciale better than Qwen1.5-72B?
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 30.8 on the Noometry Index.
Is DeepSeek-V3.2-Speciale or Qwen1.5-72B better for coding?
DeepSeek-V3.2-Speciale scores higher on coding benchmarks: 40.4 versus 31.9 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.2-Speciale and Qwen1.5-72B share?
0 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and Qwen1.5-72B has 22.