Model comparison
DeepSeek-V3.1-Terminus vs Qwen1.5-32B
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 30.5 on the Noometry Index.
Last verified . 10 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- They share 10 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 7 categories and Qwen1.5-32B in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.1-Terminus leads 61.0 to 34.2.
Side by side
| DeepSeek-V3.1-Terminus | Qwen1.5-32B | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 43.1 | 30.5 |
| Released | 2025-09-22 | 2024-02-04 |
| Weights | Open | Open |
| Context window | 164K | — |
| Max output | 147K | — |
| Input $ / M tokens | $0.27 | — |
| Output $ / M tokens | $1 | — |
| Results tracked | 16 | 21 |
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Category by category
Coding DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 42.0 (#113), Qwen1.5-32B: 31.7 (#282)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen1.5-32B |
|---|---|---|
| LMArena Coding | 1426 | 1155 |
| SciCode | 40.6% | — |
| BigCodeBench Instruct | — | 32.3% |
| BigCodeBench Complete | — | 42% |
| ALE-Bench | 745.17 | — |
Reasoning DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 26.4 (#133), Qwen1.5-32B: 21.8 (#212)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen1.5-32B |
|---|---|---|
| LMArena Hard Prompts | 1426 | 1130 |
| Kagi LLM Benchmark | 57.4% | — |
| CritPt | 1.7% | — |
| DTBench | 81.3% | — |
| LMCA | 28.6% | — |
Math DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 38.5 (#137), Qwen1.5-32B: 33.0 (#207)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen1.5-32B |
|---|---|---|
| LMArena Math | 1402 | 1155 |
Knowledge Not comparable
DeepSeek-V3.1-Terminus: —, Qwen1.5-32B: 13.5 (#296)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen1.5-32B |
|---|---|---|
| GPQA Diamond | — | 30.7% |
| LMArena Expert | — | 1126 |
| MMLU | — | 74.4% |
Multilingual DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 52.1 (#92), Qwen1.5-32B: 31.4 (#259)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen1.5-32B |
|---|---|---|
| LMArena Non-English | 1407 | 1106 |
| LMArena Russian | 1436 | 1073 |
| LMArena Chinese | — | 1177 |
| LMArena French | — | 1101 |
| LMArena German | — | 1058 |
| LMArena Japanese | — | 1027 |
| LMArena Korean | — | 1008 |
| LMArena Spanish | — | 1089 |
Instruction Following DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 74.0 (#106), Qwen1.5-32B: 57.7 (#265)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen1.5-32B |
|---|---|---|
| LMArena Instruction Following | 1404 | 1116 |
Long Context DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 43.4 (#97), Qwen1.5-32B: 34.7 (#246)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen1.5-32B |
|---|---|---|
| LMArena Longer Query | 1421 | 1146 |
Writing & Preference DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 61.0 (#92), Qwen1.5-32B: 34.2 (#271)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen1.5-32B |
|---|---|---|
| LMArena Text | 1419 | 1137 |
| LMArena Creative Writing | 1403 | 1083 |
| LMArena Multi-Turn | 1411 | 1140 |
Frequently asked questions
Is DeepSeek-V3.1-Terminus better than Qwen1.5-32B?
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 30.5 on the Noometry Index.
Is DeepSeek-V3.1-Terminus or Qwen1.5-32B better for coding?
DeepSeek-V3.1-Terminus scores higher on coding benchmarks: 42.0 versus 31.7 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.1-Terminus and Qwen1.5-32B share?
10 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and Qwen1.5-32B has 21.