Model comparison
DeepSeek-V3.1 vs Qwen1.5-7B
DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 30.8 on the Noometry Index.
Last verified . 12 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- They share 12 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 8 categories and Qwen1.5-7B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.1 leads 60.5 to 29.8.
Side by side
| DeepSeek-V3.1 | Qwen1.5-7B | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 42.2 | 30.8 |
| Released | 2025-08-21 | 2024-02-04 |
| Weights | Open | Open |
| Context window | 164K | — |
| Max output | 8K | — |
| Input $ / M tokens | $0.25 | — |
| Output $ / M tokens | $0.95 | — |
| Results tracked | 27 | 13 |
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Category by category
Coding DeepSeek-V3.1 leads
DeepSeek-V3.1: 39.8 (#140), Qwen1.5-7B: 31.6 (#271)
| Benchmark | DeepSeek-V3.1 | Qwen1.5-7B |
|---|---|---|
| LMArena Coding | 1417 | 1107 |
| WeirdML | 38.4% | — |
Reasoning DeepSeek-V3.1 leads
DeepSeek-V3.1: 27.3 (#109), Qwen1.5-7B: 19.5 (#237)
| Benchmark | DeepSeek-V3.1 | Qwen1.5-7B |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1066 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| Epoch Capabilities Index | 139.92 | — |
| ForecastBench | 58 | — |
Math DeepSeek-V3.1 leads
DeepSeek-V3.1: 36.1 (#125), Qwen1.5-7B: 29.0 (#222)
| Benchmark | DeepSeek-V3.1 | Qwen1.5-7B |
|---|---|---|
| LMArena Math | 1420 | 1080 |
Knowledge DeepSeek-V3.1 leads
DeepSeek-V3.1: 43.0 (#86), Qwen1.5-7B: 28.2 (#239)
| Benchmark | DeepSeek-V3.1 | Qwen1.5-7B |
|---|---|---|
| LMArena Expert | 1403 | 1055 |
| Vectara Hallucination Rate | 5.5% | — |
| MMLU | — | 62.6% |
Multilingual DeepSeek-V3.1 leads
DeepSeek-V3.1: 51.8 (#100), Qwen1.5-7B: 28.6 (#266)
| Benchmark | DeepSeek-V3.1 | Qwen1.5-7B |
|---|---|---|
| LMArena Non-English | 1400 | 1058 |
| LMArena Chinese | 1470 | 1140 |
| LMArena Russian | 1405 | 1006 |
| LMArena French | 1447 | — |
| LMArena German | 1412 | — |
| LMArena Japanese | 1374 | — |
| LMArena Korean | 1337 | — |
| LMArena Spanish | 1429 | — |
Instruction Following DeepSeek-V3.1 leads
DeepSeek-V3.1: 74.1 (#105), Qwen1.5-7B: 54.4 (#276)
| Benchmark | DeepSeek-V3.1 | Qwen1.5-7B |
|---|---|---|
| LMArena Instruction Following | 1400 | 1058 |
Long Context DeepSeek-V3.1 leads
DeepSeek-V3.1: 36.3 (#227), Qwen1.5-7B: 33.1 (#261)
| Benchmark | DeepSeek-V3.1 | Qwen1.5-7B |
|---|---|---|
| LMArena Longer Query | 1422 | 1090 |
| Fiction.LiveBench | 52.8% | — |
Writing & Preference DeepSeek-V3.1 leads
DeepSeek-V3.1: 60.5 (#93), Qwen1.5-7B: 29.8 (#288)
| Benchmark | DeepSeek-V3.1 | Qwen1.5-7B |
|---|---|---|
| LMArena Text | 1420 | 1084 |
| LMArena Creative Writing | 1401 | 1035 |
| LMArena Multi-Turn | 1408 | 1061 |
| EQ-Bench Creative Writing | 1436 | — |
Frequently asked questions
Is DeepSeek-V3.1 better than Qwen1.5-7B?
DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 30.8 on the Noometry Index.
Is DeepSeek-V3.1 or Qwen1.5-7B better for coding?
DeepSeek-V3.1 scores higher on coding benchmarks: 39.8 versus 31.6 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.1 and Qwen1.5-7B share?
12 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Qwen1.5-7B has 13.