Model comparison
DeepSeek-V3.1 vs Qwen-14B
DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 30.8 on the Noometry Index.
Last verified . 11 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- They share 11 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 7 categories and Qwen-14B in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.1 leads 60.5 to 27.8.
Side by side
| DeepSeek-V3.1 | Qwen-14B | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 42.2 | 30.8 |
| Released | 2025-08-21 | 2023-09-24 |
| Weights | Open | Open |
| Context window | 164K | — |
| Max output | 8K | — |
| Input $ / M tokens | $0.25 | — |
| Output $ / M tokens | $0.95 | — |
| Results tracked | 27 | 18 |
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Category by category
Coding DeepSeek-V3.1 leads
DeepSeek-V3.1: 39.8 (#140), Qwen-14B: 30.6 (#283)
| Benchmark | DeepSeek-V3.1 | Qwen-14B |
|---|---|---|
| LMArena Coding | 1417 | 1072 |
| WeirdML | 38.4% | — |
Reasoning DeepSeek-V3.1 leads
DeepSeek-V3.1: 27.3 (#109), Qwen-14B: 18.7 (#255)
| Benchmark | DeepSeek-V3.1 | Qwen-14B |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1027 |
| Epoch Capabilities Index | 139.92 | 112.8 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| BIG-Bench Hard | — | 55% |
| ForecastBench | 58 | — |
| LAMBADA | — | 71.1% |
| PIQA | — | 79.9% |
Math DeepSeek-V3.1 leads
DeepSeek-V3.1: 36.1 (#125), Qwen-14B: 28.7 (#224)
| Benchmark | DeepSeek-V3.1 | Qwen-14B |
|---|---|---|
| LMArena Math | 1420 | 1068 |
| GSM8K | — | 61.3% |
Knowledge Not comparable
DeepSeek-V3.1: 43.0 (#86), Qwen-14B: —
| Benchmark | DeepSeek-V3.1 | Qwen-14B |
|---|---|---|
| Vectara Hallucination Rate | 5.5% | — |
| LMArena Expert | 1403 | — |
| ARC (AI2) Challenge | — | 84.4% |
| BoolQ | — | 86.2% |
| MMLU | — | 66.3% |
Multilingual DeepSeek-V3.1 leads
DeepSeek-V3.1: 51.8 (#100), Qwen-14B: 27.7 (#270)
| Benchmark | DeepSeek-V3.1 | Qwen-14B |
|---|---|---|
| LMArena Non-English | 1400 | 1042 |
| LMArena Chinese | 1470 | 1077 |
| LMArena French | 1447 | — |
| LMArena German | 1412 | — |
| LMArena Japanese | 1374 | — |
| LMArena Korean | 1337 | — |
| LMArena Russian | 1405 | — |
| LMArena Spanish | 1429 | — |
Instruction Following DeepSeek-V3.1 leads
DeepSeek-V3.1: 74.1 (#105), Qwen-14B: 52.6 (#284)
| Benchmark | DeepSeek-V3.1 | Qwen-14B |
|---|---|---|
| LMArena Instruction Following | 1400 | 1031 |
Long Context DeepSeek-V3.1 leads
DeepSeek-V3.1: 36.3 (#227), Qwen-14B: 31.3 (#276)
| Benchmark | DeepSeek-V3.1 | Qwen-14B |
|---|---|---|
| LMArena Longer Query | 1422 | 1029 |
| Fiction.LiveBench | 52.8% | — |
Writing & Preference DeepSeek-V3.1 leads
DeepSeek-V3.1: 60.5 (#93), Qwen-14B: 27.8 (#294)
| Benchmark | DeepSeek-V3.1 | Qwen-14B |
|---|---|---|
| LMArena Text | 1420 | 1051 |
| LMArena Creative Writing | 1401 | 1028 |
| LMArena Multi-Turn | 1408 | 1022 |
| EQ-Bench Creative Writing | 1436 | — |
Frequently asked questions
Is DeepSeek-V3.1 better than Qwen-14B?
DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 30.8 on the Noometry Index.
Is DeepSeek-V3.1 or Qwen-14B better for coding?
DeepSeek-V3.1 scores higher on coding benchmarks: 39.8 versus 30.6 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.1 and Qwen-14B share?
11 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Qwen-14B has 18.