Model comparison
DeepSeek-V3.1 vs Qwen Turbo
DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 26.8 on the Noometry Index. Qwen Turbo costs 4.9× less per token, which makes it the better buy when DeepSeek-V3.1's lead doesn't matter for your workload.
Last verified . 0 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- The widest gap is in math, where DeepSeek-V3.1 leads 36.1 to 14.5.
- Qwen Turbo is cheaper at $0.05 / $0.20 per million input/output tokens, against $0.25 / $0.95 for DeepSeek-V3.1.
- Qwen Turbo accepts more context: 1M tokens versus 164K.
- DeepSeek-V3.1 has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.1 | Qwen Turbo | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 42.2 | 26.8 |
| Released | 2025-08-21 | 2024-11-01 |
| Weights | Open | Proprietary |
| Context window | 164K | 1M |
| Max output | 8K | 16K |
| Input $ / M tokens | $0.25 | $0.05 |
| Output $ / M tokens | $0.95 | $0.20 |
| Results tracked | 27 | 3 |
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Category by category
Coding Not comparable
DeepSeek-V3.1: 39.8 (#140), Qwen Turbo: —
| Benchmark | DeepSeek-V3.1 | Qwen Turbo |
|---|---|---|
| WeirdML | 38.4% | — |
| LMArena Coding | 1417 | — |
Reasoning Not comparable
DeepSeek-V3.1: 27.3 (#109), Qwen Turbo: —
| Benchmark | DeepSeek-V3.1 | Qwen Turbo |
|---|---|---|
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| LMArena Hard Prompts | 1417 | — |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| Epoch Capabilities Index | 139.92 | — |
| ForecastBench | 58 | — |
Math DeepSeek-V3.1 leads
DeepSeek-V3.1: 36.1 (#125), Qwen Turbo: 14.5 (#291)
| Benchmark | DeepSeek-V3.1 | Qwen Turbo |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 6.1% |
| LMArena Math | 1420 | — |
| MATH Level 5 | — | 56.2% |
Knowledge DeepSeek-V3.1 leads
DeepSeek-V3.1: 43.0 (#86), Qwen Turbo: 22.2 (#267)
| Benchmark | DeepSeek-V3.1 | Qwen Turbo |
|---|---|---|
| GPQA Diamond | — | 41.8% |
| Vectara Hallucination Rate | 5.5% | — |
| LMArena Expert | 1403 | — |
Multilingual Not comparable
DeepSeek-V3.1: 51.8 (#100), Qwen Turbo: —
| Benchmark | DeepSeek-V3.1 | Qwen Turbo |
|---|---|---|
| LMArena Non-English | 1400 | — |
| LMArena Chinese | 1470 | — |
| LMArena French | 1447 | — |
| LMArena German | 1412 | — |
| LMArena Japanese | 1374 | — |
| LMArena Korean | 1337 | — |
| LMArena Russian | 1405 | — |
| LMArena Spanish | 1429 | — |
Instruction Following Not comparable
DeepSeek-V3.1: 74.1 (#105), Qwen Turbo: —
| Benchmark | DeepSeek-V3.1 | Qwen Turbo |
|---|---|---|
| LMArena Instruction Following | 1400 | — |
Long Context Not comparable
DeepSeek-V3.1: 36.3 (#227), Qwen Turbo: —
| Benchmark | DeepSeek-V3.1 | Qwen Turbo |
|---|---|---|
| Fiction.LiveBench | 52.8% | — |
| LMArena Longer Query | 1422 | — |
Writing & Preference Not comparable
DeepSeek-V3.1: 60.5 (#93), Qwen Turbo: —
| Benchmark | DeepSeek-V3.1 | Qwen Turbo |
|---|---|---|
| LMArena Text | 1420 | — |
| LMArena Creative Writing | 1401 | — |
| EQ-Bench Creative Writing | 1436 | — |
| LMArena Multi-Turn | 1408 | — |
Frequently asked questions
Is DeepSeek-V3.1 better than Qwen Turbo?
DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 26.8 on the Noometry Index. Qwen Turbo costs 4.9× less per token, which makes it the better buy when DeepSeek-V3.1's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.1 or Qwen Turbo?
Qwen Turbo is cheaper. It lists at $0.05 per million input tokens and $0.20 per million output tokens; DeepSeek-V3.1 lists at $0.25 and $0.95.
Which has the bigger context window?
Qwen Turbo does, with 1M tokens against 164K.
How many benchmarks do DeepSeek-V3.1 and Qwen Turbo share?
0 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Qwen Turbo has 3.