Model comparison
DeepSeek-V3.1-Terminus vs Qwen Turbo
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 27.1 on the Noometry Index. Qwen Turbo costs 5.2× less per token, which makes it the better buy when DeepSeek-V3.1-Terminus'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-Terminus leads 38.5 to 15.3.
- Qwen Turbo is cheaper at $0.05 / $0.20 per million input/output tokens, against $0.27 / $1 for DeepSeek-V3.1-Terminus.
- Qwen Turbo accepts more context: 1M tokens versus 164K.
- DeepSeek-V3.1-Terminus has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.1-Terminus | Qwen Turbo | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 43.1 | 27.1 |
| Released | 2025-09-22 | 2024-11-01 |
| Weights | Open | Proprietary |
| Context window | 164K | 1M |
| Max output | 147K | 16K |
| Input $ / M tokens | $0.27 | $0.05 |
| Output $ / M tokens | $1 | $0.20 |
| Results tracked | 16 | 3 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Not comparable
DeepSeek-V3.1-Terminus: 42.0 (#113), Qwen Turbo: —
| Benchmark | DeepSeek-V3.1-Terminus | Qwen Turbo |
|---|---|---|
| SciCode | 40.6% | — |
| LMArena Coding | 1426 | — |
| ALE-Bench | 745.17 | — |
Reasoning Not comparable
DeepSeek-V3.1-Terminus: 26.4 (#133), Qwen Turbo: —
| Benchmark | DeepSeek-V3.1-Terminus | Qwen Turbo |
|---|---|---|
| Kagi LLM Benchmark | 57.4% | — |
| CritPt | 1.7% | — |
| LMArena Hard Prompts | 1426 | — |
| DTBench | 81.3% | — |
| LMCA | 28.6% | — |
Math DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 38.5 (#137), Qwen Turbo: 15.3 (#297)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen Turbo |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 6.1% |
| LMArena Math | 1402 | — |
| MATH Level 5 | — | 56.2% |
Knowledge Not comparable
DeepSeek-V3.1-Terminus: —, Qwen Turbo: 22.2 (#272)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen Turbo |
|---|---|---|
| GPQA Diamond | — | 41.8% |
Multilingual Not comparable
DeepSeek-V3.1-Terminus: 52.1 (#92), Qwen Turbo: —
| Benchmark | DeepSeek-V3.1-Terminus | Qwen Turbo |
|---|---|---|
| LMArena Non-English | 1407 | — |
| LMArena Russian | 1436 | — |
Instruction Following Not comparable
DeepSeek-V3.1-Terminus: 74.0 (#106), Qwen Turbo: —
| Benchmark | DeepSeek-V3.1-Terminus | Qwen Turbo |
|---|---|---|
| LMArena Instruction Following | 1404 | — |
Long Context Not comparable
DeepSeek-V3.1-Terminus: 43.4 (#97), Qwen Turbo: —
| Benchmark | DeepSeek-V3.1-Terminus | Qwen Turbo |
|---|---|---|
| LMArena Longer Query | 1421 | — |
Writing & Preference Not comparable
DeepSeek-V3.1-Terminus: 61.0 (#92), Qwen Turbo: —
| Benchmark | DeepSeek-V3.1-Terminus | Qwen Turbo |
|---|---|---|
| LMArena Text | 1419 | — |
| LMArena Creative Writing | 1403 | — |
| LMArena Multi-Turn | 1411 | — |
Frequently asked questions
Is DeepSeek-V3.1-Terminus better than Qwen Turbo?
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 27.1 on the Noometry Index. Qwen Turbo costs 5.2× less per token, which makes it the better buy when DeepSeek-V3.1-Terminus's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.1-Terminus 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-Terminus lists at $0.27 and $1.
Which has the bigger context window?
Qwen Turbo does, with 1M tokens against 164K.
How many benchmarks do DeepSeek-V3.1-Terminus and Qwen Turbo share?
0 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and Qwen Turbo has 3.