Model comparison
DeepSeek-V3.1 vs Qwen2.5 32B Instruct
DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 29.7 on the Noometry Index.
Last verified . 1 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- They share 1 benchmark with published results for both. DeepSeek-V3.1 scores higher in 4 categories and Qwen2.5 32B Instruct in 0 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek-V3.1 leads 36.1 to 15.3.
- DeepSeek-V3.1 is cheaper at $0.25 / $0.95 per million input/output tokens, against $0.70 / $2.80 for Qwen2.5 32B Instruct.
- DeepSeek-V3.1 accepts more context: 164K tokens versus 131K.
Side by side
| DeepSeek-V3.1 | Qwen2.5 32B Instruct | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 42.2 | 29.7 |
| Released | 2025-08-21 | 2024-09 |
| Weights | Open | Open |
| Context window | 164K | 131K |
| Max output | 8K | 8K |
| Input $ / M tokens | $0.25 | $0.70 |
| Output $ / M tokens | $0.95 | $2.80 |
| Results tracked | 27 | 7 |
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Category by category
Coding DeepSeek-V3.1 leads
DeepSeek-V3.1: 39.8 (#140), Qwen2.5 32B Instruct: 38.1 (#165)
| Benchmark | DeepSeek-V3.1 | Qwen2.5 32B Instruct |
|---|---|---|
| WeirdML | 38.4% | — |
| BigCodeBench Instruct | — | 45% |
| LMArena Coding | 1417 | — |
| BigCodeBench Complete | — | 52.3% |
Reasoning DeepSeek-V3.1 leads
DeepSeek-V3.1: 27.3 (#109), Qwen2.5 32B Instruct: 18.5 (#262)
| Benchmark | DeepSeek-V3.1 | Qwen2.5 32B Instruct |
|---|---|---|
| Epoch Capabilities Index | 139.92 | 128.52 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| Chess Puzzles | — | 0% |
| LMArena Hard Prompts | 1417 | — |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| ForecastBench | 58 | — |
Math DeepSeek-V3.1 leads
DeepSeek-V3.1: 36.1 (#125), Qwen2.5 32B Instruct: 15.3 (#289)
| Benchmark | DeepSeek-V3.1 | Qwen2.5 32B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 7.4% |
| LMArena Math | 1420 | — |
| MATH Level 5 | — | 56.1% |
Knowledge DeepSeek-V3.1 leads
DeepSeek-V3.1: 43.0 (#86), Qwen2.5 32B Instruct: 24.9 (#259)
| Benchmark | DeepSeek-V3.1 | Qwen2.5 32B Instruct |
|---|---|---|
| GPQA Diamond | — | 46.1% |
| Vectara Hallucination Rate | 5.5% | — |
| LMArena Expert | 1403 | — |
Multilingual Not comparable
DeepSeek-V3.1: 51.8 (#100), Qwen2.5 32B Instruct: —
| Benchmark | DeepSeek-V3.1 | Qwen2.5 32B Instruct |
|---|---|---|
| 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), Qwen2.5 32B Instruct: —
| Benchmark | DeepSeek-V3.1 | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Instruction Following | 1400 | — |
Long Context Not comparable
DeepSeek-V3.1: 36.3 (#227), Qwen2.5 32B Instruct: —
| Benchmark | DeepSeek-V3.1 | Qwen2.5 32B Instruct |
|---|---|---|
| Fiction.LiveBench | 52.8% | — |
| LMArena Longer Query | 1422 | — |
Writing & Preference Not comparable
DeepSeek-V3.1: 60.5 (#93), Qwen2.5 32B Instruct: —
| Benchmark | DeepSeek-V3.1 | Qwen2.5 32B Instruct |
|---|---|---|
| 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 Qwen2.5 32B Instruct?
DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 29.7 on the Noometry Index.
Which is cheaper, DeepSeek-V3.1 or Qwen2.5 32B Instruct?
DeepSeek-V3.1 is cheaper. It lists at $0.25 per million input tokens and $0.95 per million output tokens; Qwen2.5 32B Instruct lists at $0.70 and $2.80.
Is DeepSeek-V3.1 or Qwen2.5 32B Instruct better for coding?
DeepSeek-V3.1 scores higher on coding benchmarks: 39.8 versus 38.1 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-V3.1 does, with 164K tokens against 131K.
How many benchmarks do DeepSeek-V3.1 and Qwen2.5 32B Instruct share?
1 benchmark has published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Qwen2.5 32B Instruct has 7.