Model comparison
DeepSeek-V3.1 vs Qwen2.5-VL 72B Instruct
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 29.9 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 1 category and Qwen2.5-VL 72B Instruct in 0 categories; one gap is clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek-V3.1 leads 27.9 to 20.7.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 53.2% for DeepSeek-V3.1 and 36% for Qwen2.5-VL 72B Instruct.
- DeepSeek-V3.1 is cheaper at $0.25 / $0.95 per million input/output tokens, against $2.80 / $8.40 for Qwen2.5-VL 72B Instruct.
- DeepSeek-V3.1 accepts more context: 164K tokens versus 131K.
Side by side
| DeepSeek-V3.1 | Qwen2.5-VL 72B Instruct | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 42.8 | 29.9 |
| Released | 2025-08-21 | 2024-09 |
| Weights | Open | Open |
| Context window | 164K | 131K |
| Max output | 8K | 8K |
| Input $ / M tokens | $0.25 | $2.80 |
| Output $ / M tokens | $0.95 | $8.40 |
| Results tracked | 27 | 6 |
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Category by category
Coding Not comparable
DeepSeek-V3.1: 40.3 (#144), Qwen2.5-VL 72B Instruct: —
| Benchmark | DeepSeek-V3.1 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| WeirdML | 38.4% | — |
| LMArena Coding | 1417 | — |
Agentic & Tool Use Not comparable
DeepSeek-V3.1: —, Qwen2.5-VL 72B Instruct: 18.6 (#144)
| Benchmark | DeepSeek-V3.1 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| OSWorld | — | 5% |
Reasoning DeepSeek-V3.1 leads
DeepSeek-V3.1: 27.9 (#110), Qwen2.5-VL 72B Instruct: 20.7 (#233)
| Benchmark | DeepSeek-V3.1 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| Kagi LLM Benchmark | 53.2% | 36% |
| SimpleBench | 40% | — |
| LMArena Hard Prompts | 1417 | — |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| Epoch Capabilities Index | 139.92 | — |
| ForecastBench | 58 | — |
Math Not comparable
DeepSeek-V3.1: 38.9 (#122), Qwen2.5-VL 72B Instruct: —
| Benchmark | DeepSeek-V3.1 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Math | 1420 | — |
Knowledge Not comparable
DeepSeek-V3.1: 43.7 (#90), Qwen2.5-VL 72B Instruct: —
| Benchmark | DeepSeek-V3.1 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| Vectara Hallucination Rate | 5.5% | — |
| LMArena Expert | 1405 | — |
Multimodal Not comparable
DeepSeek-V3.1: —, Qwen2.5-VL 72B Instruct: 33.5 (#97)
| Benchmark | DeepSeek-V3.1 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Vision | — | 1107 |
| Video-MME | — | 73.5% |
| GeoBench | — | 62% |
| SpatialViz-Bench | — | 33.3% |
Multilingual Not comparable
DeepSeek-V3.1: 51.6 (#106), Qwen2.5-VL 72B Instruct: —
| Benchmark | DeepSeek-V3.1 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Non-English | 1400 | — |
| LMArena Chinese | 1469 | — |
| LMArena French | 1447 | — |
| LMArena German | 1411 | — |
| LMArena Japanese | 1378 | — |
| LMArena Korean | 1337 | — |
| LMArena Russian | 1405 | — |
| LMArena Spanish | 1431 | — |
Instruction Following Not comparable
DeepSeek-V3.1: 73.9 (#110), Qwen2.5-VL 72B Instruct: —
| Benchmark | DeepSeek-V3.1 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Instruction Following | 1400 | — |
Long Context Not comparable
DeepSeek-V3.1: 36.3 (#232), Qwen2.5-VL 72B Instruct: —
| Benchmark | DeepSeek-V3.1 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| Fiction.LiveBench | 52.8% | — |
| LMArena Longer Query | 1422 | — |
Writing & Preference Not comparable
DeepSeek-V3.1: 60.3 (#98), Qwen2.5-VL 72B Instruct: —
| Benchmark | DeepSeek-V3.1 | Qwen2.5-VL 72B 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-VL 72B Instruct?
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 29.9 on the Noometry Index.
Which is cheaper, DeepSeek-V3.1 or Qwen2.5-VL 72B Instruct?
DeepSeek-V3.1 is cheaper. It lists at $0.25 per million input tokens and $0.95 per million output tokens; Qwen2.5-VL 72B Instruct lists at $2.80 and $8.40.
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-VL 72B Instruct share?
1 benchmark has published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Qwen2.5-VL 72B Instruct has 6.