Model comparison
DeepSeek-V2.5 (Sep 2024) vs Qwen3.7 Flash
Qwen3.7 Flash is the stronger model overall, scoring 39.9 to 37.6 on the Noometry Index.
Last verified . 0 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- The widest gap is in knowledge, where Qwen3.7 Flash leads 48.9 to 34.8.
- DeepSeek-V2.5 (Sep 2024) has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V2.5 (Sep 2024) | Qwen3.7 Flash | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 37.6 | 39.9 |
| Released | 2024-09-06 | 2026-07-15 |
| Weights | Open | Proprietary |
| Context window | — | 1M |
| Max output | — | 131K |
| Input $ / M tokens | — | $0.03 |
| Output $ / M tokens | — | $0.13 |
| Results tracked | 22 | 7 |
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Category by category
Coding Not comparable
DeepSeek-V2.5 (Sep 2024): 31.7 (#281), Qwen3.7 Flash: —
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3.7 Flash |
|---|---|---|
| Aider Polyglot | 17.8% | — |
| BigCodeBench Instruct | 48.6% | — |
| LMArena Coding | 1309 | — |
| BigCodeBench Complete | 53.2% | — |
| HumanEval+ | 83.5% | — |
| MBPP+ | 74.1% | — |
Reasoning Qwen3.7 Flash leads
DeepSeek-V2.5 (Sep 2024): 25.6 (#145), Qwen3.7 Flash: 28.2 (#108)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3.7 Flash |
|---|---|---|
| NYT Connections (extended) | — | 43.8% |
| Chess Puzzles | — | 23% |
| LMArena Hard Prompts | 1289 | — |
| Mystery Game Puzzles | — | 15% |
| Epoch Capabilities Index | — | 144.64 |
Math Qwen3.7 Flash leads
DeepSeek-V2.5 (Sep 2024): 35.9 (#177), Qwen3.7 Flash: 38.3 (#140)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3.7 Flash |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 19.3% |
| OTIS Mock AIME 2024-2025 | — | 86.7% |
| LMArena Math | 1288 | — |
Knowledge Qwen3.7 Flash leads
DeepSeek-V2.5 (Sep 2024): 34.8 (#193), Qwen3.7 Flash: 48.9 (#75)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3.7 Flash |
|---|---|---|
| GPQA Diamond | — | 82.3% |
| LMArena Expert | 1266 | — |
Multilingual Not comparable
DeepSeek-V2.5 (Sep 2024): 42.5 (#193), Qwen3.7 Flash: —
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3.7 Flash |
|---|---|---|
| LMArena Non-English | 1273 | — |
| LMArena Chinese | 1318 | — |
| LMArena French | 1289 | — |
| LMArena German | 1258 | — |
| LMArena Japanese | 1228 | — |
| LMArena Korean | 1209 | — |
| LMArena Russian | 1289 | — |
| LMArena Spanish | 1248 | — |
Instruction Following Not comparable
DeepSeek-V2.5 (Sep 2024): 67.5 (#194), Qwen3.7 Flash: —
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3.7 Flash |
|---|---|---|
| LMArena Instruction Following | 1280 | — |
Long Context Not comparable
DeepSeek-V2.5 (Sep 2024): 39.5 (#174), Qwen3.7 Flash: —
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3.7 Flash |
|---|---|---|
| LMArena Longer Query | 1301 | — |
Writing & Preference Not comparable
DeepSeek-V2.5 (Sep 2024): 49.8 (#187), Qwen3.7 Flash: —
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3.7 Flash |
|---|---|---|
| LMArena Text | 1294 | — |
| LMArena Creative Writing | 1285 | — |
| LMArena Multi-Turn | 1297 | — |
Frequently asked questions
Is DeepSeek-V2.5 (Sep 2024) better than Qwen3.7 Flash?
Qwen3.7 Flash is the stronger model overall, scoring 39.9 to 37.6 on the Noometry Index.
How many benchmarks do DeepSeek-V2.5 (Sep 2024) and Qwen3.7 Flash share?
0 benchmarks have published results for both models. DeepSeek-V2.5 (Sep 2024) has 22 scored results on Noometry and Qwen3.7 Flash has 7.