Model comparison
DeepSeek-V2.5 (Sep 2024) vs Qwen3.5-9B
DeepSeek-V2.5 (Sep 2024) is the stronger model overall, scoring 37.0 to 33.3 on the Noometry Index.
Last verified . 0 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- The widest gap is in knowledge, where Qwen3.5-9B leads 46.0 to 34.2.
Side by side
| DeepSeek-V2.5 (Sep 2024) | Qwen3.5-9B | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 37.0 | 33.3 |
| Released | 2024-09-06 | 2026-02-23 |
| Weights | Open | Open |
| Context window | — | 262K |
| Max output | — | 66K |
| Input $ / M tokens | — | $0.10 |
| Output $ / M tokens | — | $0.15 |
| Results tracked | 22 | 10 |
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Category by category
Coding Qwen3.5-9B leads
DeepSeek-V2.5 (Sep 2024): 31.3 (#274), Qwen3.5-9B: 35.3 (#213)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3.5-9B |
|---|---|---|
| Aider Polyglot | 17.8% | — |
| SciCode | — | 27.5% |
| BigCodeBench Instruct | 48.6% | — |
| LMArena Coding | 1310 | — |
| BigCodeBench Complete | 53.2% | — |
| HumanEval+ | 83.5% | — |
| MBPP+ | 74.1% | — |
Agentic & Tool Use Not comparable
DeepSeek-V2.5 (Sep 2024): —, Qwen3.5-9B: 14.4 (#143)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3.5-9B |
|---|---|---|
| Terminal-Bench | — | 9.2% |
Reasoning DeepSeek-V2.5 (Sep 2024) leads
DeepSeek-V2.5 (Sep 2024): 24.5 (#145), Qwen3.5-9B: 22.5 (#178)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3.5-9B |
|---|---|---|
| CritPt | — | 0.3% |
| Chess Puzzles | — | 12% |
| LMArena Hard Prompts | 1289 | — |
| DTBench | — | 71.2% |
| LMCA | — | 24.5% |
| Epoch Capabilities Index | — | 139.45 |
Math Too close to call
DeepSeek-V2.5 (Sep 2024): 33.3 (#177), Qwen3.5-9B: 33.5 (#174)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3.5-9B |
|---|---|---|
| MathArena Final-Answer Competitions | — | 48.5% |
| OTIS Mock AIME 2024-2025 | — | 61.7% |
| LMArena Math | 1289 | — |
Knowledge Qwen3.5-9B leads
DeepSeek-V2.5 (Sep 2024): 34.2 (#190), Qwen3.5-9B: 46.0 (#77)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3.5-9B |
|---|---|---|
| GPQA Diamond | — | 79% |
| LMArena Expert | 1266 | — |
Multilingual Not comparable
DeepSeek-V2.5 (Sep 2024): 42.7 (#188), Qwen3.5-9B: —
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3.5-9B |
|---|---|---|
| LMArena Non-English | 1273 | — |
| LMArena Chinese | 1317 | — |
| LMArena French | 1288 | — |
| LMArena German | 1258 | — |
| LMArena Japanese | 1228 | — |
| LMArena Korean | 1209 | — |
| LMArena Russian | 1289 | — |
| LMArena Spanish | 1248 | — |
Instruction Following Not comparable
DeepSeek-V2.5 (Sep 2024): 67.7 (#189), Qwen3.5-9B: —
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3.5-9B |
|---|---|---|
| LMArena Instruction Following | 1280 | — |
Long Context Not comparable
DeepSeek-V2.5 (Sep 2024): 39.5 (#169), Qwen3.5-9B: —
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3.5-9B |
|---|---|---|
| LMArena Longer Query | 1301 | — |
Writing & Preference Not comparable
DeepSeek-V2.5 (Sep 2024): 50.0 (#182), Qwen3.5-9B: —
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3.5-9B |
|---|---|---|
| LMArena Text | 1294 | — |
| LMArena Creative Writing | 1285 | — |
| LMArena Multi-Turn | 1297 | — |
Frequently asked questions
Is DeepSeek-V2.5 (Sep 2024) better than Qwen3.5-9B?
DeepSeek-V2.5 (Sep 2024) is the stronger model overall, scoring 37.0 to 33.3 on the Noometry Index.
Is DeepSeek-V2.5 (Sep 2024) or Qwen3.5-9B better for coding?
Qwen3.5-9B scores higher on coding benchmarks: 35.3 versus 31.3 in the Noometry coding category.
How many benchmarks do DeepSeek-V2.5 (Sep 2024) and Qwen3.5-9B share?
0 benchmarks have published results for both models. DeepSeek-V2.5 (Sep 2024) has 22 scored results on Noometry and Qwen3.5-9B has 10.