Model comparison
DeepSeek-V2.5 (Sep 2024) vs Qwen3 32B
Qwen3 32B is the stronger model overall, scoring 39.2 to 37.6 on the Noometry Index.
Last verified . 14 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- They share 14 benchmarks with published results for both. DeepSeek-V2.5 (Sep 2024) scores higher in 1 category and Qwen3 32B in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in coding, where Qwen3 32B leads 37.7 to 31.7.
- The biggest single-benchmark swing is Aider Polyglot: 17.8% for DeepSeek-V2.5 (Sep 2024) and 40% for Qwen3 32B.
Side by side
| DeepSeek-V2.5 (Sep 2024) | Qwen3 32B | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 37.6 | 39.2 |
| Released | 2024-09-06 | 2025-04 |
| Weights | Open | Open |
| Context window | — | 131K |
| Max output | — | 16K |
| Input $ / M tokens | — | $0.70 |
| Output $ / M tokens | — | $2.80 |
| Results tracked | 22 | 26 |
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Category by category
Coding Qwen3 32B leads
DeepSeek-V2.5 (Sep 2024): 31.7 (#281), Qwen3 32B: 37.7 (#190)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3 32B |
|---|---|---|
| Aider Polyglot | 17.8% | 40% |
| LMArena Coding | 1309 | 1358 |
| SciCode | — | 35.4% |
| BigCodeBench Instruct | 48.6% | — |
| BigCodeBench Complete | 53.2% | — |
| HumanEval+ | 83.5% | — |
| MBPP+ | 74.1% | — |
Agentic & Tool Use Not comparable
DeepSeek-V2.5 (Sep 2024): —, Qwen3 32B: 32.6 (#62)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3 32B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 48.7% |
Reasoning DeepSeek-V2.5 (Sep 2024) leads
DeepSeek-V2.5 (Sep 2024): 25.6 (#145), Qwen3 32B: 20.2 (#241)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3 32B |
|---|---|---|
| LMArena Hard Prompts | 1289 | 1334 |
| Kagi LLM Benchmark | — | 54.9% |
| CritPt | — | 0.3% |
| Chess Puzzles | — | 5% |
| DTBench | — | 67.5% |
| LMCA | — | 17.3% |
| Epoch Capabilities Index | — | 138.51 |
Math Qwen3 32B leads
DeepSeek-V2.5 (Sep 2024): 35.9 (#177), Qwen3 32B: 39.7 (#99)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3 32B |
|---|---|---|
| LMArena Math | 1288 | 1399 |
| OTIS Mock AIME 2024-2025 | — | 66.9% |
Knowledge Qwen3 32B leads
DeepSeek-V2.5 (Sep 2024): 34.8 (#193), Qwen3 32B: 40.0 (#125)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3 32B |
|---|---|---|
| LMArena Expert | 1266 | 1362 |
| GPQA Diamond | — | 65.7% |
| Vectara Hallucination Rate | — | 5.9% |
Multilingual Qwen3 32B leads
DeepSeek-V2.5 (Sep 2024): 42.5 (#193), Qwen3 32B: 45.6 (#167)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3 32B |
|---|---|---|
| LMArena Non-English | 1273 | 1317 |
| LMArena Chinese | 1318 | 1357 |
| LMArena German | 1258 | 1341 |
| LMArena Russian | 1289 | 1311 |
| LMArena French | 1289 | — |
| LMArena Japanese | 1228 | — |
| LMArena Korean | 1209 | — |
| LMArena Spanish | 1248 | — |
Instruction Following Qwen3 32B leads
DeepSeek-V2.5 (Sep 2024): 67.5 (#194), Qwen3 32B: 68.9 (#179)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3 32B |
|---|---|---|
| LMArena Instruction Following | 1280 | 1305 |
Long Context Qwen3 32B leads
DeepSeek-V2.5 (Sep 2024): 39.5 (#174), Qwen3 32B: 43.8 (#87)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3 32B |
|---|---|---|
| LMArena Longer Query | 1301 | 1327 |
| Fiction.LiveBench | — | 74.2% |
Writing & Preference Qwen3 32B leads
DeepSeek-V2.5 (Sep 2024): 49.8 (#187), Qwen3 32B: 52.9 (#163)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3 32B |
|---|---|---|
| LMArena Text | 1294 | 1340 |
| LMArena Creative Writing | 1285 | 1297 |
| LMArena Multi-Turn | 1297 | 1331 |
Frequently asked questions
Is DeepSeek-V2.5 (Sep 2024) better than Qwen3 32B?
Qwen3 32B is the stronger model overall, scoring 39.2 to 37.6 on the Noometry Index.
Is DeepSeek-V2.5 (Sep 2024) or Qwen3 32B better for coding?
Qwen3 32B scores higher on coding benchmarks: 37.7 versus 31.7 in the Noometry coding category.
How many benchmarks do DeepSeek-V2.5 (Sep 2024) and Qwen3 32B share?
14 benchmarks have published results for both models. DeepSeek-V2.5 (Sep 2024) has 22 scored results on Noometry and Qwen3 32B has 26.