Model comparison
DeepSeek-V3.2-Exp vs Qwen3 32B
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 38.8 on the Noometry Index.
Last verified . 26 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- They share 26 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 9 categories and Qwen3 32B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.2-Exp leads 51.6 to 40.0.
- The biggest single-benchmark swing is Aider Polyglot: 74.2% for DeepSeek-V3.2-Exp and 40% for Qwen3 32B.
- DeepSeek-V3.2-Exp is cheaper at $0.26 / $0.38 per million input/output tokens, against $0.70 / $2.80 for Qwen3 32B.
- DeepSeek-V3.2-Exp accepts more context: 164K tokens versus 131K.
Side by side
| DeepSeek-V3.2-Exp | Qwen3 32B | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 44.9 | 38.8 |
| Released | 2025-09-29 | 2025-04 |
| Weights | Open | Open |
| Context window | 164K | 131K |
| Max output | 66K | 16K |
| Input $ / M tokens | $0.26 | $0.70 |
| Output $ / M tokens | $0.38 | $2.80 |
| Results tracked | 48 | 26 |
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Category by category
Coding DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 46.4 (#61), Qwen3 32B: 37.3 (#184)
| Benchmark | DeepSeek-V3.2-Exp | Qwen3 32B |
|---|---|---|
| Aider Polyglot | 74.2% | 40% |
| SciCode | 38.9% | 35.4% |
| LMArena Coding | 1454 | 1358 |
| SWE-bench Verified (bash only) | 70% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| WeirdML | 39.5% | — |
Agentic & Tool Use DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 37.9 (#31), Qwen3 32B: 32.3 (#58)
| Benchmark | DeepSeek-V3.2-Exp | Qwen3 32B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 56.7% | 48.7% |
| Terminal-Bench | 39.6% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |
Reasoning DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 22.0 (#185), Qwen3 32B: 19.6 (#233)
| Benchmark | DeepSeek-V3.2-Exp | Qwen3 32B |
|---|---|---|
| Kagi LLM Benchmark | 52.2% | 54.9% |
| CritPt | 2.9% | 0.3% |
| Chess Puzzles | 14% | 5% |
| LMArena Hard Prompts | 1433 | 1335 |
| DTBench | 87.7% | 67.5% |
| LMCA | 29.1% | 17.3% |
| Epoch Capabilities Index | 146.28 | 138.51 |
| ARC-AGI-2 | 4% | — |
| NYT Connections (extended) | 36.7% | — |
| ARC-AGI-1 | 57% | — |
| Thematic Generalization | 65% | — |
Math DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 40.8 (#75), Qwen3 32B: 37.9 (#89)
| Benchmark | DeepSeek-V3.2-Exp | Qwen3 32B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 87.8% | 66.9% |
| LMArena Math | 1435 | 1399 |
| MathArena Final-Answer Competitions | 57.7% | — |
| ProofBench | 8% | — |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 51.6 (#61), Qwen3 32B: 40.0 (#112)
| Benchmark | DeepSeek-V3.2-Exp | Qwen3 32B |
|---|---|---|
| GPQA Diamond | 83.4% | 65.7% |
| Vectara Hallucination Rate | 5.3% | 5.9% |
| LMArena Expert | 1436 | 1362 |
Multilingual DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 52.4 (#85), Qwen3 32B: 45.8 (#162)
| Benchmark | DeepSeek-V3.2-Exp | Qwen3 32B |
|---|---|---|
| LMArena Non-English | 1409 | 1317 |
| LMArena Chinese | 1461 | 1357 |
| LMArena German | 1440 | 1341 |
| LMArena Russian | 1423 | 1311 |
| LMArena French | 1435 | — |
| LMArena Japanese | 1377 | — |
| LMArena Korean | 1372 | — |
| LMArena Spanish | 1441 | — |
Instruction Following DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 74.7 (#89), Qwen3 32B: 69.1 (#174)
| Benchmark | DeepSeek-V3.2-Exp | Qwen3 32B |
|---|---|---|
| LMArena Instruction Following | 1413 | 1305 |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#15), Qwen3 32B: 43.7 (#83)
| Benchmark | DeepSeek-V3.2-Exp | Qwen3 32B |
|---|---|---|
| Fiction.LiveBench | 83.3% | 74.2% |
| LMArena Longer Query | 1428 | 1327 |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
Writing & Preference DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 62.5 (#73), Qwen3 32B: 53.1 (#158)
| Benchmark | DeepSeek-V3.2-Exp | Qwen3 32B |
|---|---|---|
| LMArena Text | 1425 | 1340 |
| LMArena Creative Writing | 1403 | 1297 |
| LMArena Multi-Turn | 1427 | 1331 |
| EQ-Bench Creative Writing | 1515 | — |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than Qwen3 32B?
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 38.8 on the Noometry Index.
Which is cheaper, DeepSeek-V3.2-Exp or Qwen3 32B?
DeepSeek-V3.2-Exp is cheaper. It lists at $0.26 per million input tokens and $0.38 per million output tokens; Qwen3 32B lists at $0.70 and $2.80.
Is DeepSeek-V3.2-Exp or Qwen3 32B better for coding?
DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.4 versus 37.3 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-V3.2-Exp does, with 164K tokens against 131K.
How many benchmarks do DeepSeek-V3.2-Exp and Qwen3 32B share?
26 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 48 scored results on Noometry and Qwen3 32B has 26.