Model comparison
GPT-5.2 Pro vs Qwen3 32B
GPT-5.2 Pro is the stronger model overall, scoring 52.2 to 38.8 on the Noometry Index. Qwen3 32B costs 47× less per token, which makes it the better buy when GPT-5.2 Pro's lead doesn't matter for your workload.
Last verified . 1 shared benchmarks.
Alibaba (Qwen)Summary
- They share 1 benchmark with published results for both. GPT-5.2 Pro scores higher in 2 categories and Qwen3 32B in 0 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.2 Pro leads 51.8 to 19.6.
- Qwen3 32B is cheaper at $0.70 / $2.80 per million input/output tokens, against $21 / $168 for GPT-5.2 Pro.
- GPT-5.2 Pro accepts more context: 400K tokens versus 131K.
- Qwen3 32B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.2 Pro | Qwen3 32B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 52.2 | 38.8 |
| Released | 2025-12-11 | 2025-04 |
| Weights | Proprietary | Open |
| Context window | 400K | 131K |
| Max output | 128K | 16K |
| Input $ / M tokens | $21 | $0.70 |
| Output $ / M tokens | $168 | $2.80 |
| Results tracked | 8 | 26 |
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Category by category
Coding Not comparable
GPT-5.2 Pro: —, Qwen3 32B: 37.3 (#184)
| Benchmark | GPT-5.2 Pro | Qwen3 32B |
|---|---|---|
| Aider Polyglot | — | 40% |
| SciCode | — | 35.4% |
| LMArena Coding | — | 1358 |
Agentic & Tool Use Not comparable
GPT-5.2 Pro: —, Qwen3 32B: 32.3 (#58)
| Benchmark | GPT-5.2 Pro | Qwen3 32B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 48.7% |
Reasoning GPT-5.2 Pro leads
GPT-5.2 Pro: 51.8 (#31), Qwen3 32B: 19.6 (#233)
| Benchmark | GPT-5.2 Pro | Qwen3 32B |
|---|---|---|
| Epoch Capabilities Index | 155.41 | 138.51 |
| ARC-AGI-2 | 54.2% | — |
| SimpleBench | 57.4% | — |
| Kagi LLM Benchmark | — | 54.9% |
| NYT Connections (extended) | 79.3% | — |
| ARC-AGI-1 | 90.5% | — |
| CritPt | — | 0.3% |
| Chess Puzzles | — | 5% |
| LMArena Hard Prompts | — | 1335 |
| DTBench | — | 67.5% |
| LMCA | — | 17.3% |
Math GPT-5.2 Pro leads
GPT-5.2 Pro: 64.8 (#23), Qwen3 32B: 37.9 (#89)
| Benchmark | GPT-5.2 Pro | Qwen3 32B |
|---|---|---|
| FrontierMath (Tiers 1-3) | 74% | — |
| FrontierMath Tier 4 | 46% | — |
| OTIS Mock AIME 2024-2025 | — | 66.9% |
| LMArena Math | — | 1399 |
| FrontierMath Tier 4 (v1) | 31.3% | — |
Knowledge Not comparable
GPT-5.2 Pro: —, Qwen3 32B: 40.0 (#112)
| Benchmark | GPT-5.2 Pro | Qwen3 32B |
|---|---|---|
| GPQA Diamond | — | 65.7% |
| Vectara Hallucination Rate | — | 5.9% |
| LMArena Expert | — | 1362 |
Multilingual Not comparable
GPT-5.2 Pro: —, Qwen3 32B: 45.8 (#162)
| Benchmark | GPT-5.2 Pro | Qwen3 32B |
|---|---|---|
| LMArena Non-English | — | 1317 |
| LMArena Chinese | — | 1357 |
| LMArena German | — | 1341 |
| LMArena Russian | — | 1311 |
Instruction Following Not comparable
GPT-5.2 Pro: —, Qwen3 32B: 69.1 (#174)
| Benchmark | GPT-5.2 Pro | Qwen3 32B |
|---|---|---|
| LMArena Instruction Following | — | 1305 |
Long Context Not comparable
GPT-5.2 Pro: —, Qwen3 32B: 43.7 (#83)
| Benchmark | GPT-5.2 Pro | Qwen3 32B |
|---|---|---|
| Fiction.LiveBench | — | 74.2% |
| LMArena Longer Query | — | 1327 |
Writing & Preference Not comparable
GPT-5.2 Pro: —, Qwen3 32B: 53.1 (#158)
| Benchmark | GPT-5.2 Pro | Qwen3 32B |
|---|---|---|
| LMArena Text | — | 1340 |
| LMArena Creative Writing | — | 1297 |
| LMArena Multi-Turn | — | 1331 |
Frequently asked questions
Is GPT-5.2 Pro better than Qwen3 32B?
GPT-5.2 Pro is the stronger model overall, scoring 52.2 to 38.8 on the Noometry Index. Qwen3 32B costs 47× less per token, which makes it the better buy when GPT-5.2 Pro's lead doesn't matter for your workload.
Which is cheaper, GPT-5.2 Pro or Qwen3 32B?
Qwen3 32B is cheaper. It lists at $0.70 per million input tokens and $2.80 per million output tokens; GPT-5.2 Pro lists at $21 and $168.
Which has the bigger context window?
GPT-5.2 Pro does, with 400K tokens against 131K.
How many benchmarks do GPT-5.2 Pro and Qwen3 32B share?
1 benchmark has published results for both models. GPT-5.2 Pro has 8 scored results on Noometry and Qwen3 32B has 26.