Model comparison
o3-pro vs Qwen1.5-32B
o3-pro is the stronger model overall, scoring 42.9 to 30.5 on the Noometry Index.
Last verified . 0 shared benchmarks.
Alibaba (Qwen)Summary
- The widest gap is in long context, where o3-pro leads 72.2 to 34.7.
- Qwen1.5-32B has downloadable open weights; the other is API-only.
Side by side
| o3-pro | Qwen1.5-32B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 42.9 | 30.5 |
| Released | 2025-06-10 | 2024-02-04 |
| Weights | Proprietary | Open |
| Context window | 200K | — |
| Max output | 100K | — |
| Input $ / M tokens | $20 | — |
| Output $ / M tokens | $80 | — |
| Results tracked | 12 | 21 |
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Category by category
Coding o3-pro leads
o3-pro: 55.5 (#24), Qwen1.5-32B: 31.7 (#282)
| Benchmark | o3-pro | Qwen1.5-32B |
|---|---|---|
| Aider Polyglot | 84.9% | — |
| WeirdML | 58.2% | — |
| BigCodeBench Instruct | — | 32.3% |
| LMArena Coding | — | 1155 |
| BigCodeBench Complete | — | 42% |
Reasoning o3-pro leads
o3-pro: 23.8 (#171), Qwen1.5-32B: 21.8 (#212)
| Benchmark | o3-pro | Qwen1.5-32B |
|---|---|---|
| ARC-AGI-2 | 4.9% | — |
| Kagi LLM Benchmark | 72.1% | — |
| ARC-AGI-1 | 59.3% | — |
| LMArena Hard Prompts | — | 1130 |
| DTBench | 86.9% | — |
| LMCA | 38.5% | — |
| Epoch Capabilities Index | 147.42 | — |
Math Not comparable
o3-pro: —, Qwen1.5-32B: 33.0 (#207)
| Benchmark | o3-pro | Qwen1.5-32B |
|---|---|---|
| LMArena Math | — | 1155 |
Knowledge o3-pro leads
o3-pro: 29.5 (#238), Qwen1.5-32B: 13.5 (#296)
| Benchmark | o3-pro | Qwen1.5-32B |
|---|---|---|
| GPQA Diamond | — | 30.7% |
| Confabulations | 14.2% | — |
| Vectara Hallucination Rate | 23.3% | — |
| LMArena Expert | — | 1126 |
| MMLU | — | 74.4% |
Multilingual Not comparable
o3-pro: —, Qwen1.5-32B: 31.4 (#259)
| Benchmark | o3-pro | Qwen1.5-32B |
|---|---|---|
| LMArena Non-English | — | 1106 |
| LMArena Chinese | — | 1177 |
| LMArena French | — | 1101 |
| LMArena German | — | 1058 |
| LMArena Japanese | — | 1027 |
| LMArena Korean | — | 1008 |
| LMArena Russian | — | 1073 |
| LMArena Spanish | — | 1089 |
Instruction Following Not comparable
o3-pro: —, Qwen1.5-32B: 57.7 (#265)
| Benchmark | o3-pro | Qwen1.5-32B |
|---|---|---|
| LMArena Instruction Following | — | 1116 |
Long Context o3-pro leads
o3-pro: 72.2 (#1), Qwen1.5-32B: 34.7 (#246)
| Benchmark | o3-pro | Qwen1.5-32B |
|---|---|---|
| Fiction.LiveBench | 97.2% | — |
| LMArena Longer Query | — | 1146 |
Writing & Preference o3-pro leads
o3-pro: 57.1 (#133), Qwen1.5-32B: 34.2 (#271)
| Benchmark | o3-pro | Qwen1.5-32B |
|---|---|---|
| LMArena Text | — | 1137 |
| LMArena Creative Writing | — | 1083 |
| Short-Story Creative Writing | 84.4% | — |
| LMArena Multi-Turn | — | 1140 |
Frequently asked questions
Is o3-pro better than Qwen1.5-32B?
o3-pro is the stronger model overall, scoring 42.9 to 30.5 on the Noometry Index.
Is o3-pro or Qwen1.5-32B better for coding?
o3-pro scores higher on coding benchmarks: 55.5 versus 31.7 in the Noometry coding category.
How many benchmarks do o3-pro and Qwen1.5-32B share?
0 benchmarks have published results for both models. o3-pro has 12 scored results on Noometry and Qwen1.5-32B has 21.