Model comparison
o3-pro vs Qwen1.5-110B
o3-pro is the stronger model overall, scoring 42.7 to 33.6 on the Noometry Index.
Last verified . 0 shared benchmarks.
Alibaba (Qwen)Summary
- The widest gap is in long context, where o3-pro leads 72.1 to 35.0.
- Qwen1.5-110B has downloadable open weights; the other is API-only.
Side by side
| o3-pro | Qwen1.5-110B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 42.7 | 33.6 |
| Released | 2025-06-10 | 2024-04-25 |
| Weights | Proprietary | Open |
| Context window | 200K | — |
| Max output | 100K | — |
| Input $ / M tokens | $20 | — |
| Output $ / M tokens | $80 | — |
| Results tracked | 12 | 20 |
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Category by category
Coding o3-pro leads
o3-pro: 55.1 (#22), Qwen1.5-110B: 32.4 (#260)
| Benchmark | o3-pro | Qwen1.5-110B |
|---|---|---|
| Aider Polyglot | 84.9% | — |
| WeirdML | 58.2% | — |
| BigCodeBench Instruct | — | 35% |
| LMArena Coding | — | 1184 |
| BigCodeBench Complete | — | 44.4% |
Reasoning o3-pro leads
o3-pro: 24.1 (#152), Qwen1.5-110B: 21.7 (#192)
| Benchmark | o3-pro | Qwen1.5-110B |
|---|---|---|
| ARC-AGI-2 | 4.9% | — |
| Kagi LLM Benchmark | 72.1% | — |
| ARC-AGI-1 | 59.3% | — |
| LMArena Hard Prompts | — | 1168 |
| DTBench | 86.9% | — |
| LMCA | 38.5% | — |
| Epoch Capabilities Index | 147.43 | — |
| ForecastBench | — | 57.7 |
Math Not comparable
o3-pro: —, Qwen1.5-110B: 31.1 (#199)
| Benchmark | o3-pro | Qwen1.5-110B |
|---|---|---|
| LMArena Math | — | 1185 |
Knowledge Qwen1.5-110B leads
o3-pro: 29.0 (#234), Qwen1.5-110B: 30.7 (#217)
| Benchmark | o3-pro | Qwen1.5-110B |
|---|---|---|
| Confabulations | 14.2% | — |
| Vectara Hallucination Rate | 23.3% | — |
| LMArena Expert | — | 1144 |
Multilingual Not comparable
o3-pro: —, Qwen1.5-110B: 33.8 (#245)
| Benchmark | o3-pro | Qwen1.5-110B |
|---|---|---|
| LMArena Non-English | — | 1142 |
| LMArena Chinese | — | 1206 |
| LMArena French | — | 1150 |
| LMArena German | — | 1123 |
| LMArena Japanese | — | 1074 |
| LMArena Korean | — | 1044 |
| LMArena Russian | — | 1118 |
| LMArena Spanish | — | 1141 |
Instruction Following Not comparable
o3-pro: —, Qwen1.5-110B: 60.6 (#247)
| Benchmark | o3-pro | Qwen1.5-110B |
|---|---|---|
| LMArena Instruction Following | — | 1158 |
Long Context o3-pro leads
o3-pro: 72.1 (#1), Qwen1.5-110B: 35.0 (#237)
| Benchmark | o3-pro | Qwen1.5-110B |
|---|---|---|
| Fiction.LiveBench | 97.2% | — |
| LMArena Longer Query | — | 1157 |
Writing & Preference o3-pro leads
o3-pro: 57.1 (#128), Qwen1.5-110B: 38.1 (#250)
| Benchmark | o3-pro | Qwen1.5-110B |
|---|---|---|
| LMArena Text | — | 1175 |
| LMArena Creative Writing | — | 1148 |
| Short-Story Creative Writing | 84.4% | — |
| LMArena Multi-Turn | — | 1160 |
Frequently asked questions
Is o3-pro better than Qwen1.5-110B?
o3-pro is the stronger model overall, scoring 42.7 to 33.6 on the Noometry Index.
Is o3-pro or Qwen1.5-110B better for coding?
o3-pro scores higher on coding benchmarks: 55.1 versus 32.4 in the Noometry coding category.
How many benchmarks do o3-pro and Qwen1.5-110B share?
0 benchmarks have published results for both models. o3-pro has 12 scored results on Noometry and Qwen1.5-110B has 20.