Model comparison
o3-pro vs Qwen3.5 122B-A10B
o3-pro is the stronger model overall, scoring 42.7 to 41.6 on the Noometry Index. Qwen3.5 122B-A10B costs 32× less per token, which makes it the better buy when o3-pro's lead doesn't matter for your workload.
Last verified . 3 shared benchmarks.
Alibaba (Qwen)Summary
- They share 3 benchmarks with published results for both. o3-pro scores higher in 2 categories and Qwen3.5 122B-A10B in 3 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in long context, where o3-pro leads 72.1 to 43.0.
- The biggest single-benchmark swing is Vectara Hallucination Rate: 23.3% for o3-pro and 11.2% for Qwen3.5 122B-A10B.
- Qwen3.5 122B-A10B is cheaper at $0.40 / $3.20 per million input/output tokens, against $20 / $80 for o3-pro.
- Qwen3.5 122B-A10B accepts more context: 262K tokens versus 200K.
- Qwen3.5 122B-A10B has downloadable open weights; the other is API-only.
Side by side
| o3-pro | Qwen3.5 122B-A10B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 42.7 | 41.6 |
| Released | 2025-06-10 | 2026-02-23 |
| Weights | Proprietary | Open |
| Context window | 200K | 262K |
| Max output | 100K | 66K |
| Input $ / M tokens | $20 | $0.40 |
| Output $ / M tokens | $80 | $3.20 |
| Results tracked | 12 | 27 |
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Category by category
Coding o3-pro leads
o3-pro: 55.1 (#22), Qwen3.5 122B-A10B: 38.8 (#155)
| Benchmark | o3-pro | Qwen3.5 122B-A10B |
|---|---|---|
| Aider Polyglot | 84.9% | — |
| LMArena WebDev | — | 1360 |
| SciCode | — | 35.6% |
| WeirdML | 58.2% | — |
| LMArena Coding | — | 1436 |
Reasoning Qwen3.5 122B-A10B leads
o3-pro: 24.1 (#152), Qwen3.5 122B-A10B: 26.4 (#121)
| Benchmark | o3-pro | Qwen3.5 122B-A10B |
|---|---|---|
| DTBench | 86.9% | 84.3% |
| LMCA | 38.5% | 32.2% |
| ARC-AGI-2 | 4.9% | — |
| Kagi LLM Benchmark | 72.1% | — |
| NYT Connections (extended) | — | 51.7% |
| ARC-AGI-1 | 59.3% | — |
| CritPt | — | 0.9% |
| Thematic Generalization | — | 51.2% |
| LMArena Hard Prompts | — | 1421 |
| Mystery Game Puzzles | — | 17% |
| Epoch Capabilities Index | 147.43 | — |
Math Not comparable
o3-pro: —, Qwen3.5 122B-A10B: 36.4 (#114)
| Benchmark | o3-pro | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Math | — | 1432 |
Knowledge Qwen3.5 122B-A10B leads
o3-pro: 29.0 (#234), Qwen3.5 122B-A10B: 38.2 (#138)
| Benchmark | o3-pro | Qwen3.5 122B-A10B |
|---|---|---|
| Vectara Hallucination Rate | 23.3% | 11.2% |
| Confabulations | 14.2% | — |
| LMArena Expert | — | 1431 |
Multimodal Not comparable
o3-pro: —, Qwen3.5 122B-A10B: 39.0 (#52)
| Benchmark | o3-pro | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Vision | — | 1245 |
Multilingual Not comparable
o3-pro: —, Qwen3.5 122B-A10B: 51.8 (#101)
| Benchmark | o3-pro | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Non-English | — | 1400 |
| LMArena Chinese | — | 1462 |
| LMArena French | — | 1442 |
| LMArena German | — | 1425 |
| LMArena Japanese | — | 1364 |
| LMArena Korean | — | 1353 |
| LMArena Russian | — | 1400 |
| LMArena Spanish | — | 1423 |
Instruction Following Not comparable
o3-pro: —, Qwen3.5 122B-A10B: 74.0 (#109)
| Benchmark | o3-pro | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Instruction Following | — | 1399 |
Long Context o3-pro leads
o3-pro: 72.1 (#1), Qwen3.5 122B-A10B: 43.0 (#105)
| Benchmark | o3-pro | Qwen3.5 122B-A10B |
|---|---|---|
| Fiction.LiveBench | 97.2% | — |
| LMArena Longer Query | — | 1410 |
Writing & Preference Qwen3.5 122B-A10B leads
o3-pro: 57.1 (#128), Qwen3.5 122B-A10B: 60.2 (#98)
| Benchmark | o3-pro | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Text | — | 1417 |
| LMArena Creative Writing | — | 1369 |
| Short-Story Creative Writing | 84.4% | — |
| LMArena Multi-Turn | — | 1416 |
Frequently asked questions
Is o3-pro better than Qwen3.5 122B-A10B?
o3-pro is the stronger model overall, scoring 42.7 to 41.6 on the Noometry Index. Qwen3.5 122B-A10B costs 32× less per token, which makes it the better buy when o3-pro's lead doesn't matter for your workload.
Which is cheaper, o3-pro or Qwen3.5 122B-A10B?
Qwen3.5 122B-A10B is cheaper. It lists at $0.40 per million input tokens and $3.20 per million output tokens; o3-pro lists at $20 and $80.
Is o3-pro or Qwen3.5 122B-A10B better for coding?
o3-pro scores higher on coding benchmarks: 55.1 versus 38.8 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5 122B-A10B does, with 262K tokens against 200K.
How many benchmarks do o3-pro and Qwen3.5 122B-A10B share?
3 benchmarks have published results for both models. o3-pro has 12 scored results on Noometry and Qwen3.5 122B-A10B has 27.