Model comparison
o3-pro vs Qwen3.5-9B
o3-pro is the stronger model overall, scoring 42.9 to 33.8 on the Noometry Index. Qwen3.5-9B costs 311× 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 Qwen3.5-9B in 1 category; 2 gaps are clear of the uncertainty.
- The widest gap is in coding, where o3-pro leads 55.5 to 35.9.
- The biggest single-benchmark swing is DTBench: 86.9% for o3-pro and 71.2% for Qwen3.5-9B.
- Qwen3.5-9B is cheaper at $0.10 / $0.15 per million input/output tokens, against $20 / $80 for o3-pro.
- Qwen3.5-9B accepts more context: 262K tokens versus 200K.
- Qwen3.5-9B has downloadable open weights; the other is API-only.
Side by side
| o3-pro | Qwen3.5-9B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 42.9 | 33.8 |
| Released | 2025-06-10 | 2026-02-23 |
| Weights | Proprietary | Open |
| Context window | 200K | 262K |
| Max output | 100K | 66K |
| Input $ / M tokens | $20 | $0.10 |
| Output $ / M tokens | $80 | $0.15 |
| Results tracked | 12 | 10 |
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Category by category
Coding o3-pro leads
o3-pro: 55.5 (#24), Qwen3.5-9B: 35.9 (#217)
| Benchmark | o3-pro | Qwen3.5-9B |
|---|---|---|
| Aider Polyglot | 84.9% | — |
| SciCode | — | 27.5% |
| WeirdML | 58.2% | — |
Agentic & Tool Use Not comparable
o3-pro: —, Qwen3.5-9B: 14.5 (#151)
| Benchmark | o3-pro | Qwen3.5-9B |
|---|---|---|
| Terminal-Bench | — | 9.2% |
Reasoning Too close to call
o3-pro: 23.8 (#171), Qwen3.5-9B: 23.1 (#182)
| Benchmark | o3-pro | Qwen3.5-9B |
|---|---|---|
| DTBench | 86.9% | 71.2% |
| LMCA | 38.5% | 24.5% |
| Epoch Capabilities Index | 147.42 | 139.46 |
| ARC-AGI-2 | 4.9% | — |
| Kagi LLM Benchmark | 72.1% | — |
| ARC-AGI-1 | 59.3% | — |
| CritPt | — | 0.3% |
| Chess Puzzles | — | 12% |
Math Not comparable
o3-pro: —, Qwen3.5-9B: 34.8 (#192)
| Benchmark | o3-pro | Qwen3.5-9B |
|---|---|---|
| MathArena Final-Answer Competitions | — | 48.5% |
| OTIS Mock AIME 2024-2025 | — | 61.7% |
Knowledge Qwen3.5-9B leads
o3-pro: 29.5 (#238), Qwen3.5-9B: 46.0 (#84)
| Benchmark | o3-pro | Qwen3.5-9B |
|---|---|---|
| GPQA Diamond | — | 79% |
| Confabulations | 14.2% | — |
| Vectara Hallucination Rate | 23.3% | — |
Long Context Not comparable
o3-pro: 72.2 (#1), Qwen3.5-9B: —
| Benchmark | o3-pro | Qwen3.5-9B |
|---|---|---|
| Fiction.LiveBench | 97.2% | — |
Writing & Preference Not comparable
o3-pro: 57.1 (#133), Qwen3.5-9B: —
| Benchmark | o3-pro | Qwen3.5-9B |
|---|---|---|
| Short-Story Creative Writing | 84.4% | — |
Frequently asked questions
Is o3-pro better than Qwen3.5-9B?
o3-pro is the stronger model overall, scoring 42.9 to 33.8 on the Noometry Index. Qwen3.5-9B costs 311× 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 Qwen3.5-9B?
Qwen3.5-9B is cheaper. It lists at $0.10 per million input tokens and $0.15 per million output tokens; o3-pro lists at $20 and $80.
Is o3-pro or Qwen3.5-9B better for coding?
o3-pro scores higher on coding benchmarks: 55.5 versus 35.9 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5-9B does, with 262K tokens against 200K.
How many benchmarks do o3-pro and Qwen3.5-9B share?
3 benchmarks have published results for both models. o3-pro has 12 scored results on Noometry and Qwen3.5-9B has 10.