Model comparison
o3-pro vs Qwen2.5 7B Instruct
o3-pro is the stronger model overall, scoring 42.9 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 114× 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 4 categories and Qwen2.5 7B Instruct in 0 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in coding, where o3-pro leads 55.5 to 36.5.
- The biggest single-benchmark swing is DTBench: 86.9% for o3-pro and 47.7% for Qwen2.5 7B Instruct.
- Qwen2.5 7B Instruct is cheaper at $0.17 / $0.70 per million input/output tokens, against $20 / $80 for o3-pro.
- o3-pro accepts more context: 200K tokens versus 131K.
- Qwen2.5 7B Instruct has downloadable open weights; the other is API-only.
Side by side
| o3-pro | Qwen2.5 7B Instruct | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 42.9 | 29.0 |
| Released | 2025-06-10 | 2024-09 |
| Weights | Proprietary | Open |
| Context window | 200K | 131K |
| Max output | 100K | 8K |
| Input $ / M tokens | $20 | $0.17 |
| Output $ / M tokens | $80 | $0.70 |
| Results tracked | 12 | 15 |
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Category by category
Coding o3-pro leads
o3-pro: 55.5 (#24), Qwen2.5 7B Instruct: 36.5 (#208)
| Benchmark | o3-pro | Qwen2.5 7B Instruct |
|---|---|---|
| Aider Polyglot | 84.9% | — |
| WeirdML | 58.2% | — |
| BigCodeBench Instruct | — | 37.6% |
| BigCodeBench Complete | — | 46.1% |
Agentic & Tool Use Not comparable
o3-pro: —, Qwen2.5 7B Instruct: 23.8 (#124)
| Benchmark | o3-pro | Qwen2.5 7B Instruct |
|---|---|---|
| BALROG | — | 7.8% |
Reasoning o3-pro leads
o3-pro: 23.8 (#171), Qwen2.5 7B Instruct: 14.8 (#322)
| Benchmark | o3-pro | Qwen2.5 7B Instruct |
|---|---|---|
| DTBench | 86.9% | 47.7% |
| LMCA | 38.5% | 6.4% |
| Epoch Capabilities Index | 147.42 | 118.51 |
| ARC-AGI-2 | 4.9% | — |
| Kagi LLM Benchmark | 72.1% | — |
| ARC-AGI-1 | 59.3% | — |
| Chess Puzzles | — | 0% |
Math Not comparable
o3-pro: —, Qwen2.5 7B Instruct: 12.6 (#306)
| Benchmark | o3-pro | Qwen2.5 7B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 2.5% |
| Omni-MATH | — | 29.4% |
Knowledge o3-pro leads
o3-pro: 29.5 (#238), Qwen2.5 7B Instruct: 17.0 (#286)
| Benchmark | o3-pro | Qwen2.5 7B Instruct |
|---|---|---|
| GPQA Diamond | — | 35.5% |
| MMLU-Pro | — | 53.9% |
| Confabulations | 14.2% | — |
| Vectara Hallucination Rate | 23.3% | — |
| GPQA (HELM) | — | 34.1% |
| MMLU | — | 72.9% |
Instruction Following Not comparable
o3-pro: —, Qwen2.5 7B Instruct: 63.2 (#231)
| Benchmark | o3-pro | Qwen2.5 7B Instruct |
|---|---|---|
| IFEval | — | 74.1% |
Long Context Not comparable
o3-pro: 72.2 (#1), Qwen2.5 7B Instruct: —
| Benchmark | o3-pro | Qwen2.5 7B Instruct |
|---|---|---|
| Fiction.LiveBench | 97.2% | — |
Writing & Preference o3-pro leads
o3-pro: 57.1 (#133), Qwen2.5 7B Instruct: 48.8 (#195)
| Benchmark | o3-pro | Qwen2.5 7B Instruct |
|---|---|---|
| Short-Story Creative Writing | 84.4% | — |
| WildBench | — | 73.1% |
Frequently asked questions
Is o3-pro better than Qwen2.5 7B Instruct?
o3-pro is the stronger model overall, scoring 42.9 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 114× 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 Qwen2.5 7B Instruct?
Qwen2.5 7B Instruct is cheaper. It lists at $0.17 per million input tokens and $0.70 per million output tokens; o3-pro lists at $20 and $80.
Is o3-pro or Qwen2.5 7B Instruct better for coding?
o3-pro scores higher on coding benchmarks: 55.5 versus 36.5 in the Noometry coding category.
Which has the bigger context window?
o3-pro does, with 200K tokens against 131K.
How many benchmarks do o3-pro and Qwen2.5 7B Instruct share?
3 benchmarks have published results for both models. o3-pro has 12 scored results on Noometry and Qwen2.5 7B Instruct has 15.