Model comparison
o1-pro vs Qwen2.5 7B Instruct
o1-pro is the stronger model overall, scoring 31.5 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 857× less per token, which makes it the better buy when o1-pro's lead doesn't matter for your workload.
Last verified . 0 shared benchmarks.
Alibaba (Qwen)Summary
- The widest gap is in knowledge, where o1-pro leads 29.7 to 17.0.
- Qwen2.5 7B Instruct is cheaper at $0.17 / $0.70 per million input/output tokens, against $150 / $600 for o1-pro.
- o1-pro accepts more context: 200K tokens versus 131K.
- Qwen2.5 7B Instruct has downloadable open weights; the other is API-only.
Side by side
| o1-pro | Qwen2.5 7B Instruct | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 31.5 | 29.0 |
| Released | 2025-03-19 | 2024-09 |
| Weights | Proprietary | Open |
| Context window | 200K | 131K |
| Max output | 100K | 8K |
| Input $ / M tokens | $150 | $0.17 |
| Output $ / M tokens | $600 | $0.70 |
| Results tracked | 3 | 15 |
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Category by category
Coding Not comparable
o1-pro: —, Qwen2.5 7B Instruct: 36.5 (#208)
| Benchmark | o1-pro | Qwen2.5 7B Instruct |
|---|---|---|
| BigCodeBench Instruct | — | 37.6% |
| BigCodeBench Complete | — | 46.1% |
Agentic & Tool Use Not comparable
o1-pro: —, Qwen2.5 7B Instruct: 23.8 (#124)
| Benchmark | o1-pro | Qwen2.5 7B Instruct |
|---|---|---|
| BALROG | — | 7.8% |
Reasoning o1-pro leads
o1-pro: 20.4 (#239), Qwen2.5 7B Instruct: 14.8 (#322)
| Benchmark | o1-pro | Qwen2.5 7B Instruct |
|---|---|---|
| ARC-AGI-1 | 23.3% | — |
| Chess Puzzles | — | 0% |
| EnigmaEval | 6.1% | — |
| DTBench | — | 47.7% |
| LMCA | — | 6.4% |
| Epoch Capabilities Index | — | 118.51 |
Math Not comparable
o1-pro: —, Qwen2.5 7B Instruct: 12.6 (#306)
| Benchmark | o1-pro | Qwen2.5 7B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 2.5% |
| Omni-MATH | — | 29.4% |
Knowledge o1-pro leads
o1-pro: 29.7 (#234), Qwen2.5 7B Instruct: 17.0 (#286)
| Benchmark | o1-pro | Qwen2.5 7B Instruct |
|---|---|---|
| GPQA Diamond | — | 35.5% |
| Humanity's Last Exam | 8.1% | — |
| MMLU-Pro | — | 53.9% |
| GPQA (HELM) | — | 34.1% |
| MMLU | — | 72.9% |
Instruction Following Not comparable
o1-pro: —, Qwen2.5 7B Instruct: 63.2 (#231)
| Benchmark | o1-pro | Qwen2.5 7B Instruct |
|---|---|---|
| IFEval | — | 74.1% |
Writing & Preference Not comparable
o1-pro: —, Qwen2.5 7B Instruct: 48.8 (#195)
| Benchmark | o1-pro | Qwen2.5 7B Instruct |
|---|---|---|
| WildBench | — | 73.1% |
Frequently asked questions
Is o1-pro better than Qwen2.5 7B Instruct?
o1-pro is the stronger model overall, scoring 31.5 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 857× less per token, which makes it the better buy when o1-pro's lead doesn't matter for your workload.
Which is cheaper, o1-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; o1-pro lists at $150 and $600.
Which has the bigger context window?
o1-pro does, with 200K tokens against 131K.
How many benchmarks do o1-pro and Qwen2.5 7B Instruct share?
0 benchmarks have published results for both models. o1-pro has 3 scored results on Noometry and Qwen2.5 7B Instruct has 15.