o1-pro vs Qwen2.5 7B Instruct
Qwen2.5 7B Instruct costs 3000.0x less per output token than o1-pro ($0.20 vs $600.00 per million). Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $600.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.20
o1-pro or Qwen2.5 7B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick o1-pro for a larger context window (200K against 33K tokens).
No benchmark has tested both yet. o1-pro has results on AA Intelligence Index and ARC-AGI; Qwen2.5 7B Instruct on Epoch Capabilities Index, BALROG and GPQA Diamond (Epoch AI run). Compare them on price and context instead.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only o1-pro has a AA Intelligence Index result, 12.4.
Directional only
Long documents
Listed context window
o1-pro
o1-pro takes 200K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen2.5 7B Instruct
o1-pro $0.450, Qwen2.5 7B Instruct $0.0002 per turn.
List prices
At a glance
- Coding index
- o1-pro not benchmarked, Qwen2.5 7B Instruct not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- o1-pro $600.00 against Qwen2.5 7B Instruct $0.20 per million output tokens, 3000.0x apart.
- Context window
- o1-pro takes 200K tokens, Qwen2.5 7B Instruct 33K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | o1-pro | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.4 | Not measured | Only o1-pro measured |
| Epoch Capabilities Index | Not measured | 118.5 | Only Qwen2.5 7B Instruct measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | o1-pro | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| BALROG | Not measured | 7.8% | Only Qwen2.5 7B Instruct measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | o1-pro | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Epoch AI run) | Not measured | 35.5% | Only Qwen2.5 7B Instruct measured |
| ARC-AGI | 23.3% | Not measured | Only o1-pro measured |
| Neither model has a result on the other 20 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | o1-pro | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 2.5% | Only Qwen2.5 7B Instruct measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | o1-pro | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| MMLU | Not measured | 72.9% | Only Qwen2.5 7B Instruct measured |
| Neither model has a result on the other 17 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Qwen2.5 7B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen2.5 7B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen2.5 7B Instruct costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | OpenAI o1-pro | Qwen2.5 7B Instruct |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | March 19, 2025 | October 16, 2024 |
| Context window | 200K | 33K |
| Input $/Mtok | $150.00 | $0.10 |
| Output $/Mtok | $600.00 | $0.20 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is o1-pro better than Qwen2.5 7B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick o1-pro for a larger context window (200K against 33K tokens).
Which is cheaper, o1-pro or Qwen2.5 7B Instruct?
o1-pro costs $150.00 per million input tokens and $600.00 per million output tokens; Qwen2.5 7B Instruct costs $0.10 and $0.20. Cost per solved task is known for neither of them.
Which has the bigger context window, o1-pro or Qwen2.5 7B Instruct?
o1-pro, with 200K tokens against 33K.
Has any benchmark tested both o1-pro and Qwen2.5 7B Instruct?
No benchmark has tested both yet. o1-pro has results on AA Intelligence Index and ARC-AGI; Qwen2.5 7B Instruct on Epoch Capabilities Index, BALROG and GPQA Diamond (Epoch AI run). Compare them on price and context instead. Data as of October 21, 2026.