o1-pro vs Qwen2.5 Coder 32B Instruct
Updated October 21, 2026. 1 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
o1-pro has the edge where the context window is larger, 200K against 33K tokens.
- Shared results
- 1
- o1-pro only
- 1
- Qwen2.5 Coder 32B Instruct only
- 34
- Like-for-like categories
- 1 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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
o1-pro
o1-pro leads, 12.4 against 6.7.
Same board, both measured
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 Coder 32B Instruct
o1-pro $0.450, Qwen2.5 Coder 32B Instruct $0.0012 per turn.
List prices
At a glance
- Coding index
- o1-pro not benchmarked, Qwen2.5 Coder 32B Instruct not benchmarked.
- Widest category
- Intelligence, 5.7 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Intelligence Index: o1-pro 12.4 against Qwen2.5 Coder 32B Instruct 6.7, 5.7 points apart on a 0 to 100 scale.
- Output price
- o1-pro $600.00 against Qwen2.5 Coder 32B Instruct $1.00 per million output tokens, 600.0x apart.
- Context window
- o1-pro takes 200K tokens, Qwen2.5 Coder 32B Instruct 33K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | o1-pro | Bars | Qwen2.5 Coder 32B Instruct | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| All shared boards | 1 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index5 boards, none shared
| Benchmark | o1-pro | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 16.4% | Only Qwen2.5 Coder 32B Instruct measured |
| SWE-bench Verified | Not measured | 47.0% | Only Qwen2.5 Coder 32B Instruct measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 29.5% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: coding | Not measured | 1276 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: software and IT | Not measured | 1261 | Only Qwen2.5 Coder 32B Instruct measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | o1-pro | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.4 | 6.7 | o1-pro |
| Epoch Capabilities Index | Not measured | 119.5 | Only Qwen2.5 Coder 32B Instruct measured |
Agentic workNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | o1-pro | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 39 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | o1-pro | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 41.7% | Only Qwen2.5 Coder 32B Instruct measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 3.5% | Only Qwen2.5 Coder 32B Instruct measured |
| ARC-AGI | 23.3% | Not measured | Only o1-pro measured |
| LMArena Text: hard prompts | Not measured | 1251 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: expert | Not measured | 1221 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: hard prompts in English | Not measured | 1269 | Only Qwen2.5 Coder 32B Instruct measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | o1-pro | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| GSM8K | Not measured | 93.0% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: math | Not measured | 1251 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: mathematical work | Not measured | 1243 | Only Qwen2.5 Coder 32B Instruct measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | o1-pro | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 63.5% | Only Qwen2.5 Coder 32B Instruct measured |
| MMLU | Not measured | 79.1% | Only Qwen2.5 Coder 32B Instruct measured |
| AI2 Reasoning Challenge | Not measured | 70.5% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: science | Not measured | 1221 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: medicine and healthcare | Not measured | 1187 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: legal and government | Not measured | 1257 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: business and finance | Not measured | 1224 | Only Qwen2.5 Coder 32B Instruct measured |
| Neither model has a result on the other 10 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | o1-pro | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index13 boards, none shared
| Benchmark | o1-pro | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| HellaSwag | Not measured | 83.0% | Only Qwen2.5 Coder 32B Instruct measured |
| WinoGrande | Not measured | 80.8% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text | Not measured | 1230 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: instruction following | Not measured | 1223 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: creative writing | Not measured | 1174 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: English | Not measured | 1248 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: non-English | Not measured | 1205 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: Chinese | Not measured | 1222 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: Russian | Not measured | 1228 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: multi-turn | Not measured | 1222 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: longer queries | Not measured | 1251 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: writing and language | Not measured | 1191 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: entertainment and media | Not measured | 1150 | Only Qwen2.5 Coder 32B Instruct measured |
| Neither model has a result on the other 33 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | o1-pro | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
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 Coder 32B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen2.5 Coder 32B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen2.5 Coder 32B 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 Coder 32B Instruct |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | March 19, 2025 | November 11, 2024 |
| Context window | 200K | 33K |
| Input $/Mtok | $150.00 | $0.66 |
| Output $/Mtok | $600.00 | $1.00 |
| 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 Coder 32B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. o1-pro has the edge where the context window is larger, 200K against 33K tokens.
Which is cheaper, o1-pro or Qwen2.5 Coder 32B Instruct?
o1-pro costs $150.00 per million input tokens and $600.00 per million output tokens; Qwen2.5 Coder 32B Instruct costs $0.66 and $1.00. Cost per solved task is known for neither of them.
Which has the bigger context window, o1-pro or Qwen2.5 Coder 32B Instruct?
o1-pro, with 200K tokens against 33K.
How many benchmarks is the o1-pro vs Qwen2.5 Coder 32B Instruct comparison based on?
1 benchmarks have a result for both models as of October 21, 2026. o1-pro's coding index rests on 0 independent benchmarks and Qwen2.5 Coder 32B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.