Qwen2.5 Coder 32B Instruct vs Extract
Updated October 21, 2026. 0 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
- Coding
- No data
- Overall
- 6.7
- Output $/M
- $1.00
- Coding
- No data
- Overall
- No data
- Output $/M
- No data
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 0
- Qwen2.5 Coder 32B Instruct only
- 35
- Extract only
- 0
- Like-for-like categories
- 0 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
No call
Only Qwen2.5 Coder 32B Instruct has a AA Intelligence Index result, 6.7.
Directional only
Long documents
Listed context window
Extract
Extract takes 1M tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Extract
Qwen2.5 Coder 32B Instruct $0.0012, Extract $0.0000 per turn.
List prices
At a glance
- Coding index
- Qwen2.5 Coder 32B Instruct not benchmarked, Extract not benchmarked.
- Widest category
- No category has a result for both models.
- Widest single gap
- No benchmark has a result for both models.
- Output price
- Qwen2.5 Coder 32B Instruct $1.00 against Extract Free per million output tokens.
- Context window
- Qwen2.5 Coder 32B Instruct takes 33K tokens, Extract 1M.
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.
CodingDirectional onlySix of these boards feed the Vibe Coding Index5 boards, none shared
| Benchmark | Qwen2.5 Coder 32B Instruct | Extract | Leader |
|---|---|---|---|
| Aider polyglot | 16.4% | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| SWE-bench Verified | 47.0% | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LiveCodeBench (Artificial Analysis run) | 29.5% | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: coding | 1276 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: software and IT | 1261 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Qwen2.5 Coder 32B Instruct | Extract | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.7 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| Epoch Capabilities Index | 119.5 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
Agentic workNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen2.5 Coder 32B Instruct | Extract | Leader |
|---|---|---|---|
| Neither model has a result on 39 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Qwen2.5 Coder 32B Instruct | Extract | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 41.7% | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| Humanity's Last Exam (Artificial Analysis run) | 3.5% | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: hard prompts | 1251 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: expert | 1221 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: hard prompts in English | 1269 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Qwen2.5 Coder 32B Instruct | Extract | Leader |
|---|---|---|---|
| GSM8K | 93.0% | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: math | 1251 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: mathematical work | 1243 | Not measured | 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 | Qwen2.5 Coder 32B Instruct | Extract | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 63.5% | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| MMLU | 79.1% | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| AI2 Reasoning Challenge | 70.5% | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: science | 1221 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: medicine and healthcare | 1187 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: legal and government | 1257 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: business and finance | 1224 | Not measured | 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 | Qwen2.5 Coder 32B Instruct | Extract | 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 | Qwen2.5 Coder 32B Instruct | Extract | Leader |
|---|---|---|---|
| HellaSwag | 83.0% | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| WinoGrande | 80.8% | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text | 1230 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: instruction following | 1223 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: creative writing | 1174 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: English | 1248 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: non-English | 1205 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: Chinese | 1222 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: Russian | 1228 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: multi-turn | 1222 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: longer queries | 1251 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: writing and language | 1191 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: entertainment and media | 1150 | Not measured | 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 | Qwen2.5 Coder 32B Instruct | Extract | 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
Extract costs less
Repository review
50K input + 3K output tokens
Extract costs less
Agent loop
200K input + 10K output tokens
Extract 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 | Qwen2.5 Coder 32B Instruct | ScaleDown Extract |
|---|---|---|
| Maker | Qwen | Scaledown |
| Weights | Open weights | No public weights listed |
| Released | November 11, 2024 | October 13, 2026 |
| Context window | 33K | 1M |
| Input $/Mtok | $0.66 | $0.04 |
| Output $/Mtok | $1.00 | Free |
| 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 Qwen2.5 Coder 32B Instruct better than Extract for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, Qwen2.5 Coder 32B Instruct or Extract?
Qwen2.5 Coder 32B Instruct costs $0.66 per million input tokens and $1.00 per million output tokens; Extract costs $0.04 and Free. Cost per solved task is known for neither of them.
Which has the bigger context window, Qwen2.5 Coder 32B Instruct or Extract?
Extract, with 1M tokens against 33K.
How many benchmarks is the Qwen2.5 Coder 32B Instruct vs Extract comparison based on?
0 benchmarks have a result for both models as of October 21, 2026. Qwen2.5 Coder 32B Instruct's coding index rests on 0 independent benchmarks and Extract's on 0. Where only one model has a result, the page says so instead of naming a winner.