Qwen3.5-27B vs Extract
Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $1.56
- Coding
- No data
- Overall
- No data
- Output $/M
- No data
Qwen3.5-27B or Extract: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
No benchmark has tested both yet. Qwen3.5-27B has results on LMArena WebDev, Terminal-Bench Hard (Artificial Analysis run) and WeirdML; Extract has none yet. 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 Qwen3.5-27B has a AA Intelligence Index result, 22.9.
Directional only
Long documents
Listed context window
Extract
Extract takes 1M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Extract
Qwen3.5-27B $0.0010, Extract $0.0000 per turn.
List prices
At a glance
- Coding index
- Qwen3.5-27B not benchmarked, Extract 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
- Qwen3.5-27B $1.56 against Extract Free per million output tokens.
- Context window
- Qwen3.5-27B takes 262K tokens, Extract 1M.
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.
CodingDirectional onlySix of these boards feed the Vibe Coding Index8 boards measured
| Benchmark | Qwen3.5-27B | Extract | Leader |
|---|---|---|---|
| LMArena WebDev | 1358 | Not measured | Only Qwen3.5-27B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 32.6% | Not measured | Only Qwen3.5-27B measured |
| WeirdML | 39.5% | Not measured | Only Qwen3.5-27B measured |
| ALE-Bench | 349.4 | Not measured | Only Qwen3.5-27B measured |
| LMArena Text: coding | 1427 | Not measured | Only Qwen3.5-27B measured |
| LMArena Text: software and IT | 1431 | Not measured | Only Qwen3.5-27B measured |
| LMArena WebDev: React | 1346 | Not measured | Only Qwen3.5-27B measured |
| LMArena WebDev: HTML | 1386 | Not measured | Only Qwen3.5-27B measured |
| Neither model has a result on the other 31 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Qwen3.5-27B | Extract | Leader |
|---|---|---|---|
| AA Intelligence Index | 22.9 | Not measured | Only Qwen3.5-27B measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Qwen3.5-27B | Extract | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 93.9% | Not measured | Only Qwen3.5-27B measured |
| Vending-Bench 2 | $202 | Not measured | Only Qwen3.5-27B measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | Qwen3.5-27B | Extract | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 85.8% | Not measured | Only Qwen3.5-27B measured |
| Humanity's Last Exam (Artificial Analysis run) | 23.9% | Not measured | Only Qwen3.5-27B measured |
| LMArena Text: hard prompts | 1415 | Not measured | Only Qwen3.5-27B measured |
| LMArena Text: expert | 1427 | Not measured | Only Qwen3.5-27B measured |
| LMArena Text: hard prompts in English | 1429 | Not measured | Only Qwen3.5-27B measured |
| LMArena Vision: diagrams | 1249 | Not measured | Only Qwen3.5-27B measured |
| LMArena Vision: homework | 1256 | Not measured | Only Qwen3.5-27B measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Qwen3.5-27B | Extract | Leader |
|---|---|---|---|
| LMArena Text: math | 1429 | Not measured | Only Qwen3.5-27B measured |
| LMArena Text: mathematical work | 1429 | Not measured | Only Qwen3.5-27B measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | Qwen3.5-27B | Extract | Leader |
|---|---|---|---|
| LMArena Text: science | 1437 | Not measured | Only Qwen3.5-27B measured |
| LMArena Text: medicine and healthcare | 1425 | Not measured | Only Qwen3.5-27B measured |
| LMArena Text: legal and government | 1405 | Not measured | Only Qwen3.5-27B measured |
| LMArena Text: business and finance | 1399 | Not measured | Only Qwen3.5-27B measured |
| Neither model has a result on the other 14 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index29 boards measured
| Benchmark | Qwen3.5-27B | Extract | Leader |
|---|---|---|---|
| IFBench | 75.6% | Not measured | Only Qwen3.5-27B measured |
| Long context reasoning (AA-LCR) | 77.7% | Not measured | Only Qwen3.5-27B measured |
| DTBench | 82.4% | Not measured | Only Qwen3.5-27B measured |
| LMCA | 34.0% | Not measured | Only Qwen3.5-27B measured |
| LMArena Text | 1409 | Not measured | Only Qwen3.5-27B measured |
| LMArena Text: instruction following | 1393 | Not measured | Only Qwen3.5-27B measured |
| LMArena Text: creative writing | 1362 | Not measured | Only Qwen3.5-27B measured |
| LMArena Vision | 1241 | Not measured | Only Qwen3.5-27B measured |
| LMArena Text: English | 1425 | Not measured | Only Qwen3.5-27B measured |
| LMArena Text: non-English | 1390 | Not measured | Only Qwen3.5-27B measured |
| LMArena Text: Chinese | 1478 | Not measured | Only Qwen3.5-27B measured |
| LMArena Text: German | 1392 | Not measured | Only Qwen3.5-27B measured |
| LMArena Text: Japanese | 1346 | Not measured | Only Qwen3.5-27B measured |
| LMArena Text: Korean | 1359 | Not measured | Only Qwen3.5-27B measured |
| LMArena Text: French | 1410 | Not measured | Only Qwen3.5-27B measured |
| LMArena Text: Spanish | 1407 | Not measured | Only Qwen3.5-27B measured |
| LMArena Text: Russian | 1390 | Not measured | Only Qwen3.5-27B measured |
| LMArena Text: Polish | 1375 | Not measured | Only Qwen3.5-27B measured |
| LMArena Text: multi-turn | 1410 | Not measured | Only Qwen3.5-27B measured |
| LMArena Text: longer queries | 1413 | Not measured | Only Qwen3.5-27B measured |
| LMArena Text: writing and language | 1380 | Not measured | Only Qwen3.5-27B measured |
| LMArena Text: entertainment and media | 1363 | Not measured | Only Qwen3.5-27B measured |
| LMArena Vision: English | 1246 | Not measured | Only Qwen3.5-27B measured |
| LMArena Vision: Chinese | 1301 | Not measured | Only Qwen3.5-27B measured |
| LMArena Vision: reading text | 1246 | Not measured | Only Qwen3.5-27B measured |
| LMArena Vision: humor | 1227 | Not measured | Only Qwen3.5-27B measured |
| LMArena Vision: captioning | 1170 | Not measured | Only Qwen3.5-27B measured |
| LMArena Vision: recognizing things | 1210 | Not measured | Only Qwen3.5-27B measured |
| LMArena Vision: creative writing | 1224 | Not measured | Only Qwen3.5-27B measured |
| Neither model has a result on the other 17 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 | Qwen3.5-27B | ScaleDown Extract |
|---|---|---|
| Maker | Qwen | Scaledown |
| Weights | Open weights | No public weights listed |
| Released | February 25, 2026 | October 13, 2026 |
| Context window | 262K | 1M |
| Input $/Mtok | $0.195 | $0.04 |
| Output $/Mtok | $1.56 | Free |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Qwen3.5-27B 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, Qwen3.5-27B or Extract?
Qwen3.5-27B costs $0.195 per million input tokens and $1.56 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, Qwen3.5-27B or Extract?
Extract, with 1M tokens against 262K.
Has any benchmark tested both Qwen3.5-27B and Extract?
No benchmark has tested both yet. Qwen3.5-27B has results on LMArena WebDev, Terminal-Bench Hard (Artificial Analysis run) and WeirdML; Extract has none yet. Compare them on price and context instead. Data as of October 22, 2026.