Qwen3.5-27B vs Qwen3 Coder Next
Updated October 21, 2026. 8 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.
Compare them on price, context and the boards below.
- Shared results
- 8
- Qwen3.5-27B only
- 45
- Qwen3 Coder Next only
- 3
- Like-for-like categories
- 5 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
Qwen3.5-27B
Qwen3.5-27B leads, 22.9 against 9.2.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3 Coder Next has a AA Agentic Index result, 0.9.
Directional only
Long documents
Listed context window
No call
Both take 262K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 Coder Next
Qwen3.5-27B $0.0010, Qwen3 Coder Next $0.0005 per turn.
List prices
At a glance
- Coding index
- Qwen3.5-27B not benchmarked, Qwen3 Coder Next not benchmarked.
- Widest category
- General, 35.5 points apart on average over 2 shared benchmarks.
- Widest single gap
- IFBench: Qwen3.5-27B 75.6% against Qwen3 Coder Next 35.2%, 40.3 points apart on a 0 to 100 scale.
- Output price
- Qwen3.5-27B $1.56 against Qwen3 Coder Next $0.80 per million output tokens, 1.9x apart.
- Context window
- Both take 262K tokens.
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 | Qwen3.5-27B | Bars | Qwen3 Coder Next | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 8 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | Qwen3.5-27B | Qwen3 Coder Next | Leader |
|---|---|---|---|
| LMArena WebDev | 1358 | Not measured | Only Qwen3.5-27B measured |
| AA Coding Index | Not measured | 36.2 | Only Qwen3 Coder Next measured |
| SciCode | Not measured | 36.2% | Only Qwen3 Coder Next measured |
| Terminal-Bench Hard (Artificial Analysis run) | 32.6% | 18.2% | Qwen3.5-27B |
| WeirdML | 39.5% | 34.4% | Qwen3.5-27B |
| 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 28 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Qwen3.5-27B | Qwen3 Coder Next | Leader |
|---|---|---|---|
| AA Intelligence Index | 22.9 | 9.2 | Qwen3.5-27B |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Qwen3.5-27B | Qwen3 Coder Next | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 0.9 | Only Qwen3 Coder Next measured |
| τ²-Bench Telecom | 93.9% | 79.5% | Qwen3.5-27B |
| Vending-Bench 2 | $202 | Not measured | Only Qwen3.5-27B measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Qwen3.5-27B | Qwen3 Coder Next | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 85.8% | 73.7% | Qwen3.5-27B |
| Humanity's Last Exam (Artificial Analysis run) | 23.9% | 10.1% | Qwen3.5-27B |
| 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 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Qwen3.5-27B | Qwen3 Coder Next | 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, none shared
| Benchmark | Qwen3.5-27B | Qwen3 Coder Next | 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 13 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3.5-27B | Qwen3 Coder Next | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Qwen3.5-27B | Qwen3 Coder Next | Leader |
|---|---|---|---|
| IFBench | 75.6% | 35.2% | Qwen3.5-27B |
| Long context reasoning (AA-LCR) | 77.7% | 47.0% | Qwen3.5-27B |
| 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 | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3.5-27B | Qwen3 Coder Next | 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
Qwen3 Coder Next costs less
Repository review
50K input + 3K output tokens
Qwen3 Coder Next costs less
Agent loop
200K input + 10K output tokens
Qwen3 Coder Next 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 | Qwen3 Coder Next |
|---|---|---|
| Maker | Qwen | Qwen |
| Weights | Open weights | Open weights |
| Released | February 25, 2026 | February 4, 2026 |
| Context window | 262K | 262K |
| Input $/Mtok | $0.195 | $0.12 |
| Output $/Mtok | $1.56 | $0.80 |
| 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 Qwen3 Coder Next 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 Qwen3 Coder Next?
Qwen3.5-27B costs $0.195 per million input tokens and $1.56 per million output tokens; Qwen3 Coder Next costs $0.12 and $0.80. Cost per solved task is known for neither of them.
Which has the bigger context window, Qwen3.5-27B or Qwen3 Coder Next?
Neither: both take 262K tokens.
How many benchmarks is the Qwen3.5-27B vs Qwen3 Coder Next comparison based on?
8 benchmarks have a result for both models as of October 21, 2026. Qwen3.5-27B's coding index rests on 1 independent benchmark and Qwen3 Coder Next's on 0. Where only one model has a result, the page says so instead of naming a winner.