Qwen3.5-27B vs Qwen3.5 397B A17B
Qwen3.5-27B leads Qwen3.5 397B A17B on the Artificial Analysis Intelligence Index, 22.9 to 21.4, and Qwen3.5-27B costs 1.5x less per output token ($1.56 vs $2.34 per million). Updated October 22, 2026; 52 benchmarks have a result for both.
Qwen3.5-27B or Qwen3.5 397B A17B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Qwen3.5-27B for output tokens 1.5x cheaper ($1.56 vs $2.34 per million) and work measured by LMArena WebDev or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 52
- Qwen3.5-27B only
- 2
- Qwen3.5 397B A17B only
- 14
- Like-for-like categories
- 7 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
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 21.4.
Same board, both measured
Web design
Design Arena: websites
No call
Only Qwen3.5 397B A17B has a Design Arena: websites result, 1190.
Directional only
Long documents
Listed context window
No call
Both take 262K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-27B
Qwen3.5-27B $0.0010, Qwen3.5 397B A17B $0.0016 per turn.
List prices
At a glance
- Coding index
- Qwen3.5-27B not benchmarked, Qwen3.5 397B A17B not benchmarked.
- Widest category
- Coding, 6.6 points apart on average over 7 shared benchmarks.
- Widest single gap
- LMArena Text: Japanese: Qwen3.5-27B 1346 against Qwen3.5 397B A17B 1429, 13.9 points apart on a 0 to 100 scale.
- Output price
- Qwen3.5-27B $1.56 against Qwen3.5 397B A17B $2.34 per million output tokens, 1.5x 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 | Qwen3.5 397B A17B | Ties |
|---|---|---|---|---|
| Coding | 0 | 7 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 7 | ||
| Math | 0 | 3 | ||
| Knowledge | 0 | 4 | ||
| General | 1 | 28 | ||
| All shared boards | 2 | 50 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index07
| Benchmark | Qwen3.5-27B | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| LMArena WebDev | 1358 | 1400 | Qwen3.5 397B A17B |
| SciCode | Not measured | 44.8% | Only Qwen3.5 397B A17B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 32.6% | 40.9% | Qwen3.5 397B A17B |
| WeirdML | 39.5% | Not measured | Only Qwen3.5-27B measured |
| ALE-Bench | 349.4 | 621.9 | Qwen3.5 397B A17B |
| LMArena Text: coding | 1427 | 1464 | Qwen3.5 397B A17B |
| LMArena Text: software and IT | 1431 | 1463 | Qwen3.5 397B A17B |
| LMArena WebDev: React | 1346 | 1388 | Qwen3.5 397B A17B |
| LMArena WebDev: HTML | 1386 | 1410 | Qwen3.5 397B A17B |
| Neither model has a result on the other 34 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Qwen3.5-27B | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| AA Intelligence Index | 22.9 | 21.4 | Qwen3.5-27B |
| Epoch Capabilities Index | Not measured | 146.8 | Only Qwen3.5 397B A17B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Qwen3.5-27B | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 93.9% | 95.6% | Qwen3.5 397B A17B |
| APEX-Agents | Not measured | 24.9% | Only Qwen3.5 397B A17B measured |
| Vending-Bench 2 | $202 | Not measured | Only Qwen3.5-27B measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | Qwen3.5-27B | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 85.8% | 89.3% | Qwen3.5 397B A17B |
| Humanity's Last Exam (Artificial Analysis run) | 23.9% | 29.0% | Qwen3.5 397B A17B |
| GPQA Diamond (Epoch AI run) | Not measured | 86.4% | Only Qwen3.5 397B A17B measured |
| Mystery game puzzles | Not measured | 18.0% | Only Qwen3.5 397B A17B measured |
| LMArena Text: hard prompts | 1415 | 1448 | Qwen3.5 397B A17B |
| LMArena Text: expert | 1427 | 1462 | Qwen3.5 397B A17B |
| LMArena Text: hard prompts in English | 1429 | 1450 | Qwen3.5 397B A17B |
| LMArena Vision: diagrams | 1249 | 1277 | Qwen3.5 397B A17B |
| LMArena Vision: homework | 1256 | 1292 | Qwen3.5 397B A17B |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Qwen3.5-27B | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 88.9% | Only Qwen3.5 397B A17B measured |
| FrontierMath, tiers 1 to 3 | Not measured | 31.2% | Only Qwen3.5 397B A17B measured |
| LMArena Text: math | 1429 | 1453 | Qwen3.5 397B A17B |
| LMArena Text: mathematical work | 1429 | 1454 | Qwen3.5 397B A17B |
| MathArena | 15.8% | 19.2% | Qwen3.5 397B A17B |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Qwen3.5-27B | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| LMArena Text: science | 1437 | 1457 | Qwen3.5 397B A17B |
| LMArena Text: medicine and healthcare | 1425 | 1455 | Qwen3.5 397B A17B |
| LMArena Text: legal and government | 1405 | 1441 | Qwen3.5 397B A17B |
| LMArena Text: business and finance | 1399 | 1435 | Qwen3.5 397B A17B |
| Neither model has a result on the other 16 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Qwen3.5-27B | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1190 | Only Qwen3.5 397B A17B measured |
| Design Arena: UI components | Not measured | 1161 | Only Qwen3.5 397B A17B measured |
| Design Arena: game dev | Not measured | 1140 | Only Qwen3.5 397B A17B measured |
| Design Arena: data viz | Not measured | 1175 | Only Qwen3.5 397B A17B measured |
| Design Arena: 3D | Not measured | 1160 | Only Qwen3.5 397B A17B measured |
| Design Arena: all code categories | Not measured | 1179 | Only Qwen3.5 397B A17B measured |
| Design Arena: SVG | Not measured | 1139 | Only Qwen3.5 397B A17B measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index128
| Benchmark | Qwen3.5-27B | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| IFBench | 75.6% | 78.8% | Qwen3.5 397B A17B |
| Long context reasoning (AA-LCR) | 77.7% | 77.3% | Qwen3.5-27B |
| DTBench | 82.4% | 87.5% | Qwen3.5 397B A17B |
| LMCA | 34.0% | 37.9% | Qwen3.5 397B A17B |
| LMArena Text | 1409 | 1437 | Qwen3.5 397B A17B |
| LMArena Text: instruction following | 1393 | 1424 | Qwen3.5 397B A17B |
| LMArena Text: creative writing | 1362 | 1402 | Qwen3.5 397B A17B |
| LMArena Vision | 1241 | 1262 | Qwen3.5 397B A17B |
| LMArena Text: English | 1425 | 1443 | Qwen3.5 397B A17B |
| LMArena Text: non-English | 1390 | 1430 | Qwen3.5 397B A17B |
| LMArena Text: Chinese | 1478 | 1498 | Qwen3.5 397B A17B |
| LMArena Text: German | 1392 | 1445 | Qwen3.5 397B A17B |
| LMArena Text: Japanese | 1346 | 1429 | Qwen3.5 397B A17B |
| LMArena Text: Korean | 1359 | 1384 | Qwen3.5 397B A17B |
| LMArena Text: French | 1410 | 1462 | Qwen3.5 397B A17B |
| LMArena Text: Spanish | 1407 | 1441 | Qwen3.5 397B A17B |
| LMArena Text: Russian | 1390 | 1430 | Qwen3.5 397B A17B |
| LMArena Text: Polish | 1375 | 1447 | Qwen3.5 397B A17B |
| LMArena Text: multi-turn | 1410 | 1446 | Qwen3.5 397B A17B |
| LMArena Text: longer queries | 1413 | 1441 | Qwen3.5 397B A17B |
| LMArena Text: writing and language | 1380 | 1415 | Qwen3.5 397B A17B |
| LMArena Text: entertainment and media | 1363 | 1399 | Qwen3.5 397B A17B |
| LMArena Vision: English | 1246 | 1268 | Qwen3.5 397B A17B |
| LMArena Vision: Chinese | 1301 | 1314 | Qwen3.5 397B A17B |
| LMArena Vision: reading text | 1246 | 1271 | Qwen3.5 397B A17B |
| LMArena Vision: humor | 1227 | 1262 | Qwen3.5 397B A17B |
| LMArena Vision: captioning | 1170 | 1230 | Qwen3.5 397B A17B |
| LMArena Vision: recognizing things | 1210 | 1265 | Qwen3.5 397B A17B |
| LMArena Vision: creative writing | 1224 | 1260 | Qwen3.5 397B A17B |
| Neither model has a result on the other 19 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3.5-27B | Qwen3.5 397B A17B | 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
Qwen3.5-27B costs less
Repository review
50K input + 3K output tokens
Qwen3.5-27B costs less
Agent loop
200K input + 10K output tokens
Qwen3.5-27B 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 | Qwen3.5 397B A17B |
|---|---|---|
| Maker | Qwen | Qwen |
| Weights | Open weights | Open weights |
| Released | February 25, 2026 | February 16, 2026 |
| Context window | 262K | 262K |
| Input $/Mtok | $0.195 | $0.39 |
| Output $/Mtok | $1.56 | $2.34 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Qwen3.5-27B better than Qwen3.5 397B A17B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Qwen3.5-27B for output tokens 1.5x cheaper ($1.56 vs $2.34 per million) and work measured by LMArena WebDev or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, Qwen3.5-27B or Qwen3.5 397B A17B?
Qwen3.5-27B costs $0.195 per million input tokens and $1.56 per million output tokens; Qwen3.5 397B A17B costs $0.39 and $2.34. Cost per solved task is known for neither of them.
Which has the bigger context window, Qwen3.5-27B or Qwen3.5 397B A17B?
Neither: both take 262K tokens.
How many benchmarks is the Qwen3.5-27B vs Qwen3.5 397B A17B comparison based on?
52 benchmarks have a result for both models as of October 22, 2026. Qwen3.5-27B's coding index rests on 1 independent benchmark and Qwen3.5 397B A17B's on 1. Where only one model has a result, the page says so instead of naming a winner.