Muse Wonder vs Qwen3.5-27B
Qwen3.5-27B costs 12.8x less per output token than Muse Wonder ($1.56 vs $20.00 per million). Updated October 22, 2026; 31 benchmarks have a result for both.
Muse Wonder or Qwen3.5-27B: which to pick
Only Muse Wonder has a coding score, 71.9 on the Vibe Coding Index; Qwen3.5-27B has none, so the two cannot be ranked on coding.
Pick Qwen3.5-27B for output tokens 12.8x cheaper ($1.56 vs $20.00 per million).
- Shared results
- 31
- Muse Wonder only
- 29
- Qwen3.5-27B only
- 22
- Like-for-like categories
- 6 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
Only Muse Wonder has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Qwen3.5-27B has a AA Intelligence Index result, 22.9.
Directional only
Web design
Design Arena: websites
No call
Only Muse Wonder has a Design Arena: websites result, 1363.
Directional only
General
Vals Index
No call
Only Muse Wonder has a Vals Index result, 64.9%.
Directional only
Long documents
Listed context window
Muse Wonder
Muse Wonder takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-27B
Muse Wonder $0.014, Qwen3.5-27B $0.0010 per turn.
List prices
At a glance
- Coding index
- Muse Wonder 71.9 from 3 benchmarks, Qwen3.5-27B not benchmarked.
- Widest category
- Agentic work, 57.7 points apart on average over 1 shared benchmark.
- Widest single gap
- Vending-Bench 2: Muse Wonder $9,432 against Qwen3.5-27B $202, 57.7 points apart on a 0 to 100 scale.
- Output price
- Muse Wonder $20.00 against Qwen3.5-27B $1.56 per million output tokens, 12.8x apart.
- Context window
- Muse Wonder takes 1.05M tokens, Qwen3.5-27B 262K.
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 | Muse Wonder | Bars | Qwen3.5-27B | Ties |
|---|---|---|---|---|
| Coding | 5 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 4 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 15 | 0 | ||
| All shared boards | 31 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index50
| Benchmark | Muse Wonder | Qwen3.5-27B | Leader |
|---|---|---|---|
| LMArena WebDev | 1652 | 1358 | Muse Wonder |
| Terminal-Bench 4 (Vals AI) | 49.5% | Not measured | Only Muse Wonder measured |
| Vibe Code Bench (Vals AI) | 83.1% | Not measured | Only Muse Wonder measured |
| Code migration (Vals AI) | 62.5% | Not measured | Only Muse Wonder measured |
| IOI (Vals AI) | 66.9% | Not measured | Only Muse Wonder measured |
| SWE-bench Multimodal (Vals AI) | 45.6% | Not measured | Only Muse Wonder measured |
| Vibe Code Bench 1-100 (Vals AI) | 26.6% | Not measured | Only Muse Wonder measured |
| CyberBench (Vals AI) | 80.4% | Not measured | Only Muse Wonder measured |
| SciCode | 60.3% | Not measured | Only Muse Wonder measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 32.6% | Only Qwen3.5-27B measured |
| WeirdML | Not measured | 39.5% | Only Qwen3.5-27B measured |
| ALE-Bench | Not measured | 349.4 | Only Qwen3.5-27B measured |
| LMArena Text: coding | 1522 | 1427 | Muse Wonder |
| LMArena Text: software and IT | 1515 | 1431 | Muse Wonder |
| LMArena WebDev: React | 1641 | 1346 | Muse Wonder |
| LMArena WebDev: HTML | 1694 | 1386 | Muse Wonder |
| Neither model has a result on the other 23 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Muse Wonder | Qwen3.5-27B | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 22.9 | Only Qwen3.5-27B measured |
| Epoch Capabilities Index | 161.8 | Not measured | Only Muse Wonder measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Muse Wonder | Qwen3.5-27B | Leader |
|---|---|---|---|
| Design Arena: spreadsheets | 1260 | Not measured | Only Muse Wonder measured |
| Finance Agent (Vals AI) | 56.6% | Not measured | Only Muse Wonder measured |
| Tax Agent Bench (Vals AI) | 36.3% | Not measured | Only Muse Wonder measured |
| Legal Research Bench (Vals AI) | 50.5% | Not measured | Only Muse Wonder measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 23.8% | Not measured | Only Muse Wonder measured |
| Excel Modeling Benchmark (Vals AI) | 67.1% | Not measured | Only Muse Wonder measured |
| BioMysteryBench (Vals AI) | 67.0% | Not measured | Only Muse Wonder measured |
| Terminal-Bench-Science (Vals AI) | 22.9% | Not measured | Only Muse Wonder measured |
| Vals Science Index | 39.1% | Not measured | Only Muse Wonder measured |
| CUA-bench (Vals AI) | 7.2% | Not measured | Only Muse Wonder measured |
| τ²-Bench Telecom | Not measured | 93.9% | Only Qwen3.5-27B measured |
| Vending-Bench 2 | $9,432 | $202 | Muse Wonder |
| Neither model has a result on the other 29 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | Muse Wonder | Qwen3.5-27B | Leader |
|---|---|---|---|
| MysteryMechanism (Vals AI) | 27.5% | Not measured | Only Muse Wonder measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 85.8% | Only Qwen3.5-27B measured |
| Humanity's Last Exam (Artificial Analysis run) | 50.1% | 23.9% | Muse Wonder |
| LMArena Text: hard prompts | 1506 | 1415 | Muse Wonder |
| LMArena Text: expert | 1513 | 1427 | Muse Wonder |
| LMArena Text: hard prompts in English | 1506 | 1429 | Muse Wonder |
| LMArena Vision: diagrams | Not measured | 1249 | Only Qwen3.5-27B measured |
| LMArena Vision: homework | Not measured | 1256 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Muse Wonder | Qwen3.5-27B | Leader |
|---|---|---|---|
| ProofBench | 84.0% | Not measured | Only Muse Wonder measured |
| LMArena Text: math | 1486 | 1429 | Muse Wonder |
| LMArena Text: mathematical work | 1514 | 1429 | Muse Wonder |
| Neither model has a result on the other 10 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | Muse Wonder | Qwen3.5-27B | Leader |
|---|---|---|---|
| LMArena Text: science | 1506 | 1437 | Muse Wonder |
| LMArena Text: medicine and healthcare | 1512 | 1425 | Muse Wonder |
| LMArena Text: legal and government | 1505 | 1405 | Muse Wonder |
| LMArena Text: business and finance | 1483 | 1399 | Muse Wonder |
| Neither model has a result on the other 14 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Muse Wonder | Qwen3.5-27B | Leader |
|---|---|---|---|
| Design Arena: websites | 1363 | Not measured | Only Muse Wonder measured |
| Design Arena: UI components | 1380 | Not measured | Only Muse Wonder measured |
| Design Arena: game dev | 1378 | Not measured | Only Muse Wonder measured |
| Design Arena: data viz | 1312 | Not measured | Only Muse Wonder measured |
| Design Arena: 3D | 1428 | Not measured | Only Muse Wonder measured |
| Design Arena: all code categories | 1371 | Not measured | Only Muse Wonder measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index150
| Benchmark | Muse Wonder | Qwen3.5-27B | Leader |
|---|---|---|---|
| Vals Index | 64.9% | Not measured | Only Muse Wonder measured |
| Public Benefits Bench (Vals AI) | 70.2% | Not measured | Only Muse Wonder measured |
| IFBench | Not measured | 75.6% | Only Qwen3.5-27B measured |
| Long context reasoning (AA-LCR) | 80.7% | 77.7% | Muse Wonder |
| DTBench | 95.2% | 82.4% | Muse Wonder |
| LMCA | 56.8% | 34.0% | Muse Wonder |
| LMArena Text | 1502 | 1409 | Muse Wonder |
| LMArena Text: instruction following | 1491 | 1393 | Muse Wonder |
| LMArena Text: creative writing | 1491 | 1362 | Muse Wonder |
| LMArena Vision | Not measured | 1241 | Only Qwen3.5-27B measured |
| LMArena Text: English | 1507 | 1425 | Muse Wonder |
| LMArena Text: non-English | 1492 | 1390 | Muse Wonder |
| LMArena Text: Chinese | 1537 | 1478 | Muse Wonder |
| LMArena Text: German | Not measured | 1392 | Only Qwen3.5-27B measured |
| LMArena Text: Japanese | Not measured | 1346 | Only Qwen3.5-27B measured |
| LMArena Text: Korean | Not measured | 1359 | Only Qwen3.5-27B measured |
| LMArena Text: French | 1523 | 1410 | Muse Wonder |
| LMArena Text: Spanish | Not measured | 1407 | Only Qwen3.5-27B measured |
| LMArena Text: Russian | 1522 | 1390 | Muse Wonder |
| LMArena Text: Polish | Not measured | 1375 | Only Qwen3.5-27B measured |
| LMArena Text: multi-turn | 1510 | 1410 | Muse Wonder |
| LMArena Text: longer queries | 1493 | 1413 | Muse Wonder |
| LMArena Text: writing and language | 1496 | 1380 | Muse Wonder |
| LMArena Text: entertainment and media | 1482 | 1363 | Muse Wonder |
| LMArena Vision: English | Not measured | 1246 | Only Qwen3.5-27B measured |
| LMArena Vision: Chinese | Not measured | 1301 | Only Qwen3.5-27B measured |
| LMArena Vision: reading text | Not measured | 1246 | Only Qwen3.5-27B measured |
| LMArena Vision: humor | Not measured | 1227 | Only Qwen3.5-27B measured |
| LMArena Vision: captioning | Not measured | 1170 | Only Qwen3.5-27B measured |
| LMArena Vision: recognizing things | Not measured | 1210 | Only Qwen3.5-27B measured |
| LMArena Vision: creative writing | Not measured | 1224 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 15 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Muse Wonder | Qwen3.5-27B | 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 | Meta Muse Wonder | Qwen3.5-27B |
|---|---|---|
| Maker | Meta | Qwen |
| Weights | No public weights listed | Open weights |
| Released | October 12, 2026 | February 25, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $4.00 | $0.195 |
| Output $/Mtok | $20.00 | $1.56 |
| Coding index | 71.9 | Not benchmarked |
| Independent measurements behind it | 3 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Muse Wonder better than Qwen3.5-27B for coding?
Only Muse Wonder has a coding score, 71.9 on the Vibe Coding Index; Qwen3.5-27B has none, so the two cannot be ranked on coding. Pick Qwen3.5-27B for output tokens 12.8x cheaper ($1.56 vs $20.00 per million).
Which is cheaper, Muse Wonder or Qwen3.5-27B?
Muse Wonder costs $4.00 per million input tokens and $20.00 per million output tokens; Qwen3.5-27B costs $0.195 and $1.56. Cost per solved task is known for neither of them.
Which has the bigger context window, Muse Wonder or Qwen3.5-27B?
Muse Wonder, with 1.05M tokens against 262K.
How many benchmarks is the Muse Wonder vs Qwen3.5-27B comparison based on?
31 benchmarks have a result for both models as of October 22, 2026. Muse Wonder's coding index rests on 3 independent benchmarks and Qwen3.5-27B's on 1. Where only one model has a result, the page says so instead of naming a winner.