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Muse Wonder vs Qwen3.8 27B

Updated October 21, 2026. 42 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace Muse Wonder Replace Qwen3.8 27B
Muse WonderMeta
Coding
64.5
Overall
No data
Output $/M
$20.00
41
Qwen3.8 27BQwen
Coding
73.6
Overall
33.7
Output $/M
$1.35
1
vs boards won of 42 shared

Decision reading

Pick Qwen3.8 27B for coding: it scores 73.6 on the Vibe Coding Index against 64.5 for Muse Wonder.

Pick Muse Wonder when the context window is larger, 1.05M against 1M tokens, or when what matters is LMArena WebDev or Vibe Code Bench (Vals AI), where it leads.

Model AMuse WonderMeta64.5/1001 independent benchmarkNo cost per solved taskModel BQwen3.8 27BQwen73.6/1002 independent benchmarks$1.26 per solved task
Shared results
42
Muse Wonder only
18
Qwen3.8 27B only
43
Like-for-like categories
7 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.

At a glance

Coding index
Muse Wonder 64.5 from 1 benchmark, Qwen3.8 27B 73.6 from 2 benchmarks.
Widest category
Math, 27.5 points apart on average over 3 shared benchmarks.
Widest single gap
ProofBench: Muse Wonder 84.0% against Qwen3.8 27B 16.0%, 68.0 points apart on a 0 to 100 scale.
Output price
Muse Wonder $20.00 against Qwen3.8 27B $1.35 per million output tokens, 14.8x apart.
Context window
Muse Wonder takes 1.05M tokens, Qwen3.8 27B 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.

Boards won, where both have a result
CategoryMuse WonderBarsQwen3.8 27BTies
Coding100
Intelligence10
Agentic work50
Reasoning40
Math30
Knowledge40
General141
All shared boards411
CodingLike-for-likeFour of these boards feed the Vibe Coding Index100
Coding benchmarks for Muse Wonder and Qwen3.8 27B
BenchmarkMuse WonderQwen3.8 27BLeader
LiveBench codingNot measured86.8%Only Qwen3.8 27B measured
LMArena WebDev16521593Muse Wonder
AA Coding IndexNot measured68.1Only Qwen3.8 27B measured
LiveCodeBench (Vals AI)Not measured84.0%Only Qwen3.8 27B measured
SWE-bench Verified (Vals AI)Not measured86.0%Only Qwen3.8 27B measured
Terminal-Bench 4 (Vals AI)49.5%Not measuredOnly Muse Wonder measured
Vibe Code Bench (Vals AI)83.1%64.8%Muse Wonder
Code migration (Vals AI)62.5%14.2%Muse Wonder
ProgramBench (Vals AI)Not measured0.0%Only Qwen3.8 27B measured
IOI (Vals AI)66.9%39.1%Muse Wonder
Terminal-Bench 2.1 (Vals AI)Not measured58.4%Only Qwen3.8 27B measured
SWE-bench Multimodal (Vals AI)45.6%Not measuredOnly Muse Wonder measured
Vibe Code Bench 1-100 (Vals AI)26.6%Not measuredOnly Muse Wonder measured
SkillsBench (Vals AI)Not measured38.1%Only Qwen3.8 27B measured
CyberBench (Vals AI)80.4%67.8%Muse Wonder
SciCode60.3%46.6%Muse Wonder
LMArena Text: coding15221483Muse Wonder
LMArena Text: software and IT15151478Muse Wonder
LMArena WebDev: React16411602Muse Wonder
LMArena WebDev: HTML16941570Muse Wonder
LMArena WebDev: from an imageNot measured1577Only Qwen3.8 27B measured
Neither model has a result on the other 17 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for Muse Wonder and Qwen3.8 27B
BenchmarkMuse WonderQwen3.8 27BLeader
AA Intelligence IndexNot measured33.7Only Qwen3.8 27B measured
Epoch Capabilities Index161.8151.7Muse Wonder
Agentic workLike-for-likePublic boards, shown as published, kept out of the index50
Agentic work benchmarks for Muse Wonder and Qwen3.8 27B
BenchmarkMuse WonderQwen3.8 27BLeader
AA Agentic IndexNot measured45.8Only Qwen3.8 27B measured
Design Arena: spreadsheets1236Not measuredOnly Muse Wonder measured
LiveBench researchNot measured59.4%Only Qwen3.8 27B measured
LiveBench browserNot measured32.0%Only Qwen3.8 27B measured
LiveBench long agenticNot measured27.9%Only Qwen3.8 27B measured
Finance Agent (Vals AI)56.6%48.5%Muse Wonder
Tax Agent Bench (Vals AI)36.3%30.9%Muse Wonder
Legal Research Bench (Vals AI)50.5%36.1%Muse Wonder
Harvey's Legal Agent Benchmark (Vals AI)23.8%Not measuredOnly Muse Wonder measured
Excel Modeling Benchmark (Vals AI)67.1%59.7%Muse Wonder
BioMysteryBench (Vals AI)67.0%Not measuredOnly Muse Wonder measured
Terminal-Bench-Science (Vals AI)22.9%0.0%Muse Wonder
Vals Science Index39.1%Not measuredOnly Muse Wonder measured
CUA-bench (Vals AI)7.2%Not measuredOnly Muse Wonder measured
APEX-AgentsNot measured47.5%Only Qwen3.8 27B measured
Vending-Bench 2$9,432Not measuredOnly Muse Wonder measured
Neither model has a result on the other 23 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index40
Reasoning benchmarks for Muse Wonder and Qwen3.8 27B
BenchmarkMuse WonderQwen3.8 27BLeader
GPQA DiamondNot measured88.9%Only Qwen3.8 27B measured
LiveBench reasoningNot measured80.0%Only Qwen3.8 27B measured
MysteryMechanism (Vals AI)27.5%Not measuredOnly Muse Wonder measured
MMMU Pro (Vals AI)Not measured83.9%Only Qwen3.8 27B measured
GPQA Diamond (Artificial Analysis run)Not measured90.5%Only Qwen3.8 27B measured
Humanity's Last Exam (Artificial Analysis run)50.1%33.9%Muse Wonder
ARC-AGINot measured87.5%Only Qwen3.8 27B measured
ARC-AGI-2Not measured42.4%Only Qwen3.8 27B measured
LMArena Text: hard prompts15061459Muse Wonder
LMArena Text: expert15131479Muse Wonder
LMArena Text: hard prompts in English15061461Muse Wonder
LMArena Vision: diagramsNot measured1285Only Qwen3.8 27B measured
LMArena Vision: homeworkNot measured1295Only Qwen3.8 27B measured
Neither model has a result on the other 8 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for Muse Wonder and Qwen3.8 27B
BenchmarkMuse WonderQwen3.8 27BLeader
ProofBench84.0%16.0%Muse Wonder
LMArena Text: math14861455Muse Wonder
LMArena Text: mathematical work15141458Muse Wonder
Neither model has a result on the other 10 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for Muse Wonder and Qwen3.8 27B
BenchmarkMuse WonderQwen3.8 27BLeader
MMLU ProNot measured84.3%Only Qwen3.8 27B measured
LegalBench (Vals AI)Not measured82.4%Only Qwen3.8 27B measured
TaxEval (Vals AI)Not measured70.8%Only Qwen3.8 27B measured
MortgageTax (Vals AI)Not measured64.9%Only Qwen3.8 27B measured
MedCode (Vals AI)Not measured28.7%Only Qwen3.8 27B measured
LMArena Text: science15061467Muse Wonder
LMArena Text: medicine and healthcare15121437Muse Wonder
LMArena Text: legal and government15051447Muse Wonder
LMArena Text: business and finance14831438Muse Wonder
Neither model has a result on the other 8 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for Muse Wonder and Qwen3.8 27B
BenchmarkMuse WonderQwen3.8 27BLeader
Design Arena: websites1361Not measuredOnly Muse Wonder measured
Design Arena: UI components1371Not measuredOnly Muse Wonder measured
Design Arena: game dev1380Not measuredOnly Muse Wonder measured
Design Arena: data viz1330Not measuredOnly Muse Wonder measured
Design Arena: 3D1425Not measuredOnly Muse Wonder measured
Design Arena: all code categories1370Not measuredOnly Muse Wonder measured
LiveBench designNot measured75.9%Only Qwen3.8 27B measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index141
General benchmarks for Muse Wonder and Qwen3.8 27B
BenchmarkMuse WonderQwen3.8 27BLeader
Vals Index64.9%Not measuredOnly Muse Wonder measured
LiveBench data analysisNot measured76.6%Only Qwen3.8 27B measured
Public Benefits Bench (Vals AI)70.2%Not measuredOnly Muse Wonder measured
SAGE (Vals AI)Not measured52.4%Only Qwen3.8 27B measured
MedScribe (Vals AI)Not measured83.8%Only Qwen3.8 27B measured
Long context reasoning (AA-LCR)80.7%82.0%Qwen3.8 27B
DTBench95.2%88.0%Muse Wonder
LMCA56.8%41.4%Muse Wonder
Surface Evolver BenchNot measured45.0%Only Qwen3.8 27B measured
LMArena Text15021440Muse Wonder
LMArena Text: instruction following14911436Muse Wonder
LMArena Text: creative writing14911383Muse Wonder
LMArena VisionNot measured1269Only Qwen3.8 27B measured
LMArena Text: English15071448Muse Wonder
LMArena Text: non-English14921428Muse Wonder
LMArena Text: Chinese15371501Muse Wonder
LMArena Text: GermanNot measured1443Only Qwen3.8 27B measured
LMArena Text: JapaneseNot measured1387Only Qwen3.8 27B measured
LMArena Text: KoreanNot measured1384Only Qwen3.8 27B measured
LMArena Text: French15231465Muse Wonder
LMArena Text: SpanishNot measured1449Only Qwen3.8 27B measured
LMArena Text: Russian15221415Muse Wonder
LMArena Text: PolishNot measured1439Only Qwen3.8 27B measured
LMArena Text: multi-turn15101440Muse Wonder
LMArena Text: longer queries14931448Muse Wonder
LMArena Text: writing and language14961405Muse Wonder
LMArena Text: entertainment and media14821393Muse Wonder
LMArena Vision: EnglishNot measured1267Only Qwen3.8 27B measured
LMArena Vision: ChineseNot measured1326Only Qwen3.8 27B measured
LMArena Vision: reading textNot measured1281Only Qwen3.8 27B measured
LMArena Vision: humorNot measured1235Only Qwen3.8 27B measured
LMArena Vision: creative writingNot measured1237Only Qwen3.8 27B measured
Neither model has a result on the other 14 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Muse Wonder and Qwen3.8 27B
BenchmarkMuse WonderQwen3.8 27BLeader
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

Muse Wonder$0.014Fits in one request
Qwen3.8 27B$0.0007Fits in one request

Qwen3.8 27B costs less

Repository review

50K input + 3K output tokens

Muse Wonder$0.260Fits in one request
Qwen3.8 27B$0.0060Fits in one request

Qwen3.8 27B costs less

Agent loop

200K input + 10K output tokens

Muse Wonder$1.00Fits in one request
Qwen3.8 27B$0.021Fits in one request

Qwen3.8 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.

Specifications of Muse Wonder and Qwen3.8 27B
SpecificationMeta Muse WonderQwen3.8 27B
MakerMetaQwen
WeightsNo public weights listedOpen weights
ReleasedOctober 12, 2026August 14, 2026
Context window1.05M1M
Input $/Mtok$4.00$0.04
Output $/Mtok$20.00$1.35
Coding index64.573.6
Independent measurements behind it12
Cost per solved taskNot computable$1.26
SourceOpenRouter listingOpenRouter listing

Questions

Is Muse Wonder better than Qwen3.8 27B for coding?

Pick Qwen3.8 27B for coding: it scores 73.6 on the Vibe Coding Index against 64.5 for Muse Wonder. Pick Muse Wonder when the context window is larger, 1.05M against 1M tokens, or when what matters is LMArena WebDev or Vibe Code Bench (Vals AI), where it leads.

Which is cheaper, Muse Wonder or Qwen3.8 27B?

Muse Wonder costs $4.00 per million input tokens and $20.00 per million output tokens; Qwen3.8 27B costs $0.04 and $1.35. Cost per solved task is known for Qwen3.8 27B only.

Which has the bigger context window, Muse Wonder or Qwen3.8 27B?

Muse Wonder, with 1.05M tokens against 1M.

How many benchmarks is the Muse Wonder vs Qwen3.8 27B comparison based on?

42 benchmarks have a result for both models as of October 21, 2026. Muse Wonder's coding index rests on 1 independent benchmark and Qwen3.8 27B's on 2. Where only one model has a result, the page says so instead of naming a winner.

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