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Muse Wonder vs Sonar Pro

Sonar Pro costs 1.3x less per output token than Muse Wonder ($15.00 vs $20.00 per million). Updated October 21, 2026; 1 benchmarks have a result for both.

Muse WonderMeta
Coding
71.9
Overall
No data
Output $/M
$20.00
1
Sonar ProPerplexity
Coding
No data
Overall
No data
Output $/M
$15.00
0
vs boards won of 1 shared

Muse Wonder or Sonar Pro: which to pick

Only Muse Wonder has a coding score, 71.9 on the Vibe Coding Index; Sonar Pro has none, so the two cannot be ranked on coding.

Pick Sonar Pro for output tokens 1.3x cheaper ($15.00 vs $20.00 per million).

Shared results
1
Muse Wonder only
59
Sonar Pro only
4
Like-for-like categories
1 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.

At a glance

Coding index
Muse Wonder 71.9 from 3 benchmarks, Sonar Pro not benchmarked.
Widest category
Reasoning, 43.5 points apart on average over 1 shared benchmark.
Widest single gap
Humanity's Last Exam (Artificial Analysis run): Muse Wonder 50.1% against Sonar Pro 6.6%, 43.5 points apart on a 0 to 100 scale.
Output price
Muse Wonder $20.00 against Sonar Pro $15.00 per million output tokens, 1.3x apart.
Context window
Muse Wonder takes 1.05M tokens, Sonar Pro 200K.

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 WonderBarsSonar ProTies
Reasoning10
All shared boards10
CodingDirectional onlySix of these boards feed the Vibe Coding Index14 boards, none shared
Coding benchmarks for Muse Wonder and Sonar Pro
BenchmarkMuse WonderSonar ProLeader
LMArena WebDev1652Not measuredOnly Muse Wonder measured
Terminal-Bench 4 (Vals AI)49.5%Not measuredOnly Muse Wonder measured
Vibe Code Bench (Vals AI)83.1%Not measuredOnly Muse Wonder measured
Code migration (Vals AI)62.5%Not measuredOnly Muse Wonder measured
IOI (Vals AI)66.9%Not measuredOnly Muse Wonder 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
CyberBench (Vals AI)80.4%Not measuredOnly Muse Wonder measured
LiveCodeBench (Artificial Analysis run)Not measured27.5%Only Sonar Pro measured
SciCode60.3%Not measuredOnly Muse Wonder measured
LMArena Text: coding1522Not measuredOnly Muse Wonder measured
LMArena Text: software and IT1515Not measuredOnly Muse Wonder measured
LMArena WebDev: React1641Not measuredOnly Muse Wonder measured
LMArena WebDev: HTML1694Not measuredOnly Muse Wonder measured
Neither model has a result on the other 25 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Intelligence benchmarks for Muse Wonder and Sonar Pro
BenchmarkMuse WonderSonar ProLeader
AA Intelligence IndexNot measured7.6Only Sonar Pro measured
Epoch Capabilities Index161.8Not measuredOnly Muse Wonder measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
Agentic work benchmarks for Muse Wonder and Sonar Pro
BenchmarkMuse WonderSonar ProLeader
Design Arena: spreadsheets1227Not measuredOnly Muse Wonder measured
Finance Agent (Vals AI)56.6%Not measuredOnly Muse Wonder measured
Tax Agent Bench (Vals AI)36.3%Not measuredOnly Muse Wonder measured
Legal Research Bench (Vals AI)50.5%Not measuredOnly Muse Wonder measured
Harvey's Legal Agent Benchmark (Vals AI)23.8%Not measuredOnly Muse Wonder measured
Excel Modeling Benchmark (Vals AI)67.1%Not measuredOnly Muse Wonder measured
BioMysteryBench (Vals AI)67.0%Not measuredOnly Muse Wonder measured
Terminal-Bench-Science (Vals AI)22.9%Not measuredOnly Muse Wonder measured
Vals Science Index39.1%Not measuredOnly Muse Wonder measured
CUA-bench (Vals AI)7.2%Not measuredOnly Muse Wonder measured
Vending-Bench 2$9,432Not measuredOnly Muse Wonder measured
Neither model has a result on the other 30 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
Reasoning benchmarks for Muse Wonder and Sonar Pro
BenchmarkMuse WonderSonar ProLeader
MysteryMechanism (Vals AI)27.5%Not measuredOnly Muse Wonder measured
GPQA Diamond (Artificial Analysis run)Not measured57.8%Only Sonar Pro measured
Humanity's Last Exam (Artificial Analysis run)50.1%6.6%Muse Wonder
LMArena Text: hard prompts1506Not measuredOnly Muse Wonder measured
LMArena Text: expert1513Not measuredOnly Muse Wonder measured
LMArena Text: hard prompts in English1506Not measuredOnly Muse Wonder measured
Neither model has a result on the other 16 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
Math benchmarks for Muse Wonder and Sonar Pro
BenchmarkMuse WonderSonar ProLeader
ProofBench84.0%Not measuredOnly Muse Wonder measured
LMArena Text: math1486Not measuredOnly Muse Wonder measured
LMArena Text: mathematical work1514Not measuredOnly Muse Wonder measured
Neither model has a result on the other 10 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
Knowledge benchmarks for Muse Wonder and Sonar Pro
BenchmarkMuse WonderSonar ProLeader
MMLU Pro (Artificial Analysis run)Not measured75.5%Only Sonar Pro measured
LMArena Text: science1506Not measuredOnly Muse Wonder measured
LMArena Text: medicine and healthcare1512Not measuredOnly Muse Wonder measured
LMArena Text: legal and government1505Not measuredOnly Muse Wonder measured
LMArena Text: business and finance1483Not measuredOnly Muse Wonder measured
Neither model has a result on the other 13 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Web design benchmarks for Muse Wonder and Sonar Pro
BenchmarkMuse WonderSonar ProLeader
Design Arena: websites1362Not measuredOnly Muse Wonder measured
Design Arena: UI components1374Not measuredOnly Muse Wonder measured
Design Arena: game dev1377Not measuredOnly Muse Wonder measured
Design Arena: data viz1321Not measuredOnly Muse Wonder measured
Design Arena: 3D1426Not measuredOnly Muse Wonder measured
Design Arena: all code categories1371Not measuredOnly Muse Wonder measured
Neither model has a result on the other 3 boards here
GeneralDirectional onlyPublic boards, shown as published, kept out of the index17 boards, none shared
General benchmarks for Muse Wonder and Sonar Pro
BenchmarkMuse WonderSonar ProLeader
Vals Index64.9%Not measuredOnly Muse Wonder measured
Public Benefits Bench (Vals AI)70.2%Not measuredOnly Muse Wonder measured
Long context reasoning (AA-LCR)80.7%Not measuredOnly Muse Wonder measured
DTBench95.2%Not measuredOnly Muse Wonder measured
LMCA56.8%Not measuredOnly Muse Wonder measured
LMArena Text1502Not measuredOnly Muse Wonder measured
LMArena Text: instruction following1491Not measuredOnly Muse Wonder measured
LMArena Text: creative writing1491Not measuredOnly Muse Wonder measured
LMArena Text: English1507Not measuredOnly Muse Wonder measured
LMArena Text: non-English1492Not measuredOnly Muse Wonder measured
LMArena Text: Chinese1537Not measuredOnly Muse Wonder measured
LMArena Text: French1523Not measuredOnly Muse Wonder measured
LMArena Text: Russian1522Not measuredOnly Muse Wonder measured
LMArena Text: multi-turn1510Not measuredOnly Muse Wonder measured
LMArena Text: longer queries1493Not measuredOnly Muse Wonder measured
LMArena Text: writing and language1496Not measuredOnly Muse Wonder measured
LMArena Text: entertainment and media1482Not measuredOnly Muse Wonder measured
Neither model has a result on the other 29 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Muse Wonder and Sonar Pro
BenchmarkMuse WonderSonar ProLeader
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
Sonar Pro$0.011Fits in one request

Sonar Pro costs less

Repository review

50K input + 3K output tokens

Muse Wonder$0.260Fits in one request
Sonar Pro$0.195Fits in one request

Sonar Pro costs less

Agent loop

200K input + 10K output tokens

Muse Wonder$1.00Fits in one request
Sonar Pro$0.750Exceeds the context window

Sonar Pro 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 Sonar Pro
SpecificationMeta Muse WonderPerplexity Sonar Pro
MakerMetaPerplexity
WeightsNo public weights listedNo public weights listed
ReleasedOctober 12, 2026March 7, 2025
Context window1.05M200K
Input $/Mtok$4.00$3.00
Output $/Mtok$20.00$15.00
Coding index71.9Not benchmarked
Independent measurements behind it30
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Muse Wonder better than Sonar Pro for coding?

Only Muse Wonder has a coding score, 71.9 on the Vibe Coding Index; Sonar Pro has none, so the two cannot be ranked on coding. Pick Sonar Pro for output tokens 1.3x cheaper ($15.00 vs $20.00 per million).

Which is cheaper, Muse Wonder or Sonar Pro?

Muse Wonder costs $4.00 per million input tokens and $20.00 per million output tokens; Sonar Pro costs $3.00 and $15.00. Cost per solved task is known for neither of them.

Which has the bigger context window, Muse Wonder or Sonar Pro?

Muse Wonder, with 1.05M tokens against 200K.

How many benchmarks is the Muse Wonder vs Sonar Pro comparison based on?

1 benchmarks have a result for both models as of October 21, 2026. Muse Wonder's coding index rests on 3 independent benchmarks and Sonar Pro's on 0. Where only one model has a result, the page says so instead of naming a winner.

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