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Gemini 2.5 Pro vs Muse Wonder

Updated October 21, 2026. 39 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Gemini 2.5 ProGoogle
Coding
37.0
Overall
16.1
Output $/M
$10.00
0
Muse WonderMeta
Coding
64.5
Overall
No data
Output $/M
$20.00
39
vs boards won of 39 shared

Decision reading

Pick Muse Wonder for coding: it scores 64.5 on the Vibe Coding Index against 37.0 for Gemini 2.5 Pro, though on 1 benchmark against 2.

Pick Gemini 2.5 Pro when output tokens cost 2.0x less.

Shared results
39
Gemini 2.5 Pro only
48
Muse Wonder only
21
Like-for-like categories
8 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
Gemini 2.5 Pro 37.0 from 2 benchmarks, Muse Wonder 64.5 from 1 benchmark.
Widest category
Agentic work, 55.4 points apart on average over 1 shared benchmark.
Widest single gap
Vibe Code Bench (Vals AI): Gemini 2.5 Pro 0.4% against Muse Wonder 83.1%, 82.7 points apart on a 0 to 100 scale.
Output price
Gemini 2.5 Pro $10.00 against Muse Wonder $20.00 per million output tokens, 2.0x apart.
Context window
Both take 1.05M 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.

Boards won, where both have a result
CategoryGemini 2.5 ProBarsMuse WonderTies
Coding06
Intelligence01
Agentic work01
Reasoning04
Math02
Knowledge04
Web design06
General015
All shared boards039
CodingLike-for-likeFour of these boards feed the Vibe Coding Index06
Coding benchmarks for Gemini 2.5 Pro and Muse Wonder
BenchmarkGemini 2.5 ProMuse WonderLeader
SWE-bench Verified53.6%Not measuredOnly Gemini 2.5 Pro measured
LMArena WebDev12261652Muse Wonder
AA Coding Index33.3Not measuredOnly Gemini 2.5 Pro measured
SWE-bench Verified (Vals AI)54.4%Not measuredOnly Gemini 2.5 Pro measured
Terminal-Bench 4 (Vals AI)Not measured49.5%Only Muse Wonder measured
Vibe Code Bench (Vals AI)0.4%83.1%Muse Wonder
Code migration (Vals AI)Not measured62.5%Only Muse Wonder measured
IOI (Vals AI)Not measured66.9%Only Muse Wonder measured
SWE-bench Multimodal (Vals AI)Not measured45.6%Only Muse Wonder measured
Vibe Code Bench 1-100 (Vals AI)Not measured26.6%Only Muse Wonder measured
CyberBench (Vals AI)Not measured80.4%Only Muse Wonder measured
LiveCodeBench (Artificial Analysis run)80.1%Not measuredOnly Gemini 2.5 Pro measured
SciCode46.3%60.3%Muse Wonder
Terminal-Bench Hard (Artificial Analysis run)26.5%Not measuredOnly Gemini 2.5 Pro measured
Terminal-Bench32.6%Not measuredOnly Gemini 2.5 Pro measured
GSO-Bench3.9%Not measuredOnly Gemini 2.5 Pro measured
WeirdML54.0%Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: coding14521522Muse Wonder
LMArena Text: software and IT14591515Muse Wonder
LMArena WebDev: ReactNot measured1641Only Muse Wonder measured
LMArena WebDev: HTML12351694Muse Wonder
LMArena WebDev: from an image1285Not measuredOnly Gemini 2.5 Pro measured
Neither model has a result on the other 16 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Gemini 2.5 Pro and Muse Wonder
BenchmarkGemini 2.5 ProMuse WonderLeader
AA Intelligence Index16.1Not measuredOnly Gemini 2.5 Pro measured
Epoch Capabilities Index145.1161.8Muse Wonder
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Gemini 2.5 Pro and Muse Wonder
BenchmarkGemini 2.5 ProMuse WonderLeader
AA Agentic Index1.6Not measuredOnly Gemini 2.5 Pro measured
Design Arena: spreadsheetsNot measured1236Only Muse Wonder measured
Finance Agent (Vals AI)Not measured56.6%Only Muse Wonder measured
Tax Agent Bench (Vals AI)Not measured36.3%Only Muse Wonder measured
Legal Research Bench (Vals AI)Not measured50.5%Only Muse Wonder measured
Harvey's Legal Agent Benchmark (Vals AI)Not measured23.8%Only Muse Wonder measured
Excel Modeling Benchmark (Vals AI)Not measured67.1%Only Muse Wonder measured
BioMysteryBench (Vals AI)Not measured67.0%Only Muse Wonder measured
Terminal-Bench-Science (Vals AI)Not measured22.9%Only Muse Wonder measured
Vals Science IndexNot measured39.1%Only Muse Wonder measured
CUA-bench (Vals AI)Not measured7.2%Only Muse Wonder measured
τ²-Bench Telecom54.1%Not measuredOnly Gemini 2.5 Pro measured
GDPval23.3%Not measuredOnly Gemini 2.5 Pro measured
DeepResearch Bench41.5%Not measuredOnly Gemini 2.5 Pro measured
Vending-Bench 2$574$9,432Muse Wonder
Neither model has a result on the other 24 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
Reasoning benchmarks for Gemini 2.5 Pro and Muse Wonder
BenchmarkGemini 2.5 ProMuse WonderLeader
MysteryMechanism (Vals AI)Not measured27.5%Only Muse Wonder measured
GPQA Diamond (Artificial Analysis run)84.4%Not measuredOnly Gemini 2.5 Pro measured
Humanity's Last Exam (Artificial Analysis run)22.5%50.1%Muse Wonder
GPQA Diamond (Epoch AI run)85.3%Not measuredOnly Gemini 2.5 Pro measured
ARC-AGI41.0%Not measuredOnly Gemini 2.5 Pro measured
ARC-AGI-24.9%Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: hard prompts14551506Muse Wonder
LMArena Text: expert14521513Muse Wonder
LMArena Text: hard prompts in English14531506Muse Wonder
LMArena Vision: diagrams1274Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: homework1281Not measuredOnly Gemini 2.5 Pro measured
Neither model has a result on the other 10 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Gemini 2.5 Pro and Muse Wonder
BenchmarkGemini 2.5 ProMuse WonderLeader
AA Math Index87.7Not measuredOnly Gemini 2.5 Pro measured
AIME 2025 (Artificial Analysis run)87.7%Not measuredOnly Gemini 2.5 Pro measured
OTIS Mock AIME84.7%Not measuredOnly Gemini 2.5 Pro measured
FrontierMath, tiers 1 to 324.6%Not measuredOnly Gemini 2.5 Pro measured
FrontierMath, tier 40.0%Not measuredOnly Gemini 2.5 Pro measured
ProofBenchNot measured84.0%Only Muse Wonder measured
LMArena Text: math14501486Muse Wonder
LMArena Text: mathematical work14541514Muse Wonder
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for Gemini 2.5 Pro and Muse Wonder
BenchmarkGemini 2.5 ProMuse WonderLeader
CaseLaw (Vals AI)63.9%Not measuredOnly Gemini 2.5 Pro measured
CorpFin (Vals AI)60.8%Not measuredOnly Gemini 2.5 Pro measured
MortgageTax (Vals AI)68.9%Not measuredOnly Gemini 2.5 Pro measured
MedCode (Vals AI)50.6%Not measuredOnly Gemini 2.5 Pro measured
MMLU Pro (Artificial Analysis run)86.2%Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: science14801506Muse Wonder
LMArena Text: medicine and healthcare14761512Muse Wonder
LMArena Text: legal and government14801505Muse Wonder
LMArena Text: business and finance14391483Muse Wonder
Neither model has a result on the other 8 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index06
Web design benchmarks for Gemini 2.5 Pro and Muse Wonder
BenchmarkGemini 2.5 ProMuse WonderLeader
Design Arena: websites11671361Muse Wonder
Design Arena: UI components11391371Muse Wonder
Design Arena: game dev11121380Muse Wonder
Design Arena: data viz12221330Muse Wonder
Design Arena: 3D10801425Muse Wonder
Design Arena: all code categories11521370Muse Wonder
Neither model has a result on the other 3 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index015
General benchmarks for Gemini 2.5 Pro and Muse Wonder
BenchmarkGemini 2.5 ProMuse WonderLeader
Vals IndexNot measured64.9%Only Muse Wonder measured
Public Benefits Bench (Vals AI)Not measured70.2%Only Muse Wonder measured
SAGE (Vals AI)41.9%Not measuredOnly Gemini 2.5 Pro measured
MedScribe (Vals AI)73.5%Not measuredOnly Gemini 2.5 Pro measured
IFBench48.7%Not measuredOnly Gemini 2.5 Pro measured
Long context reasoning (AA-LCR)69.0%80.7%Muse Wonder
DTBench82.4%95.2%Muse Wonder
LMCA34.8%56.8%Muse Wonder
Creative writing (Lech Mazur)83.8%Not measuredOnly Gemini 2.5 Pro measured
LMArena Text14581502Muse Wonder
LMArena Text: instruction following14371491Muse Wonder
LMArena Text: creative writing14541491Muse Wonder
LMArena Vision1263Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: English14581507Muse Wonder
LMArena Text: non-English14521492Muse Wonder
LMArena Text: Chinese15071537Muse Wonder
LMArena Text: German1487Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: Japanese1461Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: Korean1434Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: French14731523Muse Wonder
LMArena Text: Spanish1474Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: Russian14611522Muse Wonder
LMArena Text: Polish1479Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: multi-turn14531510Muse Wonder
LMArena Text: longer queries14491493Muse Wonder
LMArena Text: writing and language14541496Muse Wonder
LMArena Text: entertainment and media14411482Muse Wonder
LMArena Vision: English1261Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: Chinese1300Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: reading text1270Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: humor1262Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: captioning1304Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: recognizing things1283Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: creative writing1265Not measuredOnly Gemini 2.5 Pro measured
LMArena Document1421Not measuredOnly Gemini 2.5 Pro measured
Neither model has a result on the other 11 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Gemini 2.5 Pro and Muse Wonder
BenchmarkGemini 2.5 ProMuse WonderLeader
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

Gemini 2.5 Pro$0.0063Fits in one request
Muse Wonder$0.014Fits in one request

Gemini 2.5 Pro costs less

Repository review

50K input + 3K output tokens

Gemini 2.5 Pro$0.092Fits in one request
Muse Wonder$0.260Fits in one request

Gemini 2.5 Pro costs less

Agent loop

200K input + 10K output tokens

Gemini 2.5 Pro$0.350Fits in one request
Muse Wonder$1.00Fits in one request

Gemini 2.5 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 Gemini 2.5 Pro and Muse Wonder
SpecificationGoogle Gemini 2.5 ProMeta Muse Wonder
MakerGoogleMeta
WeightsNo public weights listedNo public weights listed
ReleasedJune 17, 2025October 12, 2026
Context window1.05M1.05M
Input $/Mtok$1.25$4.00
Output $/Mtok$10.00$20.00
Coding index37.064.5
Independent measurements behind it21
Cost per solved task$0.64Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemini 2.5 Pro better than Muse Wonder for coding?

Pick Muse Wonder for coding: it scores 64.5 on the Vibe Coding Index against 37.0 for Gemini 2.5 Pro, though on 1 benchmark against 2. Pick Gemini 2.5 Pro when output tokens cost 2.0x less.

Which is cheaper, Gemini 2.5 Pro or Muse Wonder?

Gemini 2.5 Pro costs $1.25 per million input tokens and $10.00 per million output tokens; Muse Wonder costs $4.00 and $20.00. Cost per solved task is known for Gemini 2.5 Pro only.

Which has the bigger context window, Gemini 2.5 Pro or Muse Wonder?

Neither: both take 1.05M tokens.

How many benchmarks is the Gemini 2.5 Pro vs Muse Wonder comparison based on?

39 benchmarks have a result for both models as of October 21, 2026. Gemini 2.5 Pro's coding index rests on 2 independent benchmarks and Muse Wonder's on 1. Where only one model has a result, the page says so instead of naming a winner.

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