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Gemma 2 27B vs Muse Wonder

Gemma 2 27B costs 30.8x less per output token than Muse Wonder ($0.65 vs $20.00 per million). Updated October 22, 2026; 26 benchmarks have a result for both.

Replace Gemma 2 27B Replace Muse Wonder
Gemma 2 27BGoogle
Coding
No data
Overall
No data
Output $/M
$0.65
0
Muse WonderMeta
Coding
71.9
Overall
No data
Output $/M
$20.00
26
vs boards won of 26 shared

Gemma 2 27B or Muse Wonder: which to pick

Only Muse Wonder has a coding score, 71.9 on the Vibe Coding Index; Gemma 2 27B has none, so the two cannot be ranked on coding.

Pick Gemma 2 27B for output tokens 30.8x cheaper ($0.65 vs $20.00 per million).

Model AGemma 2 27BGoogleNot benchmarkedNo coding benchmarkNo cost per solved taskModel BMuse WonderMeta71.9/1003 independent benchmarksNo cost per solved task
Shared results
26
Gemma 2 27B only
10
Muse Wonder only
34
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.

At a glance

Coding index
Gemma 2 27B not benchmarked, Muse Wonder 71.9 from 3 benchmarks.
Widest category
Reasoning, 52.7 points apart on average over 3 shared benchmarks.
Widest single gap
LMArena Text: expert: Gemma 2 27B 1171 against Muse Wonder 1513, 56.9 points apart on a 0 to 100 scale.
Output price
Gemma 2 27B $0.65 against Muse Wonder $20.00 per million output tokens, 30.8x apart.
Context window
Gemma 2 27B takes 8K tokens, Muse Wonder 1.05M.

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
CategoryGemma 2 27BBarsMuse WonderTies
Coding02
Intelligence01
Reasoning03
Math02
Knowledge04
General014
All shared boards026
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
Coding benchmarks for Gemma 2 27B and Muse Wonder
BenchmarkGemma 2 27BMuse WonderLeader
LMArena WebDevNot measured1652Only Muse Wonder measured
Terminal-Bench 4 (Vals AI)Not measured49.5%Only Muse Wonder measured
Vibe Code Bench (Vals AI)Not measured83.1%Only Muse Wonder measured
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
SciCodeNot measured60.3%Only Muse Wonder measured
LMArena Text: coding12111522Muse Wonder
LMArena Text: software and IT12281515Muse Wonder
LMArena WebDev: ReactNot measured1641Only Muse Wonder measured
LMArena WebDev: HTMLNot measured1694Only Muse Wonder measured
Neither model has a result on the other 26 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Gemma 2 27B and Muse Wonder
BenchmarkGemma 2 27BMuse WonderLeader
Epoch Capabilities Index122.1161.8Muse Wonder
Neither model has a result on the other 1 board here
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
Agentic work benchmarks for Gemma 2 27B and Muse Wonder
BenchmarkGemma 2 27BMuse WonderLeader
Design Arena: spreadsheetsNot measured1243Only 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
Vending-Bench 2Not measured$9,432Only 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 index03
Reasoning benchmarks for Gemma 2 27B and Muse Wonder
BenchmarkGemma 2 27BMuse WonderLeader
MysteryMechanism (Vals AI)Not measured27.5%Only Muse Wonder measured
Humanity's Last Exam (Artificial Analysis run)Not measured50.1%Only Muse Wonder measured
GPQA Diamond (Epoch AI run)36.5%Not measuredOnly Gemma 2 27B measured
LMArena Text: hard prompts11981506Muse Wonder
LMArena Text: expert11711513Muse Wonder
LMArena Text: hard prompts in English12061506Muse Wonder
Neither model has a result on the other 16 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Gemma 2 27B and Muse Wonder
BenchmarkGemma 2 27BMuse WonderLeader
OTIS Mock AIME1.4%Not measuredOnly Gemma 2 27B measured
ProofBenchNot measured84.0%Only Muse Wonder measured
MATH level 527.9%Not measuredOnly Gemma 2 27B measured
LMArena Text: math12121486Muse Wonder
LMArena Text: mathematical work12211514Muse Wonder
Neither model has a result on the other 8 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for Gemma 2 27B and Muse Wonder
BenchmarkGemma 2 27BMuse WonderLeader
LegalBench (Vals AI)68.0%Not measuredOnly Gemma 2 27B measured
MMLU75.7%Not measuredOnly Gemma 2 27B measured
LMArena Text: science12281506Muse Wonder
LMArena Text: medicine and healthcare11981512Muse Wonder
LMArena Text: legal and government12411505Muse Wonder
LMArena Text: business and finance12081483Muse Wonder
Neither model has a result on the other 12 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Web design benchmarks for Gemma 2 27B and Muse Wonder
BenchmarkGemma 2 27BMuse WonderLeader
Design Arena: websitesNot measured1362Only Muse Wonder measured
Design Arena: UI componentsNot measured1379Only Muse Wonder measured
Design Arena: game devNot measured1376Only Muse Wonder measured
Design Arena: data vizNot measured1317Only Muse Wonder measured
Design Arena: 3DNot measured1427Only Muse Wonder measured
Design Arena: all code categoriesNot measured1371Only 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 index014
General benchmarks for Gemma 2 27B and Muse Wonder
BenchmarkGemma 2 27BMuse WonderLeader
Vals IndexNot measured64.9%Only Muse Wonder measured
Public Benefits Bench (Vals AI)Not measured70.2%Only Muse Wonder measured
Long context reasoning (AA-LCR)Not measured80.7%Only Muse Wonder measured
DTBench48.0%95.2%Muse Wonder
LMCA7.1%56.8%Muse Wonder
LMArena Text12311502Muse Wonder
LMArena Text: instruction following12061491Muse Wonder
LMArena Text: creative writing12401491Muse Wonder
LMArena Text: English12421507Muse Wonder
LMArena Text: non-English12171492Muse Wonder
LMArena Text: Chinese12211537Muse Wonder
LMArena Text: German1209Not measuredOnly Gemma 2 27B measured
LMArena Text: Japanese1175Not measuredOnly Gemma 2 27B measured
LMArena Text: Korean1174Not measuredOnly Gemma 2 27B measured
LMArena Text: French12481523Muse Wonder
LMArena Text: Spanish1228Not measuredOnly Gemma 2 27B measured
LMArena Text: Russian12341522Muse Wonder
LMArena Text: Polish1277Not measuredOnly Gemma 2 27B measured
LMArena Text: multi-turn12241510Muse Wonder
LMArena Text: longer queries12301493Muse Wonder
LMArena Text: writing and language12491496Muse Wonder
LMArena Text: entertainment and media12121482Muse Wonder
Neither model has a result on the other 24 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Gemma 2 27B and Muse Wonder
BenchmarkGemma 2 27BMuse 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

Gemma 2 27B$0.0010Fits in one request
Muse Wonder$0.014Fits in one request

Gemma 2 27B costs less

Repository review

50K input + 3K output tokens

Gemma 2 27B$0.034Exceeds the context window
Muse Wonder$0.260Fits in one request

Gemma 2 27B costs less

Agent loop

200K input + 10K output tokens

Gemma 2 27B$0.137Exceeds the context window
Muse Wonder$1.00Fits in one request

Gemma 2 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 Gemma 2 27B and Muse Wonder
SpecificationGoogle Gemma 2 27BMeta Muse Wonder
MakerGoogleMeta
WeightsOpen weightsNo public weights listed
ReleasedJuly 13, 2024October 12, 2026
Context window8K1.05M
Input $/Mtok$0.65$4.00
Output $/Mtok$0.65$20.00
Coding indexNot benchmarked71.9
Independent measurements behind it03
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemma 2 27B better than Muse Wonder for coding?

Only Muse Wonder has a coding score, 71.9 on the Vibe Coding Index; Gemma 2 27B has none, so the two cannot be ranked on coding. Pick Gemma 2 27B for output tokens 30.8x cheaper ($0.65 vs $20.00 per million).

Which is cheaper, Gemma 2 27B or Muse Wonder?

Gemma 2 27B costs $0.65 per million input tokens and $0.65 per million output tokens; Muse Wonder costs $4.00 and $20.00. Cost per solved task is known for neither of them.

Which has the bigger context window, Gemma 2 27B or Muse Wonder?

Muse Wonder, with 1.05M tokens against 8K.

How many benchmarks is the Gemma 2 27B vs Muse Wonder comparison based on?

26 benchmarks have a result for both models as of October 22, 2026. Gemma 2 27B's coding index rests on 0 independent benchmarks and Muse Wonder's on 3. Where only one model has a result, the page says so instead of naming a winner.

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