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Gemma 4 26B A4B vs Muse Glimmer 30B

Muse Glimmer 30B leads Gemma 4 26B A4B on the Artificial Analysis Intelligence Index, 17.5 to 16.7, and Gemma 4 26B A4B costs 2.3x less per output token ($0.60 vs $1.35 per million). Updated October 22, 2026; 4 benchmarks have a result for both.

Gemma 4 26B A4BGoogle
Coding
No data
Overall
54.7
Output $/M
$0.60
0
Muse Glimmer 30BMeta
Coding
No data
Overall
No data
Output $/M
$1.35
4
vs boards won of 4 shared

Gemma 4 26B A4B or Muse Glimmer 30B: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick Gemma 4 26B A4B for output tokens 2.3x cheaper ($0.60 vs $1.35 per million) and a larger context window (262K against 131K tokens).

Shared results
4
Gemma 4 26B A4B only
51
Muse Glimmer 30B only
1
Like-for-like categories
3 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 4 26B A4B not benchmarked, Muse Glimmer 30B not benchmarked.
Widest category
Agentic work, 5.6 points apart on average over 1 shared benchmark.
Widest single gap
AA Coding Index: Gemma 4 26B A4B 39.3 against Muse Glimmer 30B 49.0, 9.7 points apart on a 0 to 100 scale.
Output price
Gemma 4 26B A4B $0.60 against Muse Glimmer 30B $1.35 per million output tokens, 2.3x apart.
Context window
Gemma 4 26B A4B takes 262K tokens, Muse Glimmer 30B 131K.

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 4 26B A4BBarsMuse Glimmer 30BTies
Coding02
Intelligence01
Agentic work01
All shared boards04
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
Coding benchmarks for Gemma 4 26B A4B and Muse Glimmer 30B
BenchmarkGemma 4 26B A4BMuse Glimmer 30BLeader
LMArena WebDev1360Not measuredOnly Gemma 4 26B A4B measured
AA Coding Index39.349.0Muse Glimmer 30B
SciCode40.0%Not measuredOnly Gemma 4 26B A4B measured
Terminal-Bench Hard (Artificial Analysis run)25.0%Not measuredOnly Gemma 4 26B A4B measured
WeirdML35.2%Not measuredOnly Gemma 4 26B A4B measured
ALE-Bench927.2962.5Muse Glimmer 30B
LMArena Text: coding1446Not measuredOnly Gemma 4 26B A4B measured
LMArena Text: software and IT1454Not measuredOnly Gemma 4 26B A4B measured
LMArena WebDev: React1351Not measuredOnly Gemma 4 26B A4B measured
LMArena WebDev: HTML1362Not measuredOnly Gemma 4 26B A4B measured
Neither model has a result on the other 29 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Gemma 4 26B A4B and Muse Glimmer 30B
BenchmarkGemma 4 26B A4BMuse Glimmer 30BLeader
AA Intelligence Index16.717.5Muse Glimmer 30B
Epoch Capabilities Index141.9Not measuredOnly Gemma 4 26B A4B measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Gemma 4 26B A4B and Muse Glimmer 30B
BenchmarkGemma 4 26B A4BMuse Glimmer 30BLeader
AA Agentic Index2.98.5Muse Glimmer 30B
τ²-Bench Telecom43.6%Not measuredOnly Gemma 4 26B A4B measured
Neither model has a result on the other 39 boards here
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Reasoning benchmarks for Gemma 4 26B A4B and Muse Glimmer 30B
BenchmarkGemma 4 26B A4BMuse Glimmer 30BLeader
GPQA Diamond (Artificial Analysis run)79.2%Not measuredOnly Gemma 4 26B A4B measured
Humanity's Last Exam (Artificial Analysis run)19.3%Not measuredOnly Gemma 4 26B A4B measured
GPQA Diamond (Epoch AI run)73.2%Not measuredOnly Gemma 4 26B A4B measured
LMArena Text: hard prompts1438Not measuredOnly Gemma 4 26B A4B measured
LMArena Text: expert1447Not measuredOnly Gemma 4 26B A4B measured
LMArena Text: hard prompts in English1448Not measuredOnly Gemma 4 26B A4B measured
LMArena Vision: diagrams1264Not measuredOnly Gemma 4 26B A4B measured
LMArena Vision: homework1293Not measuredOnly Gemma 4 26B A4B measured
Neither model has a result on the other 14 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
Math benchmarks for Gemma 4 26B A4B and Muse Glimmer 30B
BenchmarkGemma 4 26B A4BMuse Glimmer 30BLeader
OTIS Mock AIME82.2%Not measuredOnly Gemma 4 26B A4B measured
LMArena Text: math1470Not measuredOnly Gemma 4 26B A4B measured
LMArena Text: mathematical work1475Not measuredOnly Gemma 4 26B A4B 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 Gemma 4 26B A4B and Muse Glimmer 30B
BenchmarkGemma 4 26B A4BMuse Glimmer 30BLeader
LMArena Text: science1446Not measuredOnly Gemma 4 26B A4B measured
LMArena Text: medicine and healthcare1428Not measuredOnly Gemma 4 26B A4B measured
LMArena Text: legal and government1457Not measuredOnly Gemma 4 26B A4B measured
LMArena Text: business and finance1429Not measuredOnly Gemma 4 26B A4B measured
FACTS Benchmark Suite42.7%Not measuredOnly Gemma 4 26B A4B measured
Neither model has a result on the other 13 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Web design benchmarks for Gemma 4 26B A4B and Muse Glimmer 30B
BenchmarkGemma 4 26B A4BMuse Glimmer 30BLeader
Design Arena: game devNot measured1131Only Muse Glimmer 30B measured
Neither model has a result on the other 8 boards here
GeneralDirectional onlyPublic boards, shown as published, kept out of the index25 boards, none shared
General benchmarks for Gemma 4 26B A4B and Muse Glimmer 30B
BenchmarkGemma 4 26B A4BMuse Glimmer 30BLeader
IFBench72.4%Not measuredOnly Gemma 4 26B A4B measured
Long context reasoning (AA-LCR)65.7%Not measuredOnly Gemma 4 26B A4B measured
DTBench74.9%Not measuredOnly Gemma 4 26B A4B measured
LMCA29.7%Not measuredOnly Gemma 4 26B A4B measured
LMArena Text1434Not measuredOnly Gemma 4 26B A4B measured
LMArena Text: instruction following1420Not measuredOnly Gemma 4 26B A4B measured
LMArena Text: creative writing1400Not measuredOnly Gemma 4 26B A4B measured
LMArena Vision1260Not measuredOnly Gemma 4 26B A4B measured
LMArena Text: English1444Not measuredOnly Gemma 4 26B A4B measured
LMArena Text: non-English1421Not measuredOnly Gemma 4 26B A4B measured
LMArena Text: Chinese1495Not measuredOnly Gemma 4 26B A4B measured
LMArena Text: French1460Not measuredOnly Gemma 4 26B A4B measured
LMArena Text: Spanish1415Not measuredOnly Gemma 4 26B A4B measured
LMArena Text: Russian1434Not measuredOnly Gemma 4 26B A4B measured
LMArena Text: multi-turn1441Not measuredOnly Gemma 4 26B A4B measured
LMArena Text: longer queries1427Not measuredOnly Gemma 4 26B A4B measured
LMArena Text: writing and language1405Not measuredOnly Gemma 4 26B A4B measured
LMArena Text: entertainment and media1403Not measuredOnly Gemma 4 26B A4B measured
LMArena Vision: English1256Not measuredOnly Gemma 4 26B A4B measured
LMArena Vision: Chinese1312Not measuredOnly Gemma 4 26B A4B measured
LMArena Vision: reading text1268Not measuredOnly Gemma 4 26B A4B measured
LMArena Vision: humor1272Not measuredOnly Gemma 4 26B A4B measured
LMArena Vision: captioning1297Not measuredOnly Gemma 4 26B A4B measured
LMArena Vision: recognizing things1191Not measuredOnly Gemma 4 26B A4B measured
LMArena Vision: creative writing1256Not measuredOnly Gemma 4 26B A4B measured
Neither model has a result on the other 21 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Gemma 4 26B A4B and Muse Glimmer 30B
BenchmarkGemma 4 26B A4BMuse Glimmer 30BLeader
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 4 26B A4B$0.0004Fits in one request
Muse Glimmer 30B$0.0010Fits in one request

Gemma 4 26B A4B costs less

Repository review

50K input + 3K output tokens

Gemma 4 26B A4B$0.0093Fits in one request
Muse Glimmer 30B$0.020Fits in one request

Gemma 4 26B A4B costs less

Agent loop

200K input + 10K output tokens

Gemma 4 26B A4B$0.036Fits in one request
Muse Glimmer 30B$0.079Exceeds the context window

Gemma 4 26B A4B 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 4 26B A4B and Muse Glimmer 30B
SpecificationGoogle Gemma 4 26B A4BMeta Muse Glimmer 30B
MakerGoogleMeta
WeightsOpen weightsOpen weights
ReleasedApril 3, 2026August 9, 2026
Context window262K131K
Input $/Mtok$0.15$0.325
Output $/Mtok$0.60$1.35
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it10
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemma 4 26B A4B better than Muse Glimmer 30B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Gemma 4 26B A4B for output tokens 2.3x cheaper ($0.60 vs $1.35 per million) and a larger context window (262K against 131K tokens).

Which is cheaper, Gemma 4 26B A4B or Muse Glimmer 30B?

Gemma 4 26B A4B costs $0.15 per million input tokens and $0.60 per million output tokens; Muse Glimmer 30B costs $0.325 and $1.35. Cost per solved task is known for neither of them.

Which has the bigger context window, Gemma 4 26B A4B or Muse Glimmer 30B?

Gemma 4 26B A4B, with 262K tokens against 131K.

How many benchmarks is the Gemma 4 26B A4B vs Muse Glimmer 30B comparison based on?

4 benchmarks have a result for both models as of October 22, 2026. Gemma 4 26B A4B's coding index rests on 1 independent benchmark and Muse Glimmer 30B's on 0. Where only one model has a result, the page says so instead of naming a winner.

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