Model A

Google

57.0/100

Supported · Public rank #88

90% interval 39.3–74.8

Gemma 4 26B A4B vs Muse Spark

Updated August 21, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

Muse Spark

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

Long documents
Prompts that approach the documented context limit
Muse Spark
Muse Spark has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

1 Gemma 4 26B A4B only

3 Shared

21 Muse Spark only

Shared results: 3
Gemma 4 26B A4B only: 1
Muse Spark only: 21
Like-for-like categories: 0 / 8

2 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Knowledge

Directional only

Gemma 4 26B A4B: 43.4
Muse Spark: 50.4
Weighted basis: 2 vs 1 rows
Reading: Directional only

Multimodal

Directional only

Gemma 4 26B A4B: 73.8
Muse Spark: 82.5
Weighted basis: 1 vs 2 rows
Reading: Directional only

Agentic

Not comparable

Gemma 4 26B A4B: Not measured
Muse Spark: 59.0
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Coding

Not comparable

Gemma 4 26B A4B: Not measured
Muse Spark: 67.8
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Reasoning

Not comparable

Gemma 4 26B A4B: Not measured
Muse Spark: 42.5
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Math

Not comparable

Gemma 4 26B A4B: Not measured
Muse Spark: 32.9
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Multilingual

Not comparable

Gemma 4 26B A4B: Not measured
Muse Spark: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Instruction following

Not comparable

Gemma 4 26B A4B: Not measured
Muse Spark: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Category averages with the server-provided evidence basis for Gemma 4 26B A4B and Muse Spark
Category	Gemma 4 26B A4B	Muse Spark	Weighted basis	Reading
Knowledge	43.4	50.4	Directional only2 vs 1 rows	Directional only
Multimodal	73.8	82.5	Directional only1 vs 2 rows	Directional only
Agentic	Not measured	59.0	Not comparable0 vs 1 rows	Not comparable
Coding	Not measured	67.8	Not comparable0 vs 2 rows	Not comparable
Reasoning	Not measured	42.5	Not comparable0 vs 1 rows	Not comparable
Math	Not measured	32.9	Not comparable0 vs 2 rows	Not comparable
Multilingual	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Instruction following	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

HLE
Knowledge
Gemma 4 26B A4B: 17.2%Muse Spark: 50.4%Normalized gap 33.2Gemma 4 26B A4B source Muse Spark source
MMMU-Pro
Multimodal
Gemma 4 26B A4B: 73.8%Muse Spark: 80.4%Normalized gap 6.6Gemma 4 26B A4B source Muse Spark source

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

Gemma 4 26B A4B: Self-hosted; infrastructure cost varies; Fits in one request
Muse Spark: API rate not published; Fits in one request

Gemma 4 26B A4B has no comparable published API token rate. Muse Spark has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemma 4 26B A4B: Self-hosted; infrastructure cost varies; Fits in one request
Muse Spark: API rate not published; Fits in one request

Gemma 4 26B A4B has no comparable published API token rate. Muse Spark has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Gemma 4 26B A4B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable
Muse Spark: API rate not published; Fits in one request; Cached-input rate unavailable

Gemma 4 26B A4B has no comparable published API token rate. Muse Spark has no comparable published API token rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationGemma 4 26B A4BMuse Spark

Context window

Maximum documented context; output-token limits may be lower.

Gemma 4 26B A4B

256K

Google Gemma 4 model documentation

Muse Spark

262K

API model ID

Gemma 4 26B A4B

gemma-4-26b-a4b-it

Google Gemma Gemini API guide

Muse Spark

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Gemma 4 26B A4B

No comparable hosted API rate

Muse Spark

No comparable hosted API rate

Documented inputs

Gemma 4 26B A4B

text, image

Google Gemma 4 model documentation

Muse Spark

Not sourced

Documented outputs

Gemma 4 26B A4B

text

Google Gemma 4 model documentation

Muse Spark

Not sourced

Provider availability

Gemma 4 26B A4B

Generally Available · Gemini API, Google AI Studio, open weights

Google Gemma Gemini API guide

Muse Spark

Not sourced

Reasoning profile

Gemma 4 26B A4B

Reasoning

Muse Spark

Reasoning

Weight access

Gemma 4 26B A4B

Open Weight

Muse Spark

Proprietary

License

Gemma 4 26B A4B

Open Weight

Muse Spark

Proprietary

Release date

Gemma 4 26B A4B

2026-04-02

Muse Spark

2026-04-08

If you already use one of these models

Deployment change: The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal: Muse Spark has the higher public score estimate, 70.6 versus 57.04, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Muse Spark has the larger documented window (262K).

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence25 rows

Agentic

Terminal-Bench 2.0
Gemma 4 26B A4B—
Muse Spark59%
Source
Not directly comparable
τ²-bench results
Gemma 4 26B A4B—
Muse Spark91.5%
Source
Not directly comparable
DeepSearchQA
Gemma 4 26B A4B—
Muse Spark74.8%
Source
Not directly comparable
CyberGym
Gemma 4 26B A4B—
Muse Spark43.5%
Source
Not directly comparable
Claw-Eval
Gemma 4 26B A4B—
Muse Spark63.8%
Source
Not directly comparable

Coding

SWE-bench Verified
Gemma 4 26B A4B—
Muse Spark77.4%
Source
Not directly comparable
SWE-bench Pro
Gemma 4 26B A4B—
Muse Spark52.4%
Source
Not directly comparable
LiveCodeBench Pro
Gemma 4 26B A4B—
Muse Spark80.0%
Source
Not directly comparable
Vibe Code Bench
Gemma 4 26B A4B—
Muse Spark19.67%
Source
Not directly comparable

Reasoning

ARC-AGI-2
Gemma 4 26B A4B—
Muse Spark42.5%
Source
Not directly comparable

Knowledge

MMLU-Pro
Gemma 4 26B A4B82.6%
Source
Muse Spark—
Not directly comparable
HLE
Gemma 4 26B A4B17.2%
Source
Muse Spark50.4%
Source
Muse Spark leads this result
HLE w/o tools
Gemma 4 26B A4B8.7%
Source
Muse Spark42.8%
Source
Muse Spark leads this result
GPQA-D
Gemma 4 26B A4B—
Muse Spark89.5%
Source
Not directly comparable
HealthBench Hard
Gemma 4 26B A4B—
Muse Spark42.8%
Source
Not directly comparable
MedXpertQA (Text)
Gemma 4 26B A4B—
Muse Spark52.6%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
Gemma 4 26B A4B—
Muse Spark39.000%
Source
Not directly comparable
FrontierMath v2 (Tier 4)
Gemma 4 26B A4B—
Muse Spark14.600%
Source
Not directly comparable

Multimodal

MMMU-Pro
Gemma 4 26B A4B73.8%
Source
Muse Spark80.4%
Source
Muse Spark leads this result
CharXiv
Gemma 4 26B A4B—
Muse Spark86.4%
Source
Not directly comparable
ERQA
Gemma 4 26B A4B—
Muse Spark64.7%
Source
Not directly comparable
SimpleVQA
Gemma 4 26B A4B—
Muse Spark71.3%
Source
Not directly comparable
ScreenSpot Pro
Gemma 4 26B A4B—
Muse Spark84.1%
Source
Not directly comparable
ZeroBench
Gemma 4 26B A4B—
Muse Spark33.0%
Source
Not directly comparable
MedXpertQA (MM)
Gemma 4 26B A4B—
Muse Spark78.4%
Source
Not directly comparable

Frequently asked questions

Which is better, Gemma 4 26B A4B or Muse Spark?

Muse Spark has the higher public score estimate, 70.6 versus 57.04, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Gemma 4 26B A4B or Muse Spark?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, Gemma 4 26B A4B or Muse Spark?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, Gemma 4 26B A4B or Muse Spark?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, Gemma 4 26B A4B or Muse Spark?

Muse Spark has the larger documented context window: 262K, compared with 256K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 21, 2026

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Gemma 4 26B A4B vs Muse Spark

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Knowledge

Multimodal

Agentic

Coding

Reasoning

Math

Multilingual

Instruction following

Shape of the matched evidence

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Math

Multimodal

Frequently asked questions

Which is better, Gemma 4 26B A4B or Muse Spark?

Which is better for coding, Gemma 4 26B A4B or Muse Spark?

Which is better for agentic tasks, Gemma 4 26B A4B or Muse Spark?

Which costs less, Gemma 4 26B A4B or Muse Spark?

Which has the larger context window, Gemma 4 26B A4B or Muse Spark?

Related comparisons

Watch Gemma 4 26B A4B vs Muse Spark