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BenchLM
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Gemma 4 12B vs Muse Spark 1.1

Updated October 7, 2026. Rank says Muse Spark 1.1 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

Muse Spark 1.1 has the higher public point estimate, 65.95 versus 31.9. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Google logo
Gemma 4 12B

Google

31.9/100

Estimated · Public rank #159

Conditional range 17.6–46.3

Model B
Meta logo
Muse Spark 1.1

Meta

65.95/100

Supported · Public rank #31

90% interval 58.0–73.9

Shared results
1
Gemma 4 12B only
11
Muse Spark 1.1 only
25
Like-for-like categories
1 / 8
Estimated: Gemma 4 12B · Supported: Muse Spark 1.1. Conditional ranges do not establish rank confidence.How the comparison works

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 1.1

    Muse Spark 1.1 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

    Gemma 4 12B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    Gemma 4 12B is not ranked on the public lane for agentic, so no winner is named for agentic.

    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

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

23.3Gemma 4 12B54.2Muse Spark 1.1

Directional only · BenchAlign v5.8

Muse Spark 1.1 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboardShortlist models for this job
Same basis rules as the category table below

What is actually comparable

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

1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Knowledge

Like-for-like
Gemma 4 12B
32.5
Supported · #130/174
Muse Spark 1.1
66.7
Supported · #21/174
Basis
BenchAlign v5.8 lane · 5 vs 5 public rows
Reading
Muse Spark 1.1 leads

Coding

Directional only
Gemma 4 12B
23.3
Estimated · #112/146
Muse Spark 1.1
54.2
Supported · #35/146
Basis
BenchAlign v5.8 lane · 1 vs 4 public rows
Reading
Directional only

Agentic

Not comparable
Gemma 4 12B
Not ranked
Muse Spark 1.1
56.9
Supported · #32/122
Basis
BenchAlign v5.8 lane · 0 vs 14 public rows
Reading
Not comparable

Reasoning

Not comparable
Gemma 4 12B
34.9
Unranked · 4 rankable rows
Muse Spark 1.1
75.7
Unranked · 3 rankable rows
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Gemma 4 12B
27.2
#45/49
Muse Spark 1.1
78.3
Unranked · 2 rankable rows
Basis
Provisional lane · 1 vs 1 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Gemma 4 12B
Not ranked
Muse Spark 1.1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Gemma 4 12B
88.7
#21/125
Muse Spark 1.1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Gemma 4 12B
53.0
Unranked · 1 rankable row
Muse Spark 1.1
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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 12B
API rate not published
Fits in one request
Muse Spark 1.1
API rate not published
Fits in one request

Gemma 4 12B has no comparable published API token rate. Muse Spark 1.1 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemma 4 12B
API rate not published
Fits in one request
Muse Spark 1.1
API rate not published
Fits in one request

Gemma 4 12B has no comparable published API token rate. Muse Spark 1.1 has no comparable published API token rate.

Cache-heavy agent loop

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

Gemma 4 12B
API rate not published
Fits in one request
Cached-input rate unavailable
Muse Spark 1.1
API rate not published
Fits in one request
Cached-input rate unavailable

Gemma 4 12B has no comparable published API token rate. Muse Spark 1.1 has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

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

API model ID

Gemma 4 12B

Not sourced

Muse Spark 1.1

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 12B

No comparable hosted API rate

Muse Spark 1.1

No comparable hosted API rate

Reasoning profile

Gemma 4 12B

Reasoning

Muse Spark 1.1

Reasoning

Weight access

Gemma 4 12B

Open Weight

Muse Spark 1.1

Proprietary

License

Gemma 4 12B

Open Weight

Muse Spark 1.1

Proprietary

Release date

Gemma 4 12B

2026-06-03

Muse Spark 1.1

2026-07-09

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 1.1 has the higher public point estimate, 65.95 versus 31.9. Their conditional score ranges do not overlap. These ranges do not establish rank confidence.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Muse Spark 1.1 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Gemma 4 12B or Muse Spark 1.1?

Muse Spark 1.1 has the higher public point estimate, 65.95 versus 31.9. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Gemma 4 12B or Muse Spark 1.1?

Muse Spark 1.1 scores higher for coding on the public lane, 54.2 to 23.3. Gemma 4 12B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Gemma 4 12B or Muse Spark 1.1?

Gemma 4 12B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Gemma 4 12B or Muse Spark 1.1?

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 12B or Muse Spark 1.1?

Muse Spark 1.1 has the larger documented context window: 1M, compared with 256K.

Benchmark evidence

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

Browse raw public benchmark evidence37 rows

Agentic

  • Terminal-Bench 2.1

    Gemma 4 12B—
    Muse Spark 1.180.0%
    Source

    Not directly comparable

  • MCP Atlas

    Gemma 4 12B—
    Muse Spark 1.188.1%
    Source

    Not directly comparable

  • Toolathlon

    Gemma 4 12B—
    Muse Spark 1.175.6%
    Source

    Not directly comparable

  • OSWorld-Verified

    Gemma 4 12B—
    Muse Spark 1.180.8%
    Source

    Not directly comparable

  • WebArena-Verified

    Gemma 4 12B—
    Muse Spark 1.169%
    Source

    Not directly comparable

  • DeepSearchQA

    Gemma 4 12B—
    Muse Spark 1.184.9%
    Source

    Not directly comparable

  • CyberGym

    Gemma 4 12B—
    Muse Spark 1.159.0%
    Source

    Not directly comparable

  • Finance Agent v2

    Gemma 4 12B—
    Muse Spark 1.157.2%
    Source

    Not directly comparable

  • deepSwe

    Gemma 4 12B—
    Muse Spark 1.153.3%
    Source

    Not directly comparable

  • OSWorld 2.0

    Gemma 4 12B—
    Muse Spark 1.114.2%
    Source

    Not directly comparable

  • JobBench

    Gemma 4 12B—
    Muse Spark 1.154.7%
    Source

    Not directly comparable

  • Cybench

    Gemma 4 12B—
    Muse Spark 1.192.9%
    Source

    Not directly comparable

  • ExploitGym

    Gemma 4 12B—
    Muse Spark 1.10.8%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Gemma 4 12B—
    Muse Spark 1.169.3%
    Source

    Not directly comparable

Coding

  • LiveCodeBench v6

    Gemma 4 12B72.0%
    Source
    Muse Spark 1.1—

    Not directly comparable

  • Terminal-Bench 2.1

    Gemma 4 12B—
    Muse Spark 1.180.0%
    Source

    Not directly comparable

  • SWE-bench Pro

    Gemma 4 12B—
    Muse Spark 1.161.5%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    Gemma 4 12B—
    Muse Spark 1.185.9%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    Gemma 4 12B—
    Muse Spark 1.182.0%
    Source

    Not directly comparable

Reasoning

  • BBH

    Gemma 4 12B53%
    Source
    Muse Spark 1.1—

    Not directly comparable

  • MRCRv2

    Gemma 4 12B43.4%
    Source
    Muse Spark 1.1—

    Not directly comparable

  • MRCR 1M

    Gemma 4 12B—
    Muse Spark 1.154.1%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    Gemma 4 12B69.1%
    Source
    Muse Spark 1.1—

    Not directly comparable

  • MathVision

    Gemma 4 12B79.7%
    Source
    Muse Spark 1.1—

    Not directly comparable

  • MedXpertQA (MM)

    Gemma 4 12B48.7%
    Source
    Muse Spark 1.1—

    Not directly comparable

  • CharXiv

    Gemma 4 12B—
    Muse Spark 1.188.4%
    Source

    Not directly comparable

  • BabyVision

    Gemma 4 12B—
    Muse Spark 1.176.3%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Gemma 4 12B78.8%
    Source
    Muse Spark 1.1—

    Not directly comparable

  • GPQA-D

    Gemma 4 12B78.8%
    Source
    Muse Spark 1.1—

    Not directly comparable

  • MMLU-Pro

    Gemma 4 12B77.2%
    Source
    Muse Spark 1.1—

    Not directly comparable

  • HLE w/o tools

    Gemma 4 12B5.2%
    Source
    Muse Spark 1.152.2%
    Source

    Muse Spark 1.1 leads this result

  • MMMLU

    Gemma 4 12B83.4%
    Source
    Muse Spark 1.1—

    Not directly comparable

  • HLE

    Gemma 4 12B—
    Muse Spark 1.162.1%
    Source

    Not directly comparable

  • HealthBench Professional

    Gemma 4 12B—
    Muse Spark 1.159.3%
    Source

    Not directly comparable

  • GPQA Diamond (Vals)

    Gemma 4 12B—
    Muse Spark 1.191.2%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    Gemma 4 12B—
    Muse Spark 1.188.7%
    Source

    Not directly comparable

Math

  • AIME26

    Gemma 4 12B77.5%
    Source
    Muse Spark 1.1—

    Not directly comparable

37 public results · 1 shared

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Last updated October 7, 2026