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BenchLM
Model A
Gemma 4 31B

Google

60.0/100

Supported · Public rank #64

90% interval 43.6–76.4

Gemma 4 31B vs Muse Spark 1.1

Updated August 22, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Muse Spark 1.1

Meta

76.7/100

Supported · Public rank #7

90% interval 72.6–80.9

Decision reading

Muse Spark 1.1 has the higher public score estimate, 76.74 versus 59.99, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

2 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

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

    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.

Shared results
2
Gemma 4 31B only
6
Muse Spark 1.1 only
19
Like-for-like categories
0 / 8

1 category uses 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 31B
52.9
Muse Spark 1.1
62.1
Weighted basis
3 vs 1 rows
Reading
Directional only

Agentic

Not comparable
Gemma 4 31B
Not measured
Muse Spark 1.1
80.4
Weighted basis
0 vs 2 rows
Reading
Not comparable

Coding

Not comparable
Gemma 4 31B
41.6
Muse Spark 1.1
61.5
Weighted basis
1 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
Gemma 4 31B
Not measured
Muse Spark 1.1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Gemma 4 31B
Not measured
Muse Spark 1.1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Gemma 4 31B
Not measured
Muse Spark 1.1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Gemma 4 31B
76.9
Muse Spark 1.1
88.4
Weighted basis
1 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
Gemma 4 31B
Not measured
Muse Spark 1.1
Not measured
Weighted basis
0 vs 0 rows
Reading
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.

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 31B
Self-hosted; infrastructure cost varies
Fits in one request
Muse Spark 1.1
API rate not published
Fits in one request

Gemma 4 31B 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 31B
Self-hosted; infrastructure cost varies
Fits in one request
Muse Spark 1.1
API rate not published
Fits in one request

Gemma 4 31B 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 31B
Self-hosted; infrastructure cost varies
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 31B has no comparable published API token rate. Muse Spark 1.1 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.

Cached-input rate

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

Gemma 4 31B

No comparable hosted API rate

Muse Spark 1.1

No comparable hosted API rate

Provider availability

Gemma 4 31B

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

Google Gemma Gemini API guide

Muse Spark 1.1

Not sourced

Reasoning profile

Gemma 4 31B

Reasoning

Muse Spark 1.1

Reasoning

Weight access

Gemma 4 31B

Open Weight

Muse Spark 1.1

Proprietary

License

Gemma 4 31B

Open Weight

Muse Spark 1.1

Proprietary

Release date

Gemma 4 31B

2026-04-02

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 score estimate, 76.74 versus 59.99, but the 90% score intervals overlap.
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.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Gemma 4 31B
API / mo$0
Self-host / mo$429
Break-even—
Muse Spark 1.1
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Model the full break-even

Benchmark evidence

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

Browse raw public benchmark evidence27 rows

Agentic

  • Gert Labs

    Gemma 4 31B35.26%
    Source
    Muse Spark 1.1—

    Not directly comparable

  • Terminal-Bench 2.0

    Gemma 4 31B—
    Muse Spark 1.180%
    Source

    Not directly comparable

  • MCP Atlas

    Gemma 4 31B—
    Muse Spark 1.188.1%
    Source

    Not directly comparable

  • Toolathlon

    Gemma 4 31B—
    Muse Spark 1.175.6%
    Source

    Not directly comparable

  • OSWorld-Verified

    Gemma 4 31B—
    Muse Spark 1.180.8%
    Source

    Not directly comparable

  • WebArena-Verified

    Gemma 4 31B—
    Muse Spark 1.169%
    Source

    Not directly comparable

  • DeepSearchQA

    Gemma 4 31B—
    Muse Spark 1.184.9%
    Source

    Not directly comparable

  • CyberGym

    Gemma 4 31B—
    Muse Spark 1.159.0%
    Source

    Not directly comparable

  • Finance Agent v2

    Gemma 4 31B—
    Muse Spark 1.157.2%
    Source

    Not directly comparable

  • deepSwe

    Gemma 4 31B—
    Muse Spark 1.153.3%
    Source

    Not directly comparable

  • OSWorld 2.0

    Gemma 4 31B—
    Muse Spark 1.114.2%
    Source

    Not directly comparable

  • JobBench

    Gemma 4 31B—
    Muse Spark 1.154.7%
    Source

    Not directly comparable

  • Cybench

    Gemma 4 31B—
    Muse Spark 1.192.9%
    Source

    Not directly comparable

  • ExploitGym

    Gemma 4 31B—
    Muse Spark 1.10.8%
    Source

    Not directly comparable

Coding

  • SWE-Rebench

    Gemma 4 31B41.6%
    Source
    Muse Spark 1.1—

    Not directly comparable

  • React Native Evals

    Gemma 4 31B75.2%
    Source
    Muse Spark 1.1—

    Not directly comparable

  • Terminal-Bench 2.0

    Gemma 4 31B—
    Muse Spark 1.180.0%
    Source

    Not directly comparable

  • SWE-bench Pro

    Gemma 4 31B—
    Muse Spark 1.161.5%
    Source

    Not directly comparable

Reasoning

  • MRCR 1M

    Gemma 4 31B—
    Muse Spark 1.154.1%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Gemma 4 31B84.3%
    Source
    Muse Spark 1.1—

    Not directly comparable

  • MMLU-Pro

    Gemma 4 31B85.2%
    Source
    Muse Spark 1.1—

    Not directly comparable

  • HLE

    Gemma 4 31B26.5%
    Source
    Muse Spark 1.162.1%
    Source

    Muse Spark 1.1 leads this result

  • HLE w/o tools

    Gemma 4 31B19.5%
    Source
    Muse Spark 1.152.2%
    Source

    Muse Spark 1.1 leads this result

  • HealthBench Professional

    Gemma 4 31B—
    Muse Spark 1.159.3%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    Gemma 4 31B76.9%
    Source
    Muse Spark 1.1—

    Not directly comparable

  • CharXiv

    Gemma 4 31B—
    Muse Spark 1.188.4%
    Source

    Not directly comparable

  • BabyVision

    Gemma 4 31B—
    Muse Spark 1.176.3%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Gemma 4 31B or Muse Spark 1.1?

Muse Spark 1.1 has the higher public score estimate, 76.74 versus 59.99, 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 31B or Muse Spark 1.1?

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 31B or Muse Spark 1.1?

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 31B 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 31B or Muse Spark 1.1?

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

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 22, 2026

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