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BenchLM
Model A
Gemini 3.1 Pro

Google

56.0/100

Estimated · Public rank #95

90% interval 39.9–72.0

Gemini 3.1 Pro 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

Meta

70.6/100

Supported · Public rank #17

90% interval 61.0–80.2

Decision reading

Muse Spark has the higher public score estimate, 70.6 versus 55.96, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

19 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

    Gemini 3.1 Pro

    Gemini 3.1 Pro 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
19
Gemini 3.1 Pro only
4
Muse Spark only
5
Like-for-like categories
3 / 8

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.

Reasoning

Like-for-like
Gemini 3.1 Pro
77.1
Muse Spark
42.5
Weighted basis
1 vs 1 rows
Reading
Gemini 3.1 Pro leads

Math

Like-for-like
Gemini 3.1 Pro
31.8
Muse Spark
32.9
Weighted basis
2 vs 2 rows
Reading
Muse Spark leads

Multimodal

Like-for-like
Gemini 3.1 Pro
82.6
Muse Spark
82.5
Weighted basis
2 vs 2 rows
Reading
Gemini 3.1 Pro leads

Agentic

Not comparable
Gemini 3.1 Pro
Not measured
Muse Spark
59.0
Weighted basis
0 vs 1 rows
Reading
Not comparable

Coding

Not comparable
Gemini 3.1 Pro
Not measured
Muse Spark
67.8
Weighted basis
0 vs 2 rows
Reading
Not comparable

Knowledge

Not comparable
Gemini 3.1 Pro
Not measured
Muse Spark
50.4
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 3.1 Pro
Not measured
Muse Spark
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 3.1 Pro
Not measured
Muse Spark
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.

  • ARC-AGI-2

    Reasoning

    Gemini 3.1 Pro: 77.1%Muse Spark: 42.5%Normalized gap 34.6Gemini 3.1 Pro source Muse Spark source

  • CharXiv

    Multimodal

    Gemini 3.1 Pro: 80.2%Muse Spark: 86.4%Normalized gap 6.2Shared source

  • MMMU-Pro

    Multimodal

    Gemini 3.1 Pro: 83.9%Muse Spark: 80.4%Normalized gap 3.5Shared source

  • FrontierMath v2 (Tiers 1-3)

    Math

    Gemini 3.1 Pro: 36.900%Muse Spark: 39.000%Normalized gap 2.1Shared source

  • FrontierMath v2 (Tier 4)

    Math

    Gemini 3.1 Pro: 16.700%Muse Spark: 14.600%Normalized gap 2.1Shared 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

Gemini 3.1 Pro
$0.008
Fits in one request
Muse Spark
API rate not published
Fits in one request

Muse Spark has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemini 3.1 Pro
$0.136
Fits in one request
Muse Spark
API rate not published
Fits in one request

Muse Spark has no comparable published API token rate.

Cache-heavy agent loop

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

Gemini 3.1 Pro
$0.2
Fits in one request
Muse Spark
API rate not published
Fits in one request
Cached-input rate unavailable

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.

Cached-input rate

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

Gemini 3.1 Pro

$0.2 per 1M cached input tokens

Google Gemini API pricing

Muse Spark

No comparable hosted API rate

Reasoning profile

Gemini 3.1 Pro

Reasoning

Muse Spark

Reasoning

Weight access

Gemini 3.1 Pro

Proprietary

Muse Spark

Proprietary

License

Gemini 3.1 Pro

Proprietary

Muse Spark

Proprietary

Release date

Gemini 3.1 Pro

2026-02-19

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 55.96, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Gemini 3.1 Pro has the larger documented window (1M).

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 evidence28 rows

Agentic

  • Claw-Eval

    Shared source
    Gemini 3.1 Pro57.8%
    Muse Spark63.8%

    Muse Spark leads this result

  • DeepSearchQA

    Shared source
    Gemini 3.1 Pro69.7%
    Muse Spark74.8%

    Muse Spark leads this result

  • τ²-bench results

    Gemini 3.1 Pro95.6%
    Source
    Muse Spark91.5%
    Source

    Gemini 3.1 Pro leads this result

  • Gert Labs

    Gemini 3.1 Pro56.87%
    Source
    Muse Spark—

    Not directly comparable

  • ResearchClawBench

    Gemini 3.1 Pro13.3%
    Source
    Muse Spark—

    Not directly comparable

  • Terminal-Bench 2.0

    Gemini 3.1 Pro—
    Muse Spark59%
    Source

    Not directly comparable

  • CyberGym

    Gemini 3.1 Pro—
    Muse Spark43.5%
    Source

    Not directly comparable

Coding

  • LiveCodeBench Pro

    Shared source
    Gemini 3.1 Pro82.9%
    Muse Spark80.0%

    Gemini 3.1 Pro leads this result

  • React Native Evals

    Gemini 3.1 Pro78.9%
    Source
    Muse Spark—

    Not directly comparable

  • Vibe Code Bench

    Shared source
    Gemini 3.1 Pro32.03%
    Muse Spark19.67%

    Gemini 3.1 Pro leads this result

  • SWE-bench Verified

    Gemini 3.1 Pro—
    Muse Spark77.4%
    Source

    Not directly comparable

  • SWE-bench Pro

    Gemini 3.1 Pro—
    Muse Spark52.4%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    Gemini 3.1 Pro77.1%
    Source
    Muse Spark42.5%
    Source

    Gemini 3.1 Pro leads this result

  • ARC-AGI-3

    Gemini 3.1 Pro0.4%
    Source
    Muse Spark—

    Not directly comparable

Knowledge

  • GPQA-D

    Shared source
    Gemini 3.1 Pro94.3%
    Muse Spark89.5%

    Gemini 3.1 Pro leads this result

  • HLE w/o tools

    Shared source
    Gemini 3.1 Pro45.4%
    Muse Spark42.8%

    Gemini 3.1 Pro leads this result

  • HealthBench Hard

    Shared source
    Gemini 3.1 Pro20.6%
    Muse Spark42.8%

    Muse Spark leads this result

  • MedXpertQA (Text)

    Shared source
    Gemini 3.1 Pro71.5%
    Muse Spark52.6%

    Gemini 3.1 Pro leads this result

  • HLE

    Gemini 3.1 Pro—
    Muse Spark50.4%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    Gemini 3.1 Pro36.900%
    Muse Spark39.000%

    Muse Spark leads this result

  • FrontierMath v2 (Tier 4)

    Shared source
    Gemini 3.1 Pro16.700%
    Muse Spark14.600%

    Gemini 3.1 Pro leads this result

Multimodal

  • MMMU-Pro

    Shared source
    Gemini 3.1 Pro83.9%
    Muse Spark80.4%

    Gemini 3.1 Pro leads this result

  • CharXiv

    Shared source
    Gemini 3.1 Pro80.2%
    Muse Spark86.4%

    Muse Spark leads this result

  • ERQA

    Shared source
    Gemini 3.1 Pro69.4%
    Muse Spark64.7%

    Gemini 3.1 Pro leads this result

  • SimpleVQA

    Shared source
    Gemini 3.1 Pro72.4%
    Muse Spark71.3%

    Gemini 3.1 Pro leads this result

  • ScreenSpot Pro

    Shared source
    Gemini 3.1 Pro84.4%
    Muse Spark84.1%

    Gemini 3.1 Pro leads this result

  • ZeroBench

    Shared source
    Gemini 3.1 Pro29.0%
    Muse Spark33.0%

    Muse Spark leads this result

  • MedXpertQA (MM)

    Shared source
    Gemini 3.1 Pro81.3%
    Muse Spark78.4%

    Gemini 3.1 Pro leads this result

Frequently asked questions

Which is better, Gemini 3.1 Pro or Muse Spark?

Muse Spark has the higher public score estimate, 70.6 versus 55.96, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Gemini 3.1 Pro 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, Gemini 3.1 Pro 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, Gemini 3.1 Pro 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, Gemini 3.1 Pro or Muse Spark?

Gemini 3.1 Pro has the larger documented context window: 1M, compared with 262K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 21, 2026

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