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Model comparison

Muse Glimmer 30B vs Sakana Fugu-Ultra v1.1

Updated August 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Muse Glimmer 30B

Meta

—

Evidence status unavailable

90% interval unavailable

Sakana Fugu-Ultra v1.1

Sakana AI

—

Evidence status unavailable

90% interval unavailable

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 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

    Sakana Fugu-Ultra v1.1

    Sakana Fugu-Ultra v1.1 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

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Muse Glimmer 30B does not fit this workload in one request. Muse Glimmer 30B has no comparable published API token rate.

    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
0
Muse Glimmer 30B only
14
Sakana Fugu-Ultra v1.1 only
0
Like-for-like categories
0 / 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.

Agentic

Not comparable
Muse Glimmer 30B
65.9
Sakana Fugu-Ultra v1.1
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Muse Glimmer 30B
57.8
Sakana Fugu-Ultra v1.1
Not measured
Weighted basis
3 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Muse Glimmer 30B
Not measured
Sakana Fugu-Ultra v1.1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Muse Glimmer 30B
Not measured
Sakana Fugu-Ultra v1.1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Muse Glimmer 30B
94.7
Sakana Fugu-Ultra v1.1
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Muse Glimmer 30B
Not measured
Sakana Fugu-Ultra v1.1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Muse Glimmer 30B
75.7
Sakana Fugu-Ultra v1.1
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Muse Glimmer 30B
77.0
Sakana Fugu-Ultra v1.1
Not measured
Weighted basis
1 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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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

Muse Glimmer 30B
Self-hosted; infrastructure cost varies
Fits in one request
Sakana Fugu-Ultra v1.1
$0.02
Fits in one request

Muse Glimmer 30B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Muse Glimmer 30B
Self-hosted; infrastructure cost varies
Fits in one request
Sakana Fugu-Ultra v1.1
$0.34
Fits in one request

Muse Glimmer 30B has no comparable published API token rate.

Cache-heavy agent loop

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

Muse Glimmer 30B
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable
Sakana Fugu-Ultra v1.1
$0.5
Fits in one request

Muse Glimmer 30B does not fit this workload in one request. Muse Glimmer 30B 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.

Context window

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

Muse Glimmer 30B

131K

Sakana Fugu-Ultra v1.1

1M

API model ID

Muse Glimmer 30B

Not sourced

Sakana Fugu-Ultra v1.1

Not sourced

Cached-input rate

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

Muse Glimmer 30B

No comparable hosted API rate

Sakana Fugu-Ultra v1.1

$0.5 per 1M cached input tokens

Documented inputs

Muse Glimmer 30B

Not sourced

Sakana Fugu-Ultra v1.1

Not sourced

Documented outputs

Muse Glimmer 30B

Not sourced

Sakana Fugu-Ultra v1.1

Not sourced

Provider availability

Muse Glimmer 30B

Not sourced

Sakana Fugu-Ultra v1.1

Not sourced

Reasoning profile

Muse Glimmer 30B

Reasoning

Sakana Fugu-Ultra v1.1

Reasoning

Weight access

Muse Glimmer 30B

Open Weight

Sakana Fugu-Ultra v1.1

Proprietary

License

Muse Glimmer 30B

Open Weight

Sakana Fugu-Ultra v1.1

Proprietary

Release date

Muse Glimmer 30B

2026-08-10

Sakana Fugu-Ultra v1.1

2026-07-24

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Sakana Fugu-Ultra v1.1 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 evidence14 rows

Agentic

  • MCP Atlas

    Muse Glimmer 30B75.5%
    Source
    Sakana Fugu-Ultra v1.1—

    Not directly comparable

  • DeepSearchQA

    Muse Glimmer 30B74.6%
    Source
    Sakana Fugu-Ultra v1.1—

    Not directly comparable

  • skillsBench

    Muse Glimmer 30B44.3%
    Source
    Sakana Fugu-Ultra v1.1—

    Not directly comparable

  • OSWorld-Verified

    Muse Glimmer 30B65.9%
    Source
    Sakana Fugu-Ultra v1.1—

    Not directly comparable

Coding

  • SWE-bench Pro

    Muse Glimmer 30B51.2%
    Source
    Sakana Fugu-Ultra v1.1—

    Not directly comparable

  • SWE-bench Verified

    Muse Glimmer 30B76%
    Source
    Sakana Fugu-Ultra v1.1—

    Not directly comparable

  • Terminal-Bench 2.1

    Muse Glimmer 30B51.7%
    Source
    Sakana Fugu-Ultra v1.1—

    Not directly comparable

  • SciCode

    Muse Glimmer 30B43.6%
    Source
    Sakana Fugu-Ultra v1.1—

    Not directly comparable

Math

  • AIME26

    Muse Glimmer 30B94.7%
    Source
    Sakana Fugu-Ultra v1.1—

    Not directly comparable

Multimodal

  • CharXiv

    Muse Glimmer 30B78.8%
    Source
    Sakana Fugu-Ultra v1.1—

    Not directly comparable

  • ScreenSpot Pro

    Muse Glimmer 30B75.4%
    Source
    Sakana Fugu-Ultra v1.1—

    Not directly comparable

  • OmniDocBench 1.5

    Muse Glimmer 30B75.8%
    Source
    Sakana Fugu-Ultra v1.1—

    Not directly comparable

  • MMMU-Pro

    Muse Glimmer 30B74%
    Source
    Sakana Fugu-Ultra v1.1—

    Not directly comparable

Instruction following

  • IFBench

    Muse Glimmer 30B77%
    Source
    Sakana Fugu-Ultra v1.1—

    Not directly comparable

Frequently asked questions

Which is better, Muse Glimmer 30B or Sakana Fugu-Ultra v1.1?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Muse Glimmer 30B or Sakana Fugu-Ultra v1.1?

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, Muse Glimmer 30B or Sakana Fugu-Ultra v1.1?

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, Muse Glimmer 30B or Sakana Fugu-Ultra v1.1?

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, Muse Glimmer 30B or Sakana Fugu-Ultra v1.1?

Sakana Fugu-Ultra v1.1 has the larger documented context window: 1M, compared with 131K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 10, 2026

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