SSyncDev

AI model comparison

Muse Spark 1.1 vs Fugu Ultra

Compare recorded price, context, output limits, speed, latency, capabilities, and input/output support. Every missing value stays visible, and unsourced benchmarks are excluded.

Muse Spark 1.1 and Fugu Ultra both come up when teams are choosing a model for production work, and the honest answer usually depends on your workload rather than a leaderboard. This page puts their published specifications side by side — context window, token pricing, supported inputs and outputs — so you can see where they actually differ.

Side-by-sideReviewed by our team

Meta

Muse Spark 1.1

Muse Spark is a natively multimodal reasoning model with support for tool-use, visual chain of thought, and multi-agent orchestration.

Context
1M
Max output
32K
Input / 1M
$1.25
Output / 1M
$4.25
Output speed
—

Accepts

  • Text input
  • Image input
  • PDF input
  • Video input

Produces

  • Text output
Full model profileVendor docs ↗
VS

Sakana AI

Fugu Ultra

Quality-first multi-agent model for hard research, analysis, and competitions

Context
1M
Max output
1M
Input / 1M
$5.00
Output / 1M
$30.00
Output speed
—

Accepts

  • Text input
  • Image input

Produces

  • Text output
Full model profileVendor docs ↗

At a glance

Decision snapshot

This is a directional summary of the values recorded in this directory—not a universal quality verdict. Test the finalists on your own prompts before committing.

Where Muse Spark 1.1 leads in recorded data

  • Input price: $1.25
  • Output price: $4.25

Where Fugu Ultra leads in recorded data

  • Maximum output: 1M

Context capacity

Higher listed limit

Muse Spark 1.1:1M

Fugu Ultra:1M

Same recorded value

Maximum output

Higher listed limit

Muse Spark 1.1:32K

Fugu Ultra:1M

Fugu Ultra

Input price

Lower recorded price

Muse Spark 1.1:$1.25

Fugu Ultra:$5.00

Muse Spark 1.1

Output price

Lower recorded price

Muse Spark 1.1:$4.25

Fugu Ultra:$30.00

Muse Spark 1.1

Output speed

Higher recorded throughput

Muse Spark 1.1:—

Fugu Ultra:—

No complete comparison

Time to first token

Lower recorded latency

Muse Spark 1.1:—

Fugu Ultra:—

No complete comparison

Intelligence Index

Higher directory index

Muse Spark 1.1:—

Fugu Ultra:—

No complete comparison

Evidence

Verified shared benchmarks

A score appears only when both records include a verified source. It is not treated as a universal ranking.

These two models haven't been measured on the same benchmark yet, so there's nothing meaningful to chart. Comparing scores from different tests would tell you more about the tests than the models — the specs and pricing above are the better guide here.

Specifications

Pricing, limits and capabilities

SpecificationMuse Spark 1.1Fugu Ultra
ProviderMetaSakana AI
Model familymusefugu
Accepted inputstext, image, pdf, videotext, image
Produced outputstexttext
Context window1M1M
Max output32K1MRecorded lead
Intelligence Index——
Input price / 1M$1.25Recorded lead$5.00
Output price / 1M$4.25Recorded lead$30.00
Output speed——
Time to first token——
Reasoning modeDocumentedDocumented
Tool callingDocumentedDocumented
Image inputDocumentedDocumented
Audio inputNot documentedNot documented
Structured outputDocumentedDocumented
LicenseProprietaryProprietary
ReleasedApr 8, 2026Jun 15, 2026

Workload guidance

Which model should you choose?

On the published numbers, Fugu Ultra accepts the larger context window and Muse Spark 1.1 is cheaper per input token. Which matters more depends on whether your bottleneck is document size or spend — the table above has the exact figures.

Provenance

Sources and data limits

Prices and hosted performance can change. SyncDev records source references and clearly separates documented specifications from measured values; confirm commercial terms with the provider before deployment.

Questions people ask

It compares recorded context and output limits, token prices, output speed, time to first token, Intelligence Index, modality support, tool and structured-output capabilities, release information, and only source-linked benchmarks verified on both records. Missing information is labeled rather than inferred.

Compare a different pair

Choose any two approved model records and review them with the same evidence rules.

Open model comparison