AI model comparison

Muse Spark 1.1 vs Fugu

Compare pricing, context limits, supported inputs and deployment options to choose a model for your workflow.

Muse Spark 1.1 from Meta lists 1.05M context, 131.07K maximum output, from $1.25 input per 1M tokens, from $4.25 output per 1M tokens. Fugu from Sakana AI lists 1M context. Compare the inputs, outputs and deployment options below to see which fits your workflow.

Side-by-sideCapabilities and practical differences

Meta

Muse Spark 1.1

Muse Spark 1.1 is Meta’s multimodal reasoning model for tool-assisted coding and agent workflows, with Standard API pricing and a 1M-token context.

Context
1.05M
Max output
131.07K
Input / 1M tokens
From $1.25
Output / 1M tokens
From $4.25

Pricing conditions: Standard mode costs $1.25/M input, $0.15/M cached input and $4.25/M output, without a long-context premium. Version 1.1 has no Contributor variant and is not available in Muse Code. Search grounding costs $2.50 per 1,000 queries.

Accepts

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

Produces

  • Text output
VS

Sakana AI

Fugu

Fugu from Sakana AI: Multi-agent model for routing expert agents across complex analytical tasks.

Context
1M

Accepts

  • Text input
  • Image input

Produces

  • Text output

At a glance

Key differences

Compare the available figures below, then try both models on the work you need to do.

Muse Spark 1.1

  • Context capacity: 1.05M

Context capacity

Higher listed limit

Muse Spark 1.1:1.05M

Fugu:1M

Muse Spark 1.1

Specifications

Pricing, limits and capabilities

Specifications for Muse Spark 1.1 and Fugu. Higher limits and lower prices describe these figures only.
SpecificationMuse Spark 1.1Fugu
ProviderMetaSakana AI
Listed pricing routeMeta Model APISakana AI
Model familymusefugu
Accepted inputstext, image, video, audio, pdftext, image
Produced outputstexttext
Context window1.05MHigher1M
Max output131.07K—
Input price / 1MFrom $1.25—
Output price / 1MFrom $4.25—
Reasoning modeDocumentedDocumented
Tool callingDocumentedDocumented
Image inputDocumentedDocumented
Audio inputDocumentedNot documented
Structured outputDocumentedDocumented
LicenseProprietaryProprietary

Workload guidance

Which model should you choose?

Consider Muse Spark 1.1 if you need larger context. Test both on the same representative examples and judge the output against your acceptance criteria. Include tool failures, retries and the time to a completed task.

Documentation and model profiles

Explore each model’s features, API requirements and licence terms in the sources below.

Questions people ask

It brings together documented inputs and outputs, token limits, deployment terms and available pricing. When both models have source-linked, verified results on a shared benchmark, those results are shown with their individual sources. The comparison is a starting point for evaluating your own workload.

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