AI model comparison

Fugu vs Inkling

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

Fugu from Sakana AI lists 1M context. Inkling from Thinkingmachines lists 1.05M context, 1.05M maximum output, $1.00 input per 1M tokens, $4.05 output per 1M tokens. Compare the inputs, outputs and deployment options below to see which fits your workflow.

Side-by-sideCapabilities and practical differences

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
VS

Thinkingmachines

Inkling

Inkling from Thinkingmachines: Multimodal MoE reasoning model (975B total, 41B active) for text, image, and audio.

Context
1.05M
Max output
1.05M
Input / 1M tokens
$1.00
Output / 1M tokens
$4.05

Accepts

  • Text input
  • Image input
  • Audio 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.

Inkling

  • Context capacity: 1.05M

Context capacity

Higher listed limit

Fugu:1M

Inkling:1.05M

Inkling

Specifications

Pricing, limits and capabilities

Specifications for Fugu and Inkling. Higher limits and lower prices describe these figures only.
SpecificationFuguInkling
ProviderSakana AIThinkingmachines
Listed pricing routeSakana AIThinkingmachines
Model familyfuguling
Accepted inputstext, imagetext, image, audio
Produced outputstexttext
Context window1M1.05MHigher
Max output—1.05M
Input price / 1M—$1.00
Output price / 1M—$4.05
Reasoning modeDocumentedDocumented
Tool callingDocumentedDocumented
Image inputDocumentedDocumented
Audio inputNot documentedDocumented
Structured outputDocumentedNot documented
LicenseProprietaryApache-2.0

Workload guidance

Which model should you choose?

Consider Inkling if you need larger context. Inkling offers downloadable weights; Fugu is listed as a proprietary model. Compare licence terms and operating costs before choosing a deployment. 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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