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AI model comparison

Gemini 1.5 Pro vs Fugu

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

Gemini 1.5 Pro from Google lists 2M context, 8.19K maximum output, $1.25 input per 1M tokens, $5.00 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

Google

Gemini 1.5 Pro

Google's long-context multimodal model with up to 2M token windows.

Context
2M
Max output
8.19K
Input / 1M tokens
$1.25
Output / 1M tokens
$5.00
Output speed
65 t/s

Accepts

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

Produces

  • Text output
Full model profileai.google.dev ↗
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
Full model profilegithub.com ↗

At a glance

Key differences

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

Gemini 1.5 Pro

  • Context capacity: 2M

Context capacity

Higher listed limit

Gemini 1.5 Pro:2M

Fugu:1M

Gemini 1.5 Pro

Specifications

Pricing, limits and capabilities

Specifications for Gemini 1.5 Pro and Fugu. Higher limits and lower prices describe these figures only.
SpecificationGemini 1.5 ProFugu
ProviderGoogleSakana AI
Listed pricing routeGoogleSakana AI
Model familyGemini 1.5fugu
Accepted inputstext, image, audio, videotext, image
Produced outputstexttext
Context window2MHigher1M
Max output8.19K—
Input price / 1M$1.25—
Output price / 1M$5.00—
Output speed65 t/s—
Time to first token0.90s—
Reasoning modeNot documentedDocumented
Tool callingDocumentedDocumented
Image inputDocumentedDocumented
Audio inputDocumentedNot documented
Structured outputDocumentedDocumented
LicenseProprietaryProprietary

Workload guidance

Which model should you choose?

Consider Gemini 1.5 Pro 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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