AI model comparison

North Mini Code vs Fugu

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

North Mini Code from Cohere lists 256K context, 64K maximum output, $0.00 input per 1M tokens, $0.00 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

Cohere

North Mini Code

North Mini Code from Cohere: Cohere coding model for practical software engineering and agentic edits.

Context
256K
Max output
64K
Input / 1M tokens
$0.00
Output / 1M tokens
$0.00

Accepts

  • Text 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.

Fugu

  • Context capacity: 1M

Context capacity

Higher listed limit

North Mini Code:256K

Fugu:1M

Fugu

Specifications

Pricing, limits and capabilities

Specifications for North Mini Code and Fugu. Higher limits and lower prices describe these figures only.
SpecificationNorth Mini CodeFugu
ProviderCohereSakana AI
Listed pricing routeCohereSakana AI
Model familynorthfugu
Accepted inputstexttext, image
Produced outputstexttext
Context window256K1MHigher
Max output64K—
Input price / 1M$0.00—
Output price / 1M$0.00—
Reasoning modeDocumentedDocumented
Tool callingDocumentedDocumented
Image inputNot documentedDocumented
Audio inputNot documentedNot documented
Structured outputDocumentedDocumented
LicenseOpen weightsProprietary

Workload guidance

Which model should you choose?

Consider Fugu if you need larger context. North Mini Code 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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