AI model comparison

MAI-Code-1-Flash vs Fugu Ultra

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

MAI-Code-1-Flash from Microsoft lists 256K context, 128K maximum output. Fugu Ultra from Sakana AI lists 1M context, $5.00 input per 1M tokens, $30.00 output per 1M tokens. Compare the inputs, outputs and deployment options below to see which fits your workflow.

Side-by-sideCapabilities and practical differences

Microsoft

MAI-Code-1-Flash

MAI-Code-1-Flash from Microsoft: Microsoft coding model built for fast, efficient assistance in everyday developer workflows.

Context
256K
Max output
128K

Accepts

  • Text input

Produces

  • Text output
VS

Sakana AI

Fugu Ultra

Fugu Ultra from Sakana AI: Quality-first multi-agent model for hard research, analysis, and competitions.

Context
1M
Input / 1M tokens
$5.00
Output / 1M tokens
$30.00

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 Ultra

  • Context capacity: 1M

Context capacity

Higher listed limit

MAI-Code-1-Flash:256K

Fugu Ultra:1M

Fugu Ultra

Specifications

Pricing, limits and capabilities

Specifications for MAI-Code-1-Flash and Fugu Ultra. Higher limits and lower prices describe these figures only.
SpecificationMAI-Code-1-FlashFugu Ultra
ProviderMicrosoftSakana AI
Listed pricing routeMicrosoftSakana AI
Model familymaifugu
Accepted inputstexttext, image
Produced outputstexttext
Context window256K1MHigher
Max output128K—
Input price / 1M—$5.00
Output price / 1M—$30.00
Reasoning modeDocumentedDocumented
Tool callingDocumentedDocumented
Image inputNot documentedDocumented
Audio inputNot documentedNot documented
Structured outputDocumentedDocumented
LicenseProprietaryProprietary

Workload guidance

Which model should you choose?

Consider Fugu Ultra 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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