AI model comparison

Qwen3.6 35B-A3B vs Fugu

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

Qwen3.6 35B-A3B from Alibaba lists 262.14K context, 65.54K maximum output, $0.248 input per 1M tokens, $1.485 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

Alibaba

Qwen3.6 35B-A3B

Qwen3.6 35B-A3B from Alibaba: Open multimodal Qwen MoE for local agents that need vision, audio, and code.

Context
262.14K
Max output
65.54K
Input / 1M tokens
$0.248
Output / 1M tokens
$1.485

Accepts

  • Text input
  • Image input
  • Video input
  • Audio 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

Qwen3.6 35B-A3B:262.14K

Fugu:1M

Fugu

Specifications

Pricing, limits and capabilities

Specifications for Qwen3.6 35B-A3B and Fugu. Higher limits and lower prices describe these figures only.
SpecificationQwen3.6 35B-A3BFugu
ProviderAlibabaSakana AI
Listed pricing routeAlibabaSakana AI
Model familyqwenfugu
Accepted inputstext, image, video, audiotext, image
Produced outputstexttext
Context window262.14K1MHigher
Max output65.54K—
Input price / 1M$0.248—
Output price / 1M$1.485—
Reasoning modeDocumentedDocumented
Tool callingDocumentedDocumented
Image inputDocumentedDocumented
Audio inputDocumentedNot documented
Structured outputDocumentedDocumented
LicenseOpen weightsProprietary

Workload guidance

Which model should you choose?

Consider Fugu if you need larger context. Qwen3.6 35B-A3B 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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