Fugu Ultra V2
Model Overview
Fugu Ultra v2 is the higher-performance model in Sakana AI's Fugu family and the successor to Fugu Ultra. Rather than a standard monolithic language model, it is a learned multi-agent orchestration system: a model trained to route tasks across a swappable pool of underlying models and to recursively call instances of itself. It is optimized for complex, multi-step reasoning, coding, and agentic workflows, with orchestration tokens billed as standard input/output tokens. It adds document (file) input alongside text and images, and reasoning is always on.
API Schema
Provider routing override. Use a source key such as openai, fallback, xai, google, alibaba, minimax, moonshot, baidu, or togetherai to pin that one provider and use it alone; auto (default) walks the whole chain. Case-insensitive.
autoThe maximum number of tokens that can be generated in the chat completion. This value can be used to control costs for text generated via API.
If set to True, the model response data will be streamed to the client as it is generated using server-sent events.
falseControls which (if any) tool is called by the model. none means the model will not call any tool and instead generates a message. auto means the model can pick between generating a message or calling one or more tools. required means the model must call one or more tools. Specifying a particular tool via {"type": "function", "function": {"name": "my_function"}} forces the model to call that tool. none is the default when no tools are present. auto is the default if tools are present.
none means the model will not call any tool and instead generates a message. auto means the model can pick between generating a message or calling one or more tools. required means the model must call one or more tools.
Enable provider compatibility normalization for tool function JSON schemas.
An object specifying the format that the model must output.
Constrains effort on reasoning for reasoning models. Currently supported values are low, medium, and high. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response.
Successful response.
A unique identifier for the chat completion.
chatcmpl-CQ9FPg3osank0dx0k46Z53LTqtXMlThe object type.
chat.completionPossible values: The Unix timestamp (in seconds) of when the chat completion was created.
1762343744The model used for the chat completion.
sakana/fugu-ultra-v2curl --request POST \
--url 'https://api.aimlapi.com/v1/chat/completions' \
--header 'Authorization: Bearer <YOUR_AIMLAPI_KEY>' \
--header 'Content-Type: application/json' \
--data '{"model": "sakana/fugu-ultra-v2", "messages": ["<message>"]}'Successful response.
{
"id": "chatcmpl-CQ9FPg3osank0dx0k46Z53LTqtXMl",
"object": "chat.completion",
"created": 1762343744,
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Hello! I'm just a program, so I don't have feelings, but I'm here and ready to help you. How can I assist you today?",
"refusal": null,
"annotations": null,
"audio": null,
"tool_calls": null
},
"finish_reason": "stop",
"logprobs": null
}
],
"model": "sakana/fugu-ultra-v2",
"usage": {
"prompt_tokens": 137,
"completion_tokens": 914,
"total_tokens": 1051,
"completion_tokens_details": null,
"prompt_tokens_details": null
},
"meta": {
"usage": {
"credits_used": 120000,
"usd_spent": 0.06
}
}
}Code Example
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