Sakana Fugu

• Multi-agent orchestration that coordinates multiple advanced AI models through a single model interface.
• Dynamic model routing that selects suitable underlying models according to the task.
• Fugu model for everyday coding, interactive tasks, and general-purpose workflows.
• Fugu Ultra for complex, multi-step reasoning and demanding research tasks.
• Fugu Max for coordinating a larger model pool while balancing performance and cost.
• Fugu Cyber model specialized for cybersecurity analysis and security-related reasoning.
• OpenAI-compatible Chat Completions and Responses API endpoints.
• Anthropic Messages API compatibility for supported integrations.
• Integration options for Codex CLI and Claude Code development workflows.
• Custom model-pool controls that let users exclude selected providers or agents from Fugu routing.

What Is Sakana Fugu?

Sakana Fugu is an AI model orchestration system developed by Sakana AI. It coordinates multiple advanced language models and specialized agents to tackle complex tasks through a single interface. Instead of requiring users to select and manage every underlying model themselves, Fugu dynamically organizes model participation according to the task.

The platform is designed for work that can benefit from several models collaborating, including software development, code review, research, scientific reasoning, analysis, and other multi-step problems. Users can access Fugu through an API or integrate it with compatible development tools.

Multi-Agent AI Orchestration

Fugu’s core technology is its ability to coordinate multiple AI models as a unified system. Each underlying model may have different strengths, and Fugu aims to combine those capabilities to address a user’s request.

Rather than relying on one model for every problem, the orchestration system dynamically selects and coordinates agents. It then brings their work together into a response. This approach is intended to make complex tasks more manageable through a single model endpoint.

Dynamic Model Selection

Fugu can route tasks to suitable models from its available pool. The routing process is designed to account for the task’s requirements rather than forcing users to choose a separate model for every request.

This can simplify workflows where different stages require different capabilities. The underlying model pool may change as Sakana AI updates the service, and the specific models involved in a request depend on the active Fugu configuration.

Sakana Fugu Model

The standard Fugu model balances performance and response speed for general-purpose work. It is intended for everyday coding, code review, interactive applications, and tasks where a practical balance between response quality and latency matters.

Fugu is available through Sakana AI’s API platform. Developers can connect supported applications to the service using an API key and a compatible request format.

Fugu Ultra for Complex Reasoning

Fugu Ultra is designed for demanding problems that benefit from deeper coordination among expert agents. It targets multi-step reasoning, complex coding, research, and analysis tasks where answer quality and depth are especially important.

Potential workflows include investigating a difficult technical question, comparing research findings, reproducing scientific work, or analyzing a large problem through several stages. Fugu Ultra is positioned for higher-demand workloads and has its own pricing structure.

Fugu Max for Performance and Cost Efficiency

Fugu Max coordinates a larger pool of models and aims to balance performance with cost efficiency. Its orchestration strategy is designed to identify combinations of agents that provide an appropriate trade-off between output quality and usage cost.

Fugu Max is available through the Sakana AI API platform. The model pool and capabilities may evolve as the service is updated, so users should consult the current model documentation before building workflows around a specific version.

Fugu Cyber for Security Analysis

Fugu Cyber is a specialized model designed for cybersecurity reasoning. It targets security-related analytical work, including vulnerability research, threat investigation, and interpretation of security information.

Unlike the standard subscription models, Fugu Cyber is offered through pay-as-you-go billing and requires an access request. Sakana AI reviews applications before granting access, and users should consult the provider for current eligibility requirements and pricing.

Software Development and Code Review

Fugu can be integrated into coding workflows where users need help understanding code, reviewing changes, reasoning through implementation choices, or working on multi-step development tasks. Its model-orchestration approach is intended to draw on different capabilities rather than relying on a single fixed model for every request.

Developers can use compatible interfaces to send prompts and receive results. As with other AI coding systems, generated code should be reviewed, tested, and checked against project requirements before it is used in production.

Research and Scientific Analysis

Fugu’s multi-agent architecture is also intended for research tasks involving several stages of investigation. These may include reading technical material, comparing findings, identifying gaps, organizing evidence, and producing a structured summary.

For scientific or academic work, users should independently verify important claims, citations, calculations, and conclusions. Coordinating multiple models can support analysis, but it does not guarantee that every result is correct or that all relevant evidence has been considered.

API Access and Compatibility

Sakana Fugu is available through the Sakana AI API platform. The service supports OpenAI-compatible Chat Completions and Responses endpoints, as well as the Anthropic Messages API.

This compatibility can make integration more straightforward for developers who already work with supported request formats. Users still need to configure the correct endpoint, model name, authentication credentials, and any client-specific settings required by their application.

Integration With Codex CLI and Claude Code

Sakana Fugu provides setup options for Codex CLI and Claude Code. These integrations let developers use Fugu in supported coding environments without building a complete orchestration layer themselves.

The provider supplies installation instructions for supported environments, including Ubuntu and macOS for the one-line installation method. Windows users or those encountering installation issues may need to follow the manual configuration instructions. Compatibility details can differ between tools and may change with updates.

Custom Model Pool Controls

Fugu offers a custom model-pool option that lets users exclude selected providers or agents from routing. This can be relevant for teams that need more control over which underlying models may participate in their requests.

The setting is useful to consider when designing workflows with particular data, privacy, or compliance requirements. Users should review the service’s current documentation and contractual terms rather than assuming that model-pool configuration alone satisfies every organizational requirement.

Pricing and Usage Billing

Sakana Fugu provides subscription plans for regular use and pay-as-you-go billing for consumption-based workloads. The published monthly subscription tiers are Standard at $20, Pro at $100, and Max at $200.

The Pro plan includes ten times the Standard plan’s usage allowance, while Max includes twenty times the Standard allowance. Exact usage limits are governed by the platform’s current plan terms.

Pay-as-you-go billing charges according to the underlying model and token consumption. For Fugu requests involving multiple agents, Sakana AI states that it does not stack all individual model fees; instead, the charge is based on the highest-tier model involved. Some specialized models, such as Fugu Cyber, have separate access requirements.

Fugu Ultra Token Pricing

Fugu Ultra has published per-million-token rates. The current pricing information lists input tokens at $5, output tokens at $30, and cached input tokens at $0.50 for standard context lengths.

For contexts exceeding 272,000 tokens, the listed rates rise to $10 for input, $45 for output, and $1 per million cached input tokens. Users should confirm the active model version and rates before estimating the cost of large requests.

Usage Monitoring and Cost Planning

Because Fugu can coordinate several agents during a task, token usage may differ from a simple request to a single model. Users should monitor usage and review the billing details available through the API platform.

Subscription allowances may suit regular individual work, while pay-as-you-go billing provides a consumption-based option for workloads that fluctuate. The most suitable arrangement depends on the frequency, complexity, and length of the tasks being processed.

Who Can Benefit From Sakana Fugu?

Sakana Fugu may be useful for software developers, research teams, technical analysts, and organizations working on tasks that require several stages of reasoning. Its API access also makes it relevant to developers building applications that need a unified model interface.

Users who need straightforward text generation may not require multi-agent orchestration for every request. Fugu is particularly relevant when a task can benefit from dynamically combining the strengths of several underlying models.

How to Get Started With Sakana Fugu

To begin, create an account through the Sakana AI console and generate an API key. Store the key securely, since it is shown only once during creation, and configure your application to use the documented API endpoint.

Select the appropriate Fugu model, review the current pricing and usage terms, and send a test request through a supported API interface. Developers using Codex CLI or Claude Code can follow the provider’s installation and configuration instructions for their environment.

Frequently Asked Questions About Sakana Fugu

What is Sakana Fugu?

Sakana Fugu is a multi-agent AI orchestration system that coordinates multiple advanced language models to solve complex tasks through a unified interface.

What is the difference between Fugu and Fugu Ultra?

Fugu is designed for general-purpose tasks with a balance of performance and latency. Fugu Ultra is designed for harder, multi-step tasks where deeper orchestration and answer quality are priorities.

What is Fugu Max?

Fugu Max coordinates a larger pool of models and aims to balance output quality with cost efficiency.

What is Fugu Cyber?

Fugu Cyber is a specialized orchestration model for cybersecurity reasoning, including vulnerability research and threat investigation. It requires an access request and uses pay-as-you-go billing.

Is Sakana Fugu free?

The API platform lists paid subscriptions and pay-as-you-go billing. A free API subscription tier is not listed in the current pricing information. Sakana Chat is a separate product with its own access and usage limits.

How much does Sakana Fugu cost?

The listed API subscription prices are $20 per month for Standard, $100 per month for Pro, and $200 per month for Max. Pay-as-you-go costs depend on the models and tokens used.

Does Sakana Fugu support OpenAI-compatible APIs?

Yes. The API platform supports OpenAI-compatible Chat Completions and Responses endpoints, along with the Anthropic Messages API.

Can I use Sakana Fugu with coding tools?

Yes. Sakana AI provides integration instructions for Codex CLI and Claude Code, subject to supported environments and configuration requirements.

Can I control which models Fugu uses?

Fugu supports a custom model-pool option that allows users to exclude selected providers or agents from routing.

Who should use Sakana Fugu?

It may suit developers, researchers, technical teams, and organizations that need coordinated AI reasoning for complex coding, analysis, and research workflows.

Does multi-agent orchestration guarantee better answers?

No. Coordinating multiple models is intended to improve how complex tasks are handled, but results can still contain errors. Important code, calculations, and research findings should be independently reviewed.

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