Sakana Fugu: One Model to Command Them All
Sakana AI Releases ‘Fugu Ultra’ to Match Frontier Performance via Autonomous Model Orchestration.
Our Fugu Ultra model stands shoulder-to-shoulder with leading models like Anthropic’s Fable 5 and Mythos Preview across the industry’s most rigorous engineering, scientific, and reasoning benchmarks while delivering frontier capability without the risk of export controls.
We are excited to introduce Sakana Fugu, a new product from Sakana AI that delivers a full multi-agent orchestration system as a single foundation model. Fugu dynamically orchestrates the world’s best models to tackle complex, multi-step tasks, accessible through a single model API. The result is multi-agent intelligence delivering the very best frontier-level performance without any single-vendor dependency or the complexity of a traditional multi-agent system.
Sakana Fugu is itself a language model trained to call various LLMs in an agent pool, including instances of itself recursively. Fugu dynamically orchestrates the world’s best models to tackle complex, multi-step tasks. Plug collective intelligence directly into your workflows today with a single API.
Beyond Bigger Models: Orchestration Models are the Next Frontier
For the past few years, progress in AI has been driven largely by brute-force scale: building giant, monolithic models trained on ever-larger amounts of data. But hard, real-world tasks require a multitude of specialized knowledge and skills, far beyond any individual benchmark. Unlocking the very best performance therefore requires collective intelligence: knowing which model to use, delegating tasks such as planning and execution, and combining domain-specific strengths while routing around individual weaknesses.
Since our founding, Sakana AI has been guided by a core conviction: the most powerful AI systems will not be isolated monoliths, but collaborative ecosystems. Evolution innovates under constraints, and the future belongs to systems that explicitly learn how to coordinate collective intelligence.
Today, this orchestration is no longer just a technical optimization; it has become a geopolitical and operational imperative. Recent disruptions in the AI landscape have demonstrated the severe risk of single-vendor dependency. For an organization or a nation, relying on a single company’s APIs for critical infrastructure, finance, or governance is a material vulnerability. This risk is no longer a hypothetical possibility, but a reality. As we have seen recently from export controls imposed on Anthropic’s Fable and Mythos models, access can shift or disappear overnight due to changing regulatory boundaries, export controls, and foreign policies.
Collective intelligence serves as the practical hedge against this concentration of power. Sakana Fugu is powered by models trained to be powerful orchestrators with an underlying pool of entirely swappable agents. If a single provider restricts access, Fugu dynamically routes around the disruption. Over time, Sakana Fugu will naturally grow by incorporating newer, more efficient models, including our own. By orchestrating the world’s models, we are delivering the realistic, resilient blueprint required for AI sovereignty.
What Is Sakana Fugu?
Sakana Fugu is a multi-agent system that behaves like a single model. You send a request to one endpoint, and Fugu decides how to handle it: solving it directly when that is enough, or assembling and coordinating a team of expert models when a task calls for more. It manages model selection, delegation, verification, and synthesis internally, so the complexity of a multi-agent system never reaches your code.
What makes this possible at scale is that Fugu is itself a language model specialized to understand when to delegate, how agents should communicate, and how to combine their work into a single, reliable answer. This approach builds on our research on learned model orchestration, including our recent ICLR 2026 papers Trinity and the Conductor. From the outside, you simply call one model. On the inside, a coordinated system of experts is doing the work.
Fugu and Fugu Ultra
At launch, Sakana Fugu comes in two models, so you can match the system to your workload. Both models can be accessed via a single OpenAI-compatible API.
Fugu balances strong performance with low latency, making it a great default for everyday work. It fits naturally into tools like Codex for coding and code review, as well as chatbots and other interactive services. For teams with data, privacy, or compliance requirements, Fugu also lets you opt specific agents out of its pool.
Fugu Ultra is tuned for maximum answer quality on hard, multi-step problems, coordinating a deeper pool of expert agents when accuracy and depth matter most. Early users have relied on it for demanding work such as AI research, paper reproduction, cybersecurity analysis, and literature and patent investigations.
Here is how the two models perform across standard benchmarks:
Our Fugu Ultra model stands shoulder-to-shoulder with leading models like Fable 5 and Mythos Preview across the industry’s most rigorous engineering, scientific, and reasoning benchmarks. It delivers frontier capability without the risk of export controls.
Performance comparison of Fugu models and baseline frontier models across a suite of coding, reasoning, scientific, and agentic benchmarks. All scores other than Fugu’s are reported by the model providers. For Fable 5 and Mythos Preview, we report the max of the two if both scores are available on the same benchmark. Neither of them is in Fugu’s agent pool as they are not publicly accessible. For more details, please refer to our technical report.
Benchmark results comparing Fugu with underlying foundation models used by Fugu, where highest scores are in boldface and the second highest are underlined:
＊We use the mini-swe-agent as the scaffolding for this task.
†We use model provider-reported scores for the baselines.
What Early Users Are Building
Benchmarks tell only part of the story. Fugu’s value shows up most clearly in long, messy, real-world workflows, which is exactly what we focused on during our beta program with close to 500 early users, whose feedback helped us improve the system.
Applications of Fugu Models. In our experiments, we find that Fugu Models consistently outperform frontier models Gemini 3.1 Pro (high), Opus 4.8 (max), and GPT 5.5 (xhigh) for various applications, such as AutoResearch, Rubik’s Cube, Mechanical Design, Japanese Handwriting Analysis, One-Shot Chess, Financial Time Series Prediction.
One of the clearest signals came from automated data science research: early users running Sakana Fugu in an almost fully automated research mode saw it drive meaningful progress with little to no human intervention. For us, this is exactly the kind of task Fugu Ultra is designed for: open-ended, multi-step work where the system needs to explore ideas, run experiments, interpret failures, revise its approach, and keep making progress over time.
Here is what other users are saying:
“For code review, Fugu Ultra is significantly better than GPT-5.5. It gives comprehensive answers and finds the bugs others miss. Where other tools flag about three issues, Fugu surfaced more than twenty. It's become the model I run all my reviews through.”
— Software Engineer, on Coding and Code Review
“Raw output quality is on par with top frontier models, but Fugu showed unusually strong persona stability across long sessions, holding its identity where other models drift. For agent products, that may matter more than raw benchmark scores.
— Executive at Enterprise Platform Company, on Orchestration Quality
“Given one scoped instruction, Fugu drove a full security assessment end-to-end — recon, XSS/SQLi checks, auth review, and a clean report with evidence and retest steps — staying inside scope and avoiding destructive actions.”
— Cyber Security Engineer, on Security Assessment Analysis
We saw similar patterns across paper reproduction, cybersecurity analysis, code review, and literature and patent investigations. In these workflows, the value of Fugu is not just a better answer to one prompt, but sustained progress across many steps: reading, implementing, testing, comparing evidence, finding gaps, and producing a useful final analysis or report. The beta made clear that multi-agent orchestration matters most when the task is messy, long-running, and difficult to solve with a single model call.
Sakana Fugu is generally available today. You can access both Fugu and Fugu Ultra through a single API, with subscription tiers for everyday use and a pay-as-you-go plan for heavier and enterprise workloads. To get started, visit our product page or console site.
Looking Ahead
We are deeply grateful to our early users who put Fugu through real, demanding work and helped us shape what it is today. This launch is a starting point, not a finish line. Because Fugu is built on learned orchestration rather than fixed workflows, it improves as the underlying ecosystem improves: as new frontier models arrive, we can fold them into Fugu’s agent pool and pass the gains on to you. In the months ahead, we plan to expand the pool of expert agents, including open models and Sakana AI’s own models, to strengthen coordination for long-running and agentic tasks, and give users more control over how Fugu works on their behalf. We are excited to see what you build with it.