Sakana AI's Fugu Ultra Beats GPT-5 by Orchestrating Rival Models

Sakana Fugu is a new kind of AI product: not a bigger model, but a model that commands other models. Built by Tokyo-based Sakana AI, it wraps a full multi-agent orchestration system behind a single API endpoint. You send a request, and Fugu decides whether to answer directly or assemble a team of specialized frontier models to tackle it. The complexity never reaches your code.

Fugu dynamically orchestrates the world's best models to tackle complex, multi-step tasks, accessible through a single model API. At launch, it comes in two variants: Fugu, tuned for low latency and everyday work, and Fugu Ultra, the flagship model optimized for maximum answer quality on hard, multi-step problems.

The Geopolitical Trigger

The timing of this launch is not accidental. 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. Specifically, export controls imposed on Anthropic's Fable and Mythos models showed that access can shift or disappear overnight due to changing regulatory boundaries, export controls, and foreign policies.

Fugu's architecture is a direct answer to that problem. Because 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. The agent pool is designed to be modular: swap out a restricted model, fold in a new one, and the system keeps running.

How It Actually Works

The Fugu model is itself a small language model that learns to call other LLMs. In the course of training, it can learn to call itself, enabling test-time scaling. This recursive self-calling is key: the orchestrator can spin up corrective workflows when its first coordination strategy fails, without any retraining.