Sakana Chat, the Tokyo-based lab's free browser interface, just received its biggest update since launch. The new version adds two models, the newly introduced Sakana Fugu and a next-generation Sakana Namazu, alongside sandboxed Python execution that lets the model produce and preview real deliverables inside the chat window.

Two models, two philosophies

Fugu and Namazu rest on fundamentally different ideas, and understanding that distinction matters for knowing which one to reach for.

Sakana Fugu is an orchestrator. Rather than a single neural network you query, Fugu acts as a conductor that bundles AIs together. Sakana AI describes it as "a full multi-agent system accessible via a single API." The design draws on two ICLR 2026 papers on learned model orchestration, TRINITY and the Conductor, which show how systems can learn to assemble, route, and coordinate expert agents for each task instead of relying on hand-designed workflows. In practice, a single Fugu request can fan out to multiple underlying frontier models playing Thinker, Worker, and Verifier roles, then synthesize a single answer back to you.

Sakana Namazu takes a different approach. It's a fine-tune of Moonshot AI's open-weight Kimi K2.6, aimed at Japanese language and Japanese business use. Sakana states plainly that it was adapted using in-house data, including tuning to reduce unnecessary refusals and limit bias in outputs. The adaptation shows up on Japanese-specific benchmarks: FairPoliticsQA (a measure of political neutrality) jumped from 34.10% on the base model to 56.30% on Namazu.