Fugu Ultra, a multi-agent system outperforming Fable 5, launches on OpenRouter

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On June 25, 2026, Fugu Ultra, a multi-agent system developed by Japanese AI firm Sakana AI, launched on OpenRouter. The system integrates GPT-5.5, Gemini 3.1 Pro, and Claude Opus 4.8, outperforming models such as Fable 5 in academic and coding benchmarks. Based on ICLR 2026 research, Fugu Ultra dynamically assigns roles, using Gemini for knowledge aggregation, GPT-5.5 for mathematical reasoning, and alternating models for coding tasks. It supports a context length of 1 million tokens, 128,000 tokens of output, and multimodal inputs. Pricing is $5 per million input tokens and $30 per million output tokens. On-chain data and analysis reveal strong adoption trends for AI-powered systems like Fugu Ultra.

ChainThink reports that on June 25, Sakana AI, a Japanese AI startup, launched its multi-agent collaborative system, Fugu Ultra, on OpenRouter.

This system dynamically orchestrates GPT-5.5, Gemini 3.1 Pro, and Claude Opus 4.8 through a single interface, outperforming Anthropic’s flagship model Claude Fable 5 and Mythos Preview in academic and programming benchmarks.

Fugu Ultra is a language model specifically designed to learn coordination mechanisms, with technology derived from the Trinity and Conductor papers published at ICLR 2026. It enables fine-grained, dynamic role allocation based on context: in general knowledge Q&A, it assigns Gemini 3.1 Pro as the aggregator; in mathematical computations, it switches to GPT-5.5 as the aggregator for error correction; and in multi-turn coding scenarios, it alternates between GPT-5.5 writing code and Claude Opus 4.8 auditing and debugging.

After launching on OpenRouter, this system now offers 1 million tokens of context and a maximum output of 128,000 tokens, with support for tool calling and multimodal inputs.

The pricing is $5 per million tokens for input, $30 per million tokens for output, and $0.50 per million tokens for cache reads;

The underlying model consumption from a single API call will be included in the total cost of orchestration and output, and users can control the reasoning depth and maximum token limit via the reasoning parameter.

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