Sakana AI Wires NVIDIA Nemotron Into Fugu, Beating Claude and GPT-5.5 on Coding
Sakana AI integrates NVIDIA's Nemotron open model family into its Fugu multi-agent orchestrator, creating a feedback loop that could redefine how open models get deployed in production.
- Sakana AI integrates NVIDIA Nemotron into its Sakana Fugu multi-agent orchestration system, expanding its pool of open-weight specialist agents.
- Fugu is not a single model -- it's a trained orchestrator that dynamically coordinates Claude Opus 4.8, GPT-5.5, Gemini 3.1 Pro, and now Nemotron behind one OpenAI-compatible API.
- Fugu Ultra scores 73.7 on SWE-Bench Pro (vendor-reported), ahead of Claude Opus 4.8 (69.2%) and GPT-5.5 (58.6%), though numbers are not yet independently verified.
- NVIDIA Nemotron is a fully open family (Nano/Super/Ultra) with open weights, training data, and recipes -- purpose-built for high-throughput agentic workflows with 1M-token context.
- The deal creates a feedback loop: Sakana gets deeper model specialization; NVIDIA gets real-world agentic performance data to improve Nemotron.
- Strategic context: the partnership deepens a relationship that started with NVIDIA's participation in Sakana's $200M Series A, and positions both companies in the growing sovereign/open AI movement.
Sakana AI, the Tokyo R&D lab, is deepening its partnership with NVIDIA in a move that goes well beyond a typical model integration. The next phase of this work will bring NVIDIA's open model stack, including NVIDIA Nemotron, into Sakana Fugu, Sakana AI's multi-agent orchestration system -- combining NVIDIA's open weights and accelerated computing with Sakana's Japan-born approach to collective intelligence.
This is not a cold partnership. NVIDIA participated in Sakana AI's Series A funding round, where the company raised approximately $200M, led by New Enterprise Associates, Khosla Ventures, and Lux Capital. The relationship has continued to deepen: Sakana raised a $135 million Series B in November 2025, valuing the company at approximately $2.65 billion. What's new here is the technical depth of the collaboration -- Nemotron is now being wired directly into Fugu's agent pool.
What Fugu actually is
Fugu bypasses the traditional monolithic model structure by dynamically routing queries to a swappable pool of specialized AI agents. The key insight is that instead of using domain knowledge to prescribe team organization, roles, or workflows, Fugu learns to dynamically assemble agents from a pool and coordinate them through non-obvious but highly efficient collaboration patterns.
The technology behind Sakana Fugu comes from two ICLR 2026 research papers: Trinity (an evolved LLM coordinator) and The Conductor (learning to orchestrate agents using reinforcement learning). Fugu can even read its own output and decide whether to try a better coordination strategy -- a capability called recursive orchestration -- without any retraining.
The system ships as two tiers behind a single OpenAI-compatible API:
- Fugu -- balanced performance and low latency, suited for everyday coding, chat, and review tasks.
- Fugu Ultra -- coordinates a deeper expert pool for hard, high-stakes problems like Kaggle competitions, paper reproduction, cybersecurity, and patent search.
For developers, integration doesn't require rearchitecting existing applications. You swap out an endpoint and suddenly have access to a coordinated multi-agent system instead of a single model.