Introducing Sakana Fugu: A full multi-agent orchestration system accessible via a single model API.

Our ‘Fugu Ultra’ model matches the performance of Fable and Mythos, delivering frontier capability without the risk of export controls.

Fugu stands shoulder-to-shoulder with leading models like Fable and Mythos across the industry's most rigorous engineering, scientific, and reasoning benchmarks.

Beyond Bigger Models: Why are Orchestration Models the Next Frontier

Progress in AI has been driven largely by giant, monolithic models. But the most powerful systems of the future will be collaborative ecosystems.

Today, this orchestration is no longer just a technical optimization. It has become a geopolitical and operational imperative.

For an organization or a nation, relying on a single company's model 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 with recent export controls imposed on models like Fable and Mythos, access can disappear overnight.

Collective intelligence is the practical hedge against this concentration of power. Because Fugu orchestrates an underlying pool of swappable agents, it simply routes around vendor restrictions.

By orchestrating the world’s models, we are delivering the resilient blueprint required for true AI sovereignty.

How does it work?

Sakana Fugu is itself an LLM, 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.

Fugu manages model selection, delegation, verification, and synthesis automatically. It solves tasks directly when that is enough, or coordinates a team of expert models when a problem calls for more. The complexity of a multi-agent system never reaches your code.

At launch, Sakana Fugu comes in two models accessed via a single OpenAI-compatible API:

• Fugu balances strong performance with low latency for everyday work. It fits naturally into tools like Codex for coding, as well as chatbots and interactive services. You can also opt specific agents out of its pool for data compliance.
• Fugu Ultra is our flagship model tuned for maximum answer quality on hard, multi-step problems. It coordinates a deeper pool of expert agents for demanding work like AI research, cybersecurity analysis, and patent investigations.

Use Case 1: Autonomous ML Research

Can an AI autonomously improve another AI’s training recipe?

We tasked Fugu Ultra with improving a small GPT model using AutoResearch. Over 14 hours on a single H100 GPU, Fugu ran > 100 experiments. It iteratively edited the training code, ran tests, and kept any changes that successfully lowered the validation error rate.

Fugu Ultra finished with the best mean performance and achieved the best single run of the entire experiment, leading every single baseline.

Use Case 2: Financial Time Series Prediction

Can an AI agent navigate sequential, no-look-ahead market decisions?

We tested Fugu Ultra on 50 weeks of historical data. Fugu Ultra grew the portfolio to $11,943.22 (+19.43% mean return), while other frontier models capped out at less than a +15% return.

Use Case 3: One-Shot Blindfold Chess

Can an AI hold an entire game state in memory without drifting?

We matched Fugu Ultra against 3 leading frontier models and a 2100-Elo Stockfish engine. Fugu Ultra outplayed all 4 opponents, remaining accurate while other models eventually drifted or lost track of the board state.

Use Case 4: Computer Aided Design of Mechanical Iris

Can an AI generate precise, functional mechanical designs?

Fugu Ultra generated a highly functional design where the blades rotate correctly around outer pins to fully open and close the aperture, whereas other frontier baselines failed the physical logic.

Use Case 5: Rubik’s Cube Solver

Can an AI write complex algorithmic solvers from scratch?

We tasked Fugu Ultra and three frontier models with writing a Rubik’s Cube solver in pure Python. Fugu Ultra’s solver was strictly more efficient, averaging 19.72 moves versus the next best model’s 19.76.

Use Case 6: Classical Japanese Kana Reading Order

We tested whether the models could recover the reading order of "scattered writing" (chirashigaki) in a letter from 1610. Fugu Ultra achieved a 0.80 accuracy score, tracing the highly complex path almost exactly, while other models scored significantly lower.

Introducing AB-MCTS

Our new inference-time scaling algorithm enables collective intelligence for AI by allowing multiple frontier models (like Gemini 2.5 Pro, o4-mini, DeepSeek-R1-0528) to cooperate.

Inspired by the power of human collective intelligence, where the greatest achievements arise from the collaboration of diverse minds, we believe the same principle applies to AI.

AB-MCTS (Adaptive Branching Monte Carlo Tree Search) harnesses individualities, allowing multiple models to cooperate and engage in effective trial-and-error, solving challenging problems for any single AI. Our initial results on the ARC-AGI-2 benchmark are promising, with AB-MCTS combining current frontier AI models to significantly outperform individual models by a substantial margin.