Sakana Namazu by @SakanaAILabs is live on OpenRouter. Built on Kimi K2.6, Sakana Namazu is a specialized model that combines a deep understanding of Japanese culture with high-performance reasoning capabilities, integrating web search and code execution to handle complex tasks within a business context.

Japan has an opportunity to compete differently in the next phase of AI. David Ha, Co-Founder & CEO of @SakanaAILabs, joins #BloombergInvest# Tokyo to examine frontier research, autonomous systems and how Japan can turn innovation into scalable economic impact. Register here:

Our Neuroevolution textbook is finally in print! Free online edition: Pre-order: I am incredibly grateful to my co-authors Sebastian Risi, Yujin Tang, and Risto Miikkulainen for making this happen. Neuroevolution is a subject very dear to my heart. It is the field that convinced me that nature has already figured out how to build intelligence: through evolution, collective behavior, and adaptation under constraints. This idea, that intelligence emerges from evolution operating under constraints rather than unlimited resources, is what eventually led me to founding Sakana AI here in Japan. The concepts in this book about open-ended creativity and self-organizing systems are exactly what we build at @SakanaAILabs. Our name and logo are inspired by schools of fish moving together, adapting as one. It is the core philosophy of what we build. This book captures the theoretical foundations of that belief.

In the single-model era, one API call usually implied one relatively legible execution path. You knew which model ran. You had a rough mental model of cost. You could reason about latency and usage from the outside. In the orchestration era, none of that holds. A case study 👇 ---- @SakanaAILabs launched Fugu this week. It looks like one model. You call it through a single API. But what you call and what actually runs are two different things. Fugu is a coordinator trained to break a task apart, route pieces of it to a pool of other models, and call instances of itself recursively to handle sub-tasks. One prompt can fan out into a swarm of sub-calls the user never sees. The whole thing still surfaces as one model response. Two developers ran into the consequences of that almost immediately. @LLMJunky said a single prompt consumed 100% of a five-hour quota. @cortesi paid for Sakana's $200 tier, found the API slow, and said he hit his limit in under an hour. Neither could say which models ran, how many times Fugu called itself, or why one prompt cost what it did. The issue isn't specific to Fugu either. When @AnthropicAI briefly opened access to Fable earlier this month, users reported similar quota burns with similarly little visibility into why. When one request fans out into many model calls, and some of those calls spawn more, the unit the buyer sees no longer maps cleanly to the unit of work happening underneath. Forecasting cost gets harder. So does explaining latency, attributing performance, or knowing what actually ran. Sakana's benchmark figures sharpen the question. Shared numbers for Fugu include 54.2 on SWE-Pro, 95.1 on GPQA-D, and 93.2 on LiveCodeBench v6, with testers noting scores above Opus, Gemini 3.1, and GPT 5.4 on each. But Sakana's own base model is around 7B parameters. Frontier-tier results from a coordinator that size strongly suggest that a meaningful share of the work is being done by the larger third-party models it calls. Behind one endpoint, the system may be making its own decisions about how much work to do, which models to invoke, and how many times to invoke them. For hobby use, opaque execution is an annoyance. For production systems touching real money or real workflows, it becomes harder to dismiss. You can't price what you can't predict, and you can't debug what you can't trace. When the run is a sealed box, it also becomes harder to prove what the agent actually did. Fugu is one early launch, and Sakana will likely smooth out the rough edges. But as orchestration becomes the product, execution visibility becomes core infrastructure. The next layer of competition in AI will be won by the teams that can make increasingly complex agentic runs legible enough to trust. Follow @SaharaAI for analysis of agent infrastructure, the AI economy, and where the real building is happening.

🐡 The era of agonizing over which single LLM is "the best" might be coming to an end. Title: Sakana Fugu Technical Report URL: Sakana AI's Fugu is a commander (orchestrator) model that marshals a team of frontier LLMs and automatically assembles the best lineup for each task. Here are the three highlights worth knowing. 🧭 A learned conductor Fugu adds a lightweight selection head on top of a pretrained backbone and picks the best worker model directly from logits over hidden states. Because it skips autoregressive decoding, it keeps latency on par with calling a single frontier model — an elegant trick. 🕸️ Dynamically writing its own workflows (Fugu-Ultra) The high-end variant writes out full agentic workflows in natural language, defining task decomposition, subtask allocation, and targeted communication strategies. It composes multi-agent topologies — debate, sequential chaining, tree structures — of up to 5 steps, tailored per problem. 📊 Beating any single model 73.7 on SWE-Bench Pro (above Claude Opus 4.8's 69.2), 95.5 on GPQA-Diamond, 93.2 on LiveCodeBench — state-of-the-art among publicly accessible models. In agentic coding it lands a 5–6% relative gain, described as a full "generational" improvement. Reaching the frontier through smart orchestration rather than sheer scale — a fresh new scaling axis. #SakanaAI# #LLM#

Sakana AI won 1st place in the company category at the “Recursive Self Improvement in Mathematics” Hack (OpenMath 2026), held with @stephen_wolfram at Harvard Innovation Labs. Competition: Results: Drawing mainly on our Sakana Fugu models and internal agentic tools, our team (Rachel Teo, Abderrahmene Hamdi, Wenyi Wang) contributed 14 mathematical families, including 6 complete resolutions, plus new bounds and structural advances. Thanks to the organizers and everyone involved. We plan to share more about our approach and learnings in a future report.