Sakana launches Fugu, a multi-agent orchestration system behind a single API

No Claude Fable 5? No problem: Sakana achieves frontier performance with new Fugu multi-model, auto synthesis system

Last night, the increasingly enterprise-focused AI startup Sakana launched Fugu, a multi-agent orchestration system that delivers frontier-level AI performance through a single, OpenAI-compatible API. Designed for developers, enterprises, and nations seeking resilience against vendor lock-in and geopolitical export controls, Fugu (Japanese for "pufferfish"), bypasses the traditional monolithic model structure by dynamically routing queries to a swappable pool of specialized AI agents. Sakana CEO and co-founder David Ha, formerly of Google Brain, positioned Fugu as a more reliable option for enterprise workflows than any single AI model provider in the wake of Anthropic's move on June 12 to revoke public access to its most powerful models, Claude Mythos 5 and Claude Fable 5, in the wake of a U.S. government export control order. As Ha wrote in a post today on X:"Fugu dynamically orchestrates the world’s best models to tackle complex tasks. We are proving that a well-orchestrated pool of swappable agents can match restricted frontier models like Fable and Mythos. But Fugu is about more than just performance. I believe that Orchestration Models are the next frontier, beyond bigger models. Relying on a single company’s model for national infrastructure is a massive risk. As recent export controls have shown, access to top models can disappear overnight. Collective intelligence is the practical hedge against this concentration of power. Fugu simply routes around vendor restrictions by relying on an entirely swappable agent pool."Sakana AI explicitly states that the specific models Fugu selects and how it coordinates them are proprietary, meaning this routing information is hidden from the user by design. The documentation only refers generally to a "diverse pool of powerful models," "multiple LLMs," or "specialized models" without providing a specific count.By acting as a sophisticated coordinator rather than a standalone foundation model, Fugu matches the output quality of top-tier models like Fable and Mythos on third-party benchmarks of agentic tasks, while fundamentally altering how developers deploy critical AI infrastructure.How Sakana Fugu works and where it beats Anthropic's Claude Fable 5At its core, Sakana Fugu operates like a master general contractor. When presented with a complex request, Fugu does not attempt to execute every step itself. Instead, it breaks the problem down, delegates sub-tasks to a pool of expert foundation models, verifies their work, and synthesizes the final output."Fugu is itself an LLM, trained to call various LLMs in an agent pool, including instances of itself recursively," the Sakana AI team noted in their technical release. Grounded in two of Sakana's 2026 research papers, TRINITY and the Conductor, the system autonomously manages the entire lifecycle of model selection and verification using learned coordination strategies rather than hand-designed workflows. To the end user, this multi-agent swarm is entirely abstracted behind a standard API endpoint.Sakana AI is offering two variants of the system to cater to different operational workloads:Fugu: A high-speed, low-latency model optimized for everyday tasks. It is designed to act as the default engine for interactive chatbots and integrates directly into coding environments like Codex.Fugu Ultra: The flagship tier engineered for complex, high-stakes tasks such as AI research, cybersecurity analysis, and multi-step patent investigations. According to Sakana, Fugu Ultra coordinates a deeper pool of experts and matches industry-leading monolithic models across rigorous scientific and reasoning benchmarks.Additionally, on the pay-as-you-go plan, standard Fugu charges a dynamic rate based on the specific underlying models activated, whereas Fugu Ultra utilizes a fixed pricing structure starting at $5 per million input tokens and $30 per million output tokens.As indicated by benchmark charts shared by Sakana, Fugu actually exceeds the performance of Anthropic's Claude Fable 5 on LiveCodeBench, an open source benchmark testing coding performance on regularly refreshed, software problem-solving tasks (Fugu Ultra: 93.2, Fugu: 92.9, Fable: 89.8), and beats the prior Claude Mythos Preview model on GPQA-D (Diamond) , a test of 198 graduate-level multiple-choice questions in biology, physics, and chemistry (Fugu Ultra: 95.5, Fugu: 95.5, Mythos Preview: 94.6).By orchestrating multiple models from different providers, Fugu essentially builds native redundancy into the AI stack. If one provider suffers an outage or faces sudden regulatory restrictions, Fugu routes around the disruption to maintain uptime.Licensing and availabilityFugu is offered as a commercial, proprietary API service, not an open-source framework. Because Sakana’s core intellectual property lies in its non-obvious collaboration patterns, the specific routing information—meaning exactly which underlying models Fugu selects for a given query—remains proprietary and is intentionally hidden from the user.However, Sakana offers critical controls for enterprise data compliance. Developers can explicitly opt specific models or providers out of their Fugu routing pool to maintain strict corporate privacy standards. Additionally, users can opt out of having their prompts used for future training data. Geographically, Fugu is restricted from operating within the European Union (EU) and European Economic Area (EEA) while Sakana works to align its black-box data routing architecture with GDPR regulations.Pricing is fairly steepFugu is available immediately in most regions—with the temporary exception of the EU and EEA—at subscription tiers and pay-as-you-go pricing.Teams can opt for monthly subscription allowances designed for individual or hands-on use: a Standard tier at $20/month for lightweight workflows, a Pro tier at $100/month providing 10x standard usage, and a Max tier at $200/month offering 20x usage for continuous, long-running tasks. I wasn't able to find the actual amount of tokens covered under these plans, but I've reached out to Ha on X for more information.As part of the initial rollout, Sakana is offering a free second month for users who subscribe to any tier by July 31, 2026.For enterprise scaling and production deployments, Sakana offers an elastic pay-as-you-go plan. Crucially for high-stakes environments, requests made under this consumption-based model are served at a higher priority than those from monthly subscription plans. Under this framework, the standard Fugu engine charges the single rate of the highest-tier underlying model involved in a query, without ever stacking multi-agent fees. The flagship Fugu Ultra tier (fugu-ultra-20260615) utilizes a fixed pricing structure per one million tokens: $5 for input, $30 for output, and $0.50 for cached input. These rates increase to $10, $45, and $1.00 respectively for extreme workloads utilizing context windows above 272K tokens. That puts it among the more expensive options compared to single AI models via provider APIs:VentureBeat Frontier AI Model API Pricing SnapshotModelInputOutputTotal CostSourceMiMo-V2.5 Flash$0.10$0.30$0.40Xiaomi MiModeepseek-v4-flash$0.14$0.28$0.42DeepSeekdeepseek-v4-pro$0.435$0.87$1.305DeepSeekMiniMax-M3$0.30$1.20$1.50MiniMaxGemini 3.1 Flash-Lite$0.25$1.50$1.75GoogleQwen3.7-Plus$0.40$1.60$2.00Alibaba CloudMiMo-V2.5$0.40$2.00$2.40Xiaomi MiMoGrok 4.3 (low context)$1.25$2.50$3.75xAIMiMo-V2.5 Pro (≤256K)$1.00$3.00$4.00Xiaomi MiMoKimi-K2.6$0.95$4.00$4.95MoonshotGLM-5.2$1.40$4.40$5.80Z.aiGrok 4.3 (high context)$2.50$5.00$7.50xAIMiMo-V2.5 Pro (>256K)$2.00$6.00$8.00Xiaomi MiMoQwen3.7-Max$2.50$7.50$10.00Alibaba CloudGemini 3.5 Flash$1.50$9.00$10.50GoogleGemini 3.1 Pro Preview (≤200K)$2.00$12.00$14.00GoogleGPT-5.4$2.50$15.00$17.50OpenAIGemini 3.1 Pro Preview (>200K)$4.00$18.00$22.00GoogleClaude Opus 4.8$5.00$25.00$30.00AnthropicGPT-5.5$5.00$30.00$35.00OpenAISakana Fugu Ultra$5.00$30.00$35.00Sakana AIClaude Fable 5 / Claude Mythos 5$10.00$50.00$60.00AnthropicDevelopers modeling operational costs should also note a significant architectural caveat in how Fugu bills for its multi-agent capabilities. According to the developer documentation, Fugu Ultra’s API responses include detailed usage fields that separate user-visible token generation from internal orchestration work. The background tokens consumed and generated when Fugu delegates sub-tasks, verifies code, or routes between underlying agents are not absorbed by the provider; they represent real token usage and are counted toward the final price of the request at standard rates.The Orchestration landscape: Fugu vs. The Field and notable benchmark performanceTo understand Fugu’s position in the mid-2026 AI ecosystem, it is critical to distinguish between model routing and multi-agent orchestration. Over the past year, enterprise adoption of standard routing platforms—such as Not Diamond, Martian, and the open-source RouteLLM framework—has skyrocketed. These systems act as intelligent air traffic controllers; using semantic classifiers or meta-models, they analyze an incoming prompt and predict which single foundation model will yield the highest quality or most cost-effective response, dispatching the query accordingly.Fugu operates on a fundamentally different paradigm. Rather than making a one-shot routing decision, Fugu aligns more closely with complex multi-round systems like Router-R1 (a framework introduced at NeurIPS 2025). It breaks a query down, interleaves reasoning with delegation, and dynamically assigns sub-tasks to multiple models in parallel or sequence before synthesizing a final output.While frameworks like LangGraph, CrewAI, and Microsoft AutoGen offer developers the tools to build similar multi-agent systems, they require immense manual configuration—defining roles, setting up conditional edges, and managing state across long-running loops. Fugu abstracts this operational overhead entirely. It is essentially a LangGraph-style workflow packaged as a single, black-box API endpoint.An orchestration system is ultimately bounded by the raw capabilities of the underlying models in its pool, a reality reflected in Sakana’s own benchmark testing against standalone frontier models.On rigorous coding and agentic tasks, collective intelligence shows a distinct advantage over standard models. Fugu Ultra posted a 73.7 on SWE-Bench Pro, significantly outperforming Anthropic's Claude Opus 4.8 (69.2) and OpenAI's GPT-5.5 (58.6). However, Fugu is not a silver bullet, and its performance is not a clean sweep across the board. When compared to highly specialized or restricted-access monolithic models, Fugu occasionally trails:SWE-Bench Pro: While Fugu Ultra (73.7) beat most accessible models, it was comfortably eclipsed by Anthropic’s limited-access Fable 5 (80.0), which is currently absent from Fugu's swappable pool due to the U.S. government's export control order and Anthropic's subsequent response to remove the model entirely from global usage. Humanity's Last Exam: Fugu Ultra (50.0) narrowly edged out Opus 4.8 (49.8), but again fell short of Fable 5 (53.3).Long-Context and Security: On the MRCRv2 long-context-recall test, OpenAI's GPT-5.5 maintained the lead (94.8 vs Fugu Ultra's 93.6), and Opus 4.8 remained the top performer on the CTI-REALM cybersecurity benchmark (69.6 vs Fugu Ultra's 69.4).The quantitative data points to a clear conclusion: Fugu is highly effective at boosting performance on messy, multi-step tasks (like writing a complex HTML5 game from scratch) by leaning on the combined strengths of multiple mid-tier and high-tier models. However, for sheer brute-force reasoning within a single, highly constrained domain, the industry's largest standalone models still hold the edge—provided an enterprise can maintain uninterrupted access to them.Background on Sakana's formation and noteworthy achievements to dateSakana AI was formed in Tokyo in 2023 by Llion Jones, a co-author of Google’s foundational 2017 "Attention Is All You Need" paper, and David Ha, the former head of research at Stability AI. Disillusioned by large tech company bureaucracy and the industry's hyper-fixation on scaling single, massive foundational models, the founders built Sakana around principles of biomimicry and evolutionary computing.The company's name, derived from the Japanese word for fish, reflects its core technical thesis: utilizing collective "swarm" intelligence rather than brute-force compute. Following a $2.6 billion Series B valuation in late 2025 and the recent June 2026 launch of Marlin—an autonomous, eight-hour research agent for the B2B sector—Fugu represents the commercialization of Sakana's multi-agent routing technology for everyday developers.A mixed reception among the broader AI community onlineThe developer community has responded to Fugu by rigorously testing its practical tradeoffs, weighing its routing efficiencies against the sheer power of monolithic foundation models.AI observer, developer and influencer Chris (@ChrissGPT on X) highlighted the specific utility of Fugu over raw foundational AI. "For a single clean prompt, you probably would [use Fable 5, Mythos, or GPT-5.5 directly]," he noted, but argued that Fugu's true value emerges in messy, multi-step environments. "...whether it involves delegation, verification, synthesis, code review, research loops, security analysis... the more it would make sense to use this," he wrote.Chris also pointed out the strategic geopolitical advantage of Fugu's architecture, noting that if frontier AI access is abruptly revoked due to regulation or export controls, an orchestrator can dynamically swap models to prevent a total system failure.Creative agency owner Mark Santos (@markksantos) of Mark Studios provided a direct, real-world comparison by tasking both Fugu Ultra and Claude Opus 4.8 with building a "Crossy Road" game clone using Three.js. The results underscored the operational differences between an orchestrator and a monolithic giant:Sakana Fugu Ultra: Completed the task in 22 minutes using ~89,000 tokens for roughly $7.32. However, the final game suffered from minor logic errors, such as inverted directional turns and wonky camera angles.Claude Opus 4.8: Took 79 minutes, burned ~940,000 tokens for nearly $37.85, and got stuck in a retry loop requiring human intervention. Despite the inefficiency, it ultimately produced superior application design and functionality.Santos concluded the experiment by stating, "In terms of application functionality, quality, and design, Opus won. In terms of model speed and performance, Fugu... won".Elie Bakouch, a research engineer at cloud-based, open AI infrastructure and systems provider Prime Intellect, pointed out on X that "to be clear, this is a closed source orchestrator on top of closed source models. if before you didn't control the models, now you don't even control which ones are used or how much. this is not 'AI sovereignty'..."These early tests and reactions mirror the sentiment summarized by Reddit user GreedyWorking1499 in initial platform discussions: "Until proven otherwise, this is just a highly advanced router/wrapper, not a fundamental not a fundamental leap in intelligence like Mythos/Fable was."Yet, as enterprises increasingly demand fail-safes against single-vendor reliance, Sakana is proving that packaging collective intelligence into a single API endpoint is a highly viable commercial path.

8 hours ago

Sakana AI's Fugu Explained: How the Multi-Agent Model Orchestrates Frontier LLMs

Sakana AI's Fugu is a good example of where the industry is heading. Instead of trying to win with one massive model, it coordinates a pool of strong models well. On the surface, Fugu is presented as a single API, but under the hood, it behaves like a learned manager that routes tasks, chooses roles, and stitches together the output of multiple frontier models. This makes Fugu a multi-agent orchestration system delivered as a single model, rather than just a chatbot with a nicer prompt. A lot of the messy work in production AI comes from orchestration: choosing the right model, deciding when to verify, splitting a task into subtasks, and avoiding expensive calls when a cheaper one will do. Fugu turns that problem into the product. What Fugu actually is Sakana AI describes Fugu as a multi-agent system as a model. You send one request to a single endpoint, and Fugu decides how to distribute the work across a pool of specialist models. That pool is not locked to a single vendor. The system can dynamically assemble agents, coordinate them, and even let users opt out of specific models or providers to fit privacy, data, or compliance requirements. The goal is to keep the API simple while making the backend coordination much smarter than a hand-built router. There are two public variants: Fugu, which balances latency and quality Fugu Ultra, which uses a deeper pool of agents for harder tasks This split is useful because not every task deserves the most expensive path. A lot of day-to-day coding, review, and internal support work needs a fast default. More difficult tasks, like deep reasoning, paper reproduction, or security analysis, can justify a heavier orchestration setup. How it works The basic workflow is different from a normal single-model call. First, the incoming task is routed into a learned coordination process. Fugu decides which agents should participate, what role each one should play, and how the exchange should proceed. The system learns collaboration patterns that are not obvious to a human operator, but work well in practice. Fugu is grounded in two ICLR 2026 papers: TRINITY and Conductor. TRINITY uses a lightweight evolved coordinator that assigns roles like Thinker, Worker, and Verifier across a multi-turn task. Conductor learns natural-language coordination strategies with reinforcement learning. Together, they show that instead of hand-designing every workflow, you can train a system to discover how to orchestrate other models. This points to a broader shift: while the last wave of AI progress focused on making single models stronger, this wave is about making model systems smarter. Why the orchestration layer matters Most teams already know that different models are good at different things. While one model might excel at code, others are better suited for long reasoning or factual retrieval. In a hand-built stack, someone has to decide when to call which model, how to verify the output, and when to stop paying for more inference. Fugu tries to learn those decisions instead of hard-coding them. This approach improves cost-performance. If the system can route easy subtasks to lighter agents and reserve heavier agents for the hard parts, the overall result can be better than sending every request to the most expensive model in the pool. It also improves reliability. A lot of failures in agentic systems happen because orchestration is brittle. When one model does everything, a single mistake ripples through the whole chain. Fugu's design reduces that risk by using specialists and verification roles more deliberately. Fugu versus Fugu Ultra The difference between the two variants is mostly about how much orchestration you want to pay for. Fugu is the balanced option, designed as the practical default for coding, interactive work, and general workloads where latency still matters. Fugu Ultra goes further, with Sakana positioning it for more complex, high-stakes, multi-step work where answer quality matters more than speed. The examples they highlight include paper reproduction, Kaggle competitions, security analysis, literature review, and patent research. This framing shows what the product is really for. Fugu is not just a better chat model; it is a system for tasks where the model has to reason, delegate, verify, and even disagree with itself before it answers. What the benchmarks suggest Sakana reports strong performance across coding, reasoning, science, and agentic benchmarks. Fugu and Fugu Ultra compare well with publicly available frontier models, sometimes sitting right alongside or ahead of them. The benchmarks they call out include: SWE-Pro for coding TerminalBench for terminal and tool use LiveCodeBench and LiveCodeBench Pro Humanity's Last Exam for hard reasoning GPQA-D for scientific reasoning SciCode Long-context reasoning MRCRv2 The exact numbers matter less than the pattern. Rather than claiming to be a single monolithic model, Fugu demonstrates that orchestration itself can produce frontier-level results on difficult tasks. Their qualitative examples make that point even more clearly. Sakana shows Fugu on tasks like autonomous research, classical Japanese reading-order recovery, Rubik's Cube solving, CAD generation for a mechanical iris, blindfold chess, and trading simulations. These environments are very different, but they all reward a system that can choose the right internal strategy instead of guessing once and hoping for the best. The product details that matter Fugu is delivered through an OpenAI-compatible API, which means teams do not need to rebuild their integration layer to try it. If you already have a client, a harness, or an internal agent stack that talks to an OpenAI-style endpoint, Fugu slots in without much friction. Sakana offers both subscription and pay-as-you-go plans. The pay-as-you-go model avoids stacking fees across every model in the pool; you pay a single rate based on the top-tier model involved in the configured pool. This makes orchestration financially viable instead of prohibitively expensive. One limitation: Fugu is not yet available in the EU/EEA while Sakana works toward compliance. Why this is a bigger product than it looks like At first glance, Fugu sounds like a very good router, but that description undersells it. The deeper idea is that model orchestration itself is becoming a first-class capability. If that holds, the value is not only in better benchmark scores, but in turning a pile of expensive, specialized models into a single system that a team can use without hand-tuning workflows from scratch. The system is useful for real teams because it hides just enough complexity to make multi-model workflows practical. There is also a strategic angle. Relying on one provider for every critical task is a risk. A learned orchestration layer that can route around constraints, swap agents, or exclude a provider reduces that dependency. Sakana is clearly leaning into that idea. Where teamcopilot.ai fits teamcopilot.ai is a shared control layer for AI workflows, permissions, and approvals. That makes it a natural fit for a system like Fugu. If Fugu is the orchestration engine for a task, teamcopilot.ai is the governance layer around it. You can route work through reusable workflows, keep approvals visible, and decide who can do what before the model ever touches the task. Production AI requires making models safe, repeatable, and shareable across a team. Related reads: AI Agent Governance Is the New Enterprise Control Plane Why Your AI Agent Should Never See Your API Keys Best AI Agent Platforms for Teams in 2026: Comparing 13 Tools How to Use Claude Code with a Team: Shared Context, Permissions, and MCP The tradeoffs Fugu is impressive, but it has tradeoffs. Latency will always be part of the conversation when a system calls into multiple models or multiple agent steps. If you need instant responses for a live UI, a simpler single-model path may still win. The routing logic is also proprietary. Sakana does not expose the exact internal selection process, so you get the benefits of orchestration without full visibility into every decision. Additionally, while the standard Fugu allows opt-outs, Fugu Ultra uses the full agent pool. If you need strict control over every provider in the loop, that is worth keeping in mind. Still, these are normal tradeoffs for a new product category. The real test is whether the system earns that complexity back with better results. The bigger takeaway Fugu is a sign that the market is moving from single-model thinking to system thinking. That change is easy to miss if you only look at raw benchmark numbers, but the product story is clear. Sakana AI is betting that the most useful AI systems will be coordinated pools of models, with a learned layer deciding how to use them. Many teams are already heading in this direction manually, and Fugu simply makes the orchestration layer explicit. FAQ What is Sakana Fugu? Sakana Fugu is a multi-agent orchestration system presented as a single model API. It coordinates a pool of frontier models instead of relying on one model to do everything. Is Fugu a model or a product? It is both. Sakana exposes it as a model API, but the real value is in the orchestration system behind it. What is the difference between Fugu and Fugu Ultra? Fugu is the balanced, lower-latency option. Fugu Ultra uses a deeper agent pool for harder, higher-stakes tasks where quality matters more than speed. How does Fugu work? It routes tasks across multiple specialist models, assigns roles, and coordinates the response. The research behind it comes from TRINITY and Conductor. Why not just call one frontier model directly? Because different models excel at different tasks. Fugu decides when to delegate, verify, or switch strategies instead of making one model carry the whole load. Can I control which models Fugu uses? Yes, for Fugu. Sakana lets you opt out of specific models or providers to fit privacy, data, or compliance needs. Fugu Ultra uses the full pool. Is Fugu OpenAI-compatible? Yes. It fits into existing clients and agent stacks without requiring a major integration rewrite. What tasks is Fugu best for? Coding, reasoning, research, security analysis, paper reproduction, and other multi-step workflows where orchestration matters. Is Fugu good for real-time apps? Not necessarily. The more agents you coordinate, the more latency becomes a factor, so it may not be ideal for instant responses. Does Fugu show which underlying models it used? No. Sakana treats the exact routing logic as proprietary. Can teams use Fugu safely? Yes, if the surrounding workflow is controlled. Approval layers, audit trails, and secret handling are essential for making any model safe and useful in a team setting. Why should teams care about orchestration at all? Because orchestration is where real productivity wins happen. Choosing the right model for the right subtask can matter as much as choosing the model itself. Where does teamcopilot.ai fit in? teamcopilot.ai provides a shared control layer for AI workflows, permissions, and approvals, making it easy to run systems like Fugu inside a governed, reusable process. Will Fugu replace single-model workflows? Not entirely. Simple tasks are still better served by a single call, but harder workflows that benefit from delegation and verification will increasingly rely on systems like Fugu.

19 hours ago