Tokyo AI startup founded recently by Llion Jones, co-author of “Attention Is All You Need” Sakana AI Released, claiming it can compete with Claude Fable 5 and Mythos Preview have comparable capabilities Sakana FuguSimply put, Sakana Fugu Not a new foundation model, but rather a “multi-model orchestration system” that has “learned how to schedule other models.” When a user asks a question, the Fugu system behind the scenes automatically decides whether to answer directly or to form a team by calling on multiple top-tier models like Claude, GPT, and Gemini to collaborate on completing the task. Sakana Fugu itself is a trained language model specifically designed to learn when to delegate tasks, how to get different models to cross-verify each other, and how to integrate outputs from multiple models into a more reliable answer. The interface users experience is identical to a standard chat API, but underneath it’s a collaborative team composed of multiple expert models.
Why do we need a “model scheduler”?
Sakana AI’s core argument is: no matter how powerful a single model is, it has blind spots. GPT excels at structured reasoning, Claude has advantages in long-context and nuanced language processing, and Gemini performs exceptionally well in certain knowledge retrieval tasks. Since each model has its own strengths, the true best performance should come from “collective intelligence” – knowing when to use which model, how to divide tasks, and how to integrate their respective strengths.
Previously, developers had to build their own multi-model dispatch framework to achieve this: managing multiple API keys, designing routing logic, writing validation processes, and handling retries on failures. Fugu encapsulates all this complexity behind a single API. Users call one endpoint, and Fugu automatically handles model selection, task delegation, result validation, and final synthesis internally.
The academic foundation of this approach comes from two papers published by Sakana AI at ICLR 2026:TRINITY(Evolutionary LLM Orchestrator) and Conductor(Using reinforcement learning to enable models to learn to coordinate multiple agents in natural language.) TRINITY automatically assigns Thinker (thinking), Worker (execution), and Verifier (verification) roles to different models; Conductor uses reinforcement learning to let models discover the most efficient agent communication patterns on their own.[3]
Fugu vs Fugu Ultra: Which Version Should You Choose?
Sakana Fugu provides two versions that share the same API endpoint:
- FuguSuitable for everyday use, balancing performance with low latency. Ideal for chatbots, code review, interactive services, and similar scenarios. Pricing is dynamic, based on the highest-tier model used, without stacking fees across multiple models.
- Fugu UltraOptimized for complex, multi-step tasks, coordinating a deeper pool of models to handle high-difficulty problems. Ideal for deep code development, scientific reasoning, multi-step analysis, and similar use cases. Billed at a fixed rate.
Both support allowing users to exclude specific vendors or models to meet data privacy, compliance, or organizational policy requirements.
Pricing: more flexible than you think, but not cheap either
The API pricing for Fugu Ultra is as follows (calculated per million tokens):
- Input: $5 (approx. NT$163)
- Output: $30 (approximately NT$975)
- Cache Input$0.50 (approximately NT$16)
- When the context exceeds 272K tokens: Input $10 (approximately NT$325), Output $45 (approximately NT$1,463)
- Context window: 1,000,000 tokens
- Maximum Output: 128,000 tokens
A key detail: Fugu Ultra’s billing includes “orchestration tokens” — tokens consumed when internal models communicate with each other. These tokens are charged at the same rate as standard input/output tokens but won’t appear in the final response you receive. In other words, the internal collaboration you can’t see is also being billed.
Sakana also offers a subscription plan, perfect for individual users:
- Standard: $20/month (approximately NT$650), basic allowance
- Pro$100/month (approximately NT$3,250), 10x quantity
- Max$200/month (approximately NT$6,500), 20× the quantity
Official limited-time offer: Subscribe by July 31, 2026, and get the second month free. Fugu Ultra included.Use via OpenRouterThe rates are the same.
Benchmark performance: Official figures look impressive, but haven’t been verified by third parties yet.
According to data officially released by Sakana, Fugu Ultra demonstrates excellent performance across multiple benchmark tests:
- SWE-Bench Pro(Software Engineering Capability): Fugu Ultra at 73.7%, ahead of Claude Opus 4.8 at 69.2%, GPT-5.5 at 58.6%, and Gemini 3.1 Pro at 54.2%
- GPQA-D(Scientific Reasoning): 95.5 points
- LiveCodeBench v6(Real-time coding ability): 93.2 points
- TerminalBench 2.1Terminal Operation Ability: 82.1 points
Sakana officially claims that Fugu Ultra is on par with Anthropic’s Fable 5 and Mythos Preview, but with one important caveat: due to US export controls, Fable 5 and Mythos are currently not publicly available, and therefore cannot be included in Fugu’s model pool. Sakana positions this as one of Fugu’s core selling points—when any single provider restricts access, Fugu can automatically bypass and switch to another model to continue operating.
It should be emphasized that all the benchmark data above was published by Sakana itself and has not yet been verified by independent third-party labs. Reddit’s r/Analysis of the ClaudeAI communityIt also reminds readers that, before independent reviews come out, these figures should be regarded as the manufacturer’s own marketing hype.
Community Test: Saves time and money, but quality doesn’t always win
Benchmarks are, after all, lab data—what developers really care about is real-world experience. Developers on the X/Twitter platform have also conducted some interesting real-world tests and comparisons:
Benchmark 1: Crossy Road Game Development
User @markksantos gave the same prompt “Build a high-quality Crossy Road game with Three.js” and tested it with both Fugu Ultra and Claude Opus 4.8:
- Fugu UltraUsed approximately 89K tokens, costing $7.32 (≈ NT$238), took 22 minutes. Issues: inverted direction controls, shaky camera, no sound effects.
- Claude Opus 4.8Used approximately 940k tokens, costing $37.85 (about NT$1,230), taking 79 minutes. Issues: fell into retry loops twice, character position errors.
SAKANA FUGU ULTRA vs. CLAUDE OPUS 4.8 RESULTS
Prompt: “build a really high quality single html file crossy road game with three.js”
Sakana Fugu Ultra:
– Tokens Used: ~89k ($7.32)
– Time Elapsed: 22 minutes
– Issues: inverted directional turn, wonky camera, no sfx, not… pic.twitter.com/2riS4pEds4— Mark Santos (@markksantos) June 22, 2026
Conclusion: Opus excels in output quality and design, but Fugu significantly outperforms in speed and cost. The token usage gap exceeds 10x, with a time difference of nearly 4x.
Live Test 2: Real-time Trading Platform Development
User @atomic_chat_hq Use the same prompt to have four models build a complete real-time trading dashboard (including frontend and backend, real-time quotes for 8 securities, dark theme UI):
- Fugu Ultra22,225 tokens, $0.51 (approximately NT$17), the most refined interface and most complete features
- Claude Opus 4.8: 15,802 tokens, $0.31 (approximately NT$10)
- GPT-5.511,474 tokens, $0.26 (approximately NT$8)
- GLM 5.2: 13,677 tokens, $0.03 (about NT$1), quality very close to Fugu
Sakana Fugu surprisingly performed near GLM 5.2 level but 17× more expensive!
We gave the same prompt to 4 models: build a complete live Trader Desk with both frontend and backend components, real-time market data fetched from external APIs for 8 symbols, and a custom dark-theme… https://t.co/CpFJT7ofy9 pic.twitter.com/SIfLwi8KYa
— atomic.chat (@atomic_chat_hq) June 22, 2026
Fugu produced the most refined results in this test, but GLM 5.2 achieved nearly the same quality at less than 1/17 of the price.
Sakana Fugu: Positioning and Limitations – Who It’s For and Who It’s Isn’t
From the above information, the applicable scenarios and limitations of Fugu can be summarized as follows:
Suitable scenarios:
- Complex, multi-step code development and review tasks require a complete workflow of planning, execution, and verification.
- Reasoning tasks that require cross-model collective intelligence to enhance accuracy
- Don’t want to be locked into a single model provider, need a flexible model pool strategy
- Professional development teams with ample budgets and a drive for the highest quality
Scenarios that may not be suitable:
- For simple conversations or lightweight tasks, scheduling overhead becomes wasteful.
- For highly cost-sensitive users, Chinese models like GLM 5.2 offer far better value for money on certain tasks compared to Fugu
- In compliance scenarios requiring complete transparency in the reasoning process, Fugu’s internal scheduling logic is not available to external users.
- Real-time applications requiring minimum latency
Simply put, Fugu has three potential risks: First, the scheduling overhead for simple tasks is pure waste. Second, if multiple top-tier providers simultaneously restrict access, the model pool shrinks and quality degrades—routing isn’t the same as sovereignty. Third, the hidden orchestration layer prevents users from knowing which agents participated in decisions and what data each of them saw, which is a problem for work that requires auditing.
Conclusion
Sakana Fugu represents a different approach in the AI industry, one that stands in stark contrast to “brute-force scaling”. While most companies are still pouring billions of dollars into training larger single models, Sakana has chosen to let a relatively small model learn how to orchestrate other powerful ones. This strategy certainly has its appeal, especially given that Anthropic’s Fable 5 and Mythos are unavailable due to export restrictions.
However, real-world testing also shows that Fugu’s “savings” come with conditions. On simple tasks, the Chinese model GLM 5.2 achieves comparable quality at 1/17 the price; on complex tasks, Claude Opus 4.8 still produces higher-quality results. Fugu’s value lies in offering an option that “doesn’t fall too far behind, but is safer,” especially for enterprise users who don’t want to be locked into a single vendor.
Source: KOCPC Chinese