Sakana AI has released Fugu-Cyber：https://sakana.ai/fugu-cyber-release/ (model ID is fugu-cyber-v1.0 ), a cybersecurity-specialized addition to its Fugu orchestration family. It is not just a new frontier model. It is a third endpoint on the Fugu orchestrator：https://sakana.ai/fugu/, tuned for security reasoning. Sakana launched that orchestrator a month earlier：https://www.marktechpost.com/2026/06/22/sakana-ai-launches-sakana-fugu-an-orchestration-model-that-routes-tasks-across-a-swappable-pool-of-frontier-llms/.
Sakana reports a success rate of 86.9% on CyberGym and 72.1% on CTI-REALM. It describes those results as comparable to cyber-focused frontier models such as GPT-5.5-Cyber and Claude Mythos Preview.
The two evaluations sit at opposite ends of a security workflow :
Together the pair spans ‘find and prove the bug’ and ‘turn intel into a detection.’ That framing is the most defensible part of Sakana’s announcement.
Context matters more than the number.
When the CyberGym researchers published：https://arxiv.org/abs/2506.02548 their first results, the best agent-model pairing reached roughly 20%. Anthropic reported 83.1% for Claude Mythos Preview under Project Glasswing：https://www.anthropic.com/glasswing in April 2026. OpenAI reported 85.6% for its updated GPT-5.5-Cyber：https://openai.com/index/gpt-5-5-with-trusted-access-for-cyber/, against 81.8% for GPT-5.5. Sakana’s 86.9% is therefore a small step past the reported frontier, not a jump.
CTI-REALM is a different story. Microsoft’s own evaluation put the top three configurations, all Claude, in a band from 0.624 to 0.685. Fugu-Cyber’s 72.1% would sit above that band. One caveat matters. CTI-REALM is scored as a trajectory reward between 0 and 1. It is not a pass/fail rate. Sakana calls it a success rate anyway.
Fugu is itself a language model. It is trained to read a query and build an agentic scaffold on the fly. It then delegates sub-tasks to specialist models in a pool.
The approach is documented in the Fugu technical report：https://arxiv.org/abs/2606.21228 and two ICLR 2026 papers, TRINITY：https://arxiv.org/abs/2512.04695 and the Conductor：https://arxiv.org/abs/2512.04388. TRINITY assigns Thinker, Worker, and Verifier roles across multiple LLMs. The Conductor learns natural-language coordination strategies through reinforcement learning.
For security work, Sakana research team argues the verifier role is the point. A candidate vulnerability surfaced by one agent gets validated by security-specialized sub-agents before any patch is proposed. Routing remains proprietary, so you cannot see which model handled which step.
Fugu-Cyber is gated on four dimensions.
Access requires an application form stating the intended use case and verified contact details. Sakana team reviews each one manually. The model ships under an updated Acceptable Usage Policy that prohibits offensive misuse. Billing is restricted to the Token Plan. The $20, $100, and $200 subscription tiers cover Fugu and Fugu-Ultra only. And the Fugu API is not offered in the EU or EEA while Sakana works toward GDPR compliance.
Pricing is fixed at $6 per million input tokens, $36 output, and $0.60 cached input. All three rates double above a 272K-token context. Every line is exactly 1.2× the Fugu-Ultra rate, a flat 20% premium for the cyber endpoint. Long codebase runs cross 272K easily, so the doubled tier is not an edge case.
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