Sakana Fugu Ultra vs Claude Opus 4.8: 38-Task Battle Test

Key Takeaways

• Fugu is not a new LLM. It's a small "manager" model that breaks a task down and routes sub-tasks to frontier models (Opus, GPT-5.5, Gemini, and others), then has another model merge the results — a multi-agent system delivered as one API.
• It runs inside Claude Code via a markdown config file plus an API key. Notably, the context window stays near zero through a long session because responses are routed through Fugu's server rather than filling Claude Code's own context.
• The scoreboard: across 38 AI-generated, Codex-graded, mostly pass/fail tasks (puzzles, traps, specs, heavy algorithms), 36 ended in ties and Opus won 2. Fugu never clearly won — unsurprising, since Opus 4.8 is one of the models Fugu itself selects from.
• Cost and speed are the story: Fugu's runs took 357 minutes total vs Opus's 80 minutes, and cost ~$50 vs ~$10 — about 4.5× slower and 5× pricier. Easy tasks Opus answered in ~6 seconds took Fugu several minutes.
• The pattern isn't new. It's the same orchestration you already do pairing Claude Code sub-agents, or running Codex and Claude Code on one codebase — Fugu just automates the delegation. It differs from OpenRouter's Fusion API, which fans the same prompt to three models and judges/merges rather than splitting the task.
• Honest takeaway: impressive benchmarks, but for knowledge work the cost and latency aren't worth it over a Claude Code or Codex subscription. The real value is for heavy, multi-team software development — and the broader skill of optimizing which model does which task is only getting more important.