Sakana Fugu Ultra 是一个多智能体协调层，通过 OpenAI 兼容端点将子任务路由给不同模型。在 @atomic_chat_hq 的实时交易桌面编码测试中（要求构建完整前后端、实时 API 数据、暗色主题 UI），Fugu Ultra 生成了最丰富的多面板界面（含图表、状态标签等），效果接近 GLM 5.2，但成本达后者的 17 倍：Fugu Ultra 耗 22,225 tokens / $0.51，GLM 5.2 耗 13,677 tokens / $0.03。对比模型 Opus 4.8（15,802 t / $0.31）和 GPT-5.5（11,474 t / $0.26）在质量与成本平衡上表现更佳。
Sakana Fugu Ultra just beat the other models on visual polish in a live trading-desk coding test, got close to GLM 5.2, but at 17x the cost.
Test was done on atomic[.]chat, a desktop app that runs LLMs locally.
Fugu produced the richest interface, with multiple panels, watchlists, charts, tape-style activity, status labels, and a more finished product feel.
To note that Fugu Ultra is an orchestration layer that assembles and routes subtasks across a pool of models through one OpenAI-compatible endpoint.
So Fugu is a learned coordinator model inside a multi-agent system.
When you send a prompt, Fugu decides whether to answer alone or hand pieces of the job to other models, then it gathers the outputs and produces one final response.
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 UI. Outputs: Fugu Ultra — 22,225 t, $0.51 Opus 4.8 — 15,802 t, $0.31 GPT-5.5 — 11,474 t, $0.26 GLM 5.2 — 13,677 t, $0.03 Fugu created the most polished and feature-rich trading desk in the run. GLM 5.2 was very close behind, with a similarly complete multi-panel interface and live data, but at a much lower cost. Opus and GPT also performed well, delivering solid results with a better balance between quality and cost在 X 查看被引用的帖子
来源：Rohan Paul · x.com