美团 LongCat 推出旗舰模型 LongCat-2.0，采用 1.6T 参数 MoE 架构（约 48B 活跃参数），原生支持 1M 上下文窗口。定价为 Input Cache $0.015/1M tokens、Input $0.75/1M tokens、Output $2.95/1M tokens。模型专为 Agentic Coding 设计，包含三大技术：LSA 稀疏注意力实现高效 1M 扩展；Zero-Compute Experts 动态激活 33B–56B 参数/token，无算力浪费；MOPD 将专家分为 Agent / Reasoning / Interaction 三组，按任务门控路由。在 SWE-bench Pro 上取得 59.5 分，性能接近主流闭源模型。现已上线 SiliconFlow Day 0 服务。
美团龙猫的 LongCat-2.0 专为 agentic coding 设计的 MoE 模型，架构上三种专家分工有点意思，SWE-bench 59.5 接近闭源水平，已经能在硅基流动上直接调，做 coding agent 的可以跑跑看。
The full model behind "Owl Alpha" on @OpenRouter is here🦉
Let's meet @Meituan_LongCat 's latest flagship model, LongCat-2.0
Now Day 0 live on SiliconFlow 🔥
💰 Input Cache/Input/Output: $ 0.015/0.75/2.95 per 1M tokens
⚙️ 1.6T-param MoE (~48B active) · Native 1M context window
🧠 Built for agentic coding from the ground up:
◆ LSA: sparse attention that scales efficiently to 1M
◆ Zero-Compute Experts: dynamic 33B–56B active/token, no wasted compute
◆ MOPD: three specialized expert groups (Agent / Reasoning / Interaction), gate-routed per task
🏆 59.5 SWE-bench Pro: performance on par with mainstream close-sourced models
Start building with 🐱👇
来源：SiliconFlow · x.com