美团 LongCat 推出 LongCat-2.0，基于 MoE 架构，总参数 1.6T，激活参数约 48B，支持 1M 上下文。模型专为智能体编码设计，包含 LongCat 稀疏注意力（LSA）、零计算专家（33B–56B 动态激活）及 MOPD（三组任务路由专家）。基准测试：Terminal-Bench 2.1 达 70.8，SWE-bench Pro 59.5（超 GPT-5.5 的 58.6），SWE-bench Multilingual 77.3，FORTE 73.2，RWSearch 78.8，BrowseComp 79.9。目前已通过 OpenRouter 的 Owl Alpha 开放使用。
Introducing LongCat-2.0 🐱
1.6T parameters · MoE with ~48B active · 1M context
The full model behind Owl Alpha on @OpenRouter — now available.
Built for agentic coding from the ground up:
◆ LongCat Sparse Attention (LSA) — scales efficiently for 1M-context tokens
◆ Zero-Compute Experts — dynamic activation 33B–56B per token, zero wasted compute
◆ MOPD — three specialized expert groups (Agent / Reasoning / Interaction), gate-routed per task
How it stacks up:
→ Terminal-Bench 2.1: 70.8
→ SWE-bench Pro: 59.5 (GPT-5.5: 58.6)
→ SWE-bench Multilingual: 77.3
→ FORTE: 73.2 · RWSearch: 78.8 · BrowseComp: 79.9
📖 Tech Blog: https://longcat.chat/blog/longcat-2.0/
Try it across different scenarios 🧵👇
来源：Meituan LongCat · x.com