longcat-2.0

LongCat 2.0 is a sparse Mixture-of-Experts (MoE) model designed specifically for high-complexity engineering workflows. While it boasts a massive 1.6T total parameter scale, its architectural efficiency shines through 48B active parameters, balancing high-reasoning capabilities with manageable compute requirements. What sets this model apart for developers is its massive 1M+ token context window, which moves beyond simple chat interactions into true repository-level intelligence. It is engineered for tasks that demand long-horizon planning, such as executing multi-step agentic workflows, refactoring entire codebases, and maintaining coherence across massive documentation sets. For teams building autonomous coding agents or deep-context RAG pipelines, LongCat 2.0 provides the structural depth needed to handle dependencies and logic that standard dense models often lose in long-sequence processing.