Meituan has released LongCat-2.0, a large-scale Mixture-of-Experts (MoE) language model. It carries 1.6 trillion total parameters and activates about 48 billion per token. The model targets agentic coding: code understanding, generation, and execution inside agent workflows.
Two facts stand out. First, LongCat-2.0 supports a native 1-million-token context window. Second, both training and serving ran entirely on domestic AI ASIC superpods.
What is LongCat-2.0?
LongCat-2.0 is Meituan’s next-generation trillion-parameter open model. It follows LongCat-Flash, a 560B model released in 2025. The architecture was designed around one goal: reliable, efficient agentic coding.
Pretraining spanned more than 35 trillion tokens over millions of accelerator-hours. Meituan reports no rollbacks or irrecoverable loss spikes during the run. That stability claim matters on non-Nvidia hardware, where tooling is less mature.
