Meituan releases LongCat-2.0: a 1.6T-parameter model trained on 50,000 domestic GPUs, now open source

美团 LongCat-2.0 正式发布：国产算力集群训练的万亿参数大模型

Meituan open-sourced LongCat-2.0, a 1.6T total-parameter model with ~48B activated per inference and native 1M context. It was trained and served entirely on a 50,000-card domestic GPU cluster. The architecture combines LSA sparse attention, zero-compute experts, ScMoE, and MOPD multi-expert fusion that blends Agent, Reasoning, and Interaction expert groups. SWE-bench Pro hits 59.5, Multilingual 77.3. A preview is live on OpenRouter and longcat.ai, already ranking top three globally in monthly calls on OpenRouter. The post doesn't disclose training cost, inference latency, or the specific domestic chip model, so I'd hold off on those details.

Why it matters: Meituan's trillion-param model trained end-to-end on domestic GPUs is the headline; code benchmark scores are solid. Not scoring higher because Meituan isn't a tier-1 model lab yet, and real-world usability depends on the open-source release.