Meituan Releases LongCat-2.0: A 1.6T-Parameter Open MoE Model with Native 1M Context and LongCat Sparse Attention
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&#
A company released an open large language model with 1.6 trillion total parameters designed specifically for software engineering tasks like code generation and debugging. The model can process 1 million tokens of context at once through a technique called sparse attention that reduces computational cost from quadratic to linear scaling. Training and serving happened entirely on domestic AI chips rather than Nvidia hardware, demonstrating capability on non-standard infrastructure. According to vendor benchmarks, it scores 59.5 on real-world software engineering tasks and 70.8 on shell execution benchmarks, positioning it as competitive with other frontier models in coding-focused work.
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Also reported by The Verge, SiliconANGLE and MarkTechPost
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Also reported by SiliconANGLE
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