Meituan open-sources LongCat-2.0, a 1.6T MoE model with 48B active params, trained entirely on AI ASIC superpods

LongCat-2.0, a large-scale MoE model with 1.6T total and 48B Active

Meituan open-sourced LongCat-2.0, a 1.6-trillion-parameter MoE model with ~48B active parameters per token. It was pretrained on over 35 trillion tokens using 50K+ in-house AI ASICs with no rollbacks or irrecoverable loss spikes, showing frontier-scale training is viable on non-GPU hardware. The model targets long-context and agentic workloads: it introduces LongCat Sparse Attention to speed up 1M-token processing and was trained on hundreds of billions of 1M-context tokens. Official charts place it alongside Gemini 3.1 Pro, GPT-5.5, and Opus 4.8 on Terminal-Bench 2.1, SWE-bench Pro, and other coding/agent benchmarks, though the post does not provide exact numeric comparisons. An N-gram Embedding module with 135B parameters expands the embedding space roughly 100×, which the team claims outperforms scaling standard MoE experts by the same amount. The model is integrated with Claude Code, OpenClaw, and Hermes; code and weights are available on GitHub and HuggingFace.

Why it matters: Meituan open-sources a 1.6T MoE model trained entirely on in-house AI ASICs across 50k+ cards with zero rollbacks, plus dedicated long-context and agent optimizations. Score held at 82 rather than higher because we only have the official blog post — no third-party evals or rea...