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@Meituan_LongCat

Introducing LongCat-2.0 ๐Ÿฑ 1.6T parameters ยท MoE with ~48B active ยท 1M context The full model behind Owl Alpha on @OpenRouter โ€” now available. Built for agentic coding from the ground up: โ—† LongCat Sparse Attention (LSA) โ€” scales efficiently for 1M-context tokens โ—† Zero-Compute Experts โ€” dynamic activation 33Bโ€“56B per token, zero wasted compute โ—† MOPD โ€” three specialized expert groups (Agent / Reasoning / Interaction), gate-routed per task How it stacks up: โ†’ Terminal-Bench 2.1: 70.8 โ†’ SWE-bench Pro: 59.5 (GPT-5.5: 58.6) โ†’ SWE-bench Multilingual: 77.3 โ†’ FORTE: 73.2 ยท RWSearch: 78.8 ยท BrowseComp: 79.9 ๐Ÿ“– Tech Blog: https://t.co/4KrjyKiDBn Try it across different scenarios ๐Ÿงต๐Ÿ‘‡

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  "text": "Introducing LongCat-2.0 ๐Ÿฑ\n1.6T parameters ยท MoE with ~48B active ยท 1M context\nThe full model behind Owl Alpha on @OpenRouter โ€” now available.\n\nBuilt for agentic coding from the ground up:\nโ—† LongCat Sparse Attention (LSA) โ€” scales efficiently for 1M-context tokens\nโ—† Zero-Compute Experts โ€” dynamic activation 33Bโ€“56B per token, zero wasted compute\nโ—† MOPD โ€” three specialized expert groups (Agent / Reasoning / Interaction), gate-routed per task\n\nHow it stacks up:\nโ†’ Terminal-Bench 2.1: 70.8\nโ†’ SWE-bench Pro: 59.5 (GPT-5.5: 58.6)\nโ†’ SWE-bench Multilingual: 77.3\nโ†’ FORTE: 73.2 ยท RWSearch: 78.8 ยท BrowseComp: 79.9\n\n๐Ÿ“– Tech Blog: https://t.co/4KrjyKiDBn\nTry it across different scenarios ๐Ÿงต๐Ÿ‘‡",
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