LongCat
Frontier reasoning models from Meituan
172 followers
Frontier reasoning models from Meituan
172 followers
LongCat is the AI model series from Meituan, featuring powerful, efficient, and open-source models for complex tasks. Its latest release, LongCat-Flash-Thinking, is a 560B parameter MoE reasoning model that sets a new standard for speed and performance.
This is the 2nd launch from LongCat. View more
LongCat-2.0
Launching today
LongCat-2.0 is an MIT-licensed 1.6T-parameter MoE model with ~48B active parameters, 1M context, LongCat Sparse Attention, and post-training for coding and agentic workflows. It was trained on AI ASIC superpods and integrates with Claude Code, OpenClaw, and Hermes.
Free
Launch Team
How does the 560B MoE setup actually perform on smaller hardware for fine tuning, or is it really only practical to run through Meituan's own infrastructure?
Tried LongCat-Flash-Thinking on a multi-step coding prompt and was surprised how fast it reasoned through edge cases without losing track. Solid open-source move from Meituan.
Ran it through some tricky math problems and the responses came back noticeably quick for a model this size. Curious to see how the open weights hold up on longer agentic workflows.
Finally got around to testing LongCat-Flash-Thinking on some multi-step coding problems and the speed honestly caught me off guard, especially for a 560B MoE. The reasoning chains feel surprisingly tight too.
Pulled LongCat-Flash-Thinking for some debugging yesterday and the response time genuinely caught me off guard. Reasoning feels sharp without the usual lag.
finally a chinese-built model that actually feels fast on long reasoning chains. really impressed how it handled a math-heavy prompt without losing the thread halfway through.
LongCat handled a tricky multi-step coding prompt way faster than I expected, and the reasoning chain felt genuinely clear instead of just confident. The MoE setup seems to pay off in practice.