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 translate to faster inference in practice compared to dense models of similar size, and is it open weights under a permissive license?
Curious how this stacks up against DeepSeek or Qwen on coding benchmarks, and is the 560B MoE actually deployable on a single node or do you need serious infra to run it locally?
How does the 560B MoE setup compare on inference cost versus a dense model of similar capability, and is there any self-hosting guidance for teams without massive GPU budgets?
how does the 560B MoE setup handle latency on longer reasoning chains, and is there a hosted endpoint or is it self-host only?
Curious how this stacks up against other open-source MoE models on benchmarks like HumanEval or MATH, and is it actually free to deploy commercially or are there usage limits built in?
Curious how the 560B MoE setup actually feels in latency for real time stuff like chat compared to something like DeepSeek, and what kind of hardware you'd realistically need to run a distilled version locally?
how does the 560B MoE setup hold up on longer context tasks compared to dense models like DeepSeek?