Meituan's LongCat-2.5 Is Built for Agents That Do the Work, Not Just Chat

LongCat-2.5 in One Paragraph

Meituan released LongCat-2.5 on November 2, 2026, and the headline numbers tell you who it is for. The model totals 1.6 trillion parameters with about 48 billion active per token, it handles text and vision natively, and it offers a 1 million token context window with up to 128,000 tokens of output. Pricing on Meituan's own platform is listed at 0.30 dollars per million input tokens and 1.20 dollars per million output tokens, a limited-time discounted rate. The pitch is not a better chatbot. It is a model built for agent execution and extended programming workflows, jobs where the model has to hold a huge amount of context, take many steps, and keep going without losing the thread.

That framing matters because the agent race has shifted. The question is no longer which model writes the prettiest paragraph. It is which model can be trusted with a multi-hour job: reading a codebase, running tools, checking its own work, and finishing without a human babysitting every step. LongCat-2.5 is Meituan's answer to that question, and its size and context window are designed for exactly that kind of slog.

LongCat-2.5 Agent Model Specs, in Plain Terms

A mixture-of-experts design with 48 billion active parameters per token means the model only wakes up a slice of itself for each token, which keeps inference costs sane despite the huge total size. The 1 million token context is the more practical number. It lets an agent load a large repository, a long set of documents, or a full session history in one go, instead of constantly re-reading or summarizing what it already saw. For programming workflows, that is the difference between an agent that remembers the whole project and one that keeps asking you to repeat yourself.

The 128,000 token output limit matters too. Long coding jobs, multi-file refactors, and detailed agent plans are long outputs. A model that stops early forces you to stitch pieces together by hand, which defeats the point of handing the job to an agent. LongCat-2.5 is clearly built to avoid that failure mode. Whether it succeeds in practice will depend on real-world tests, not spec sheets, and early adopters should measure it on their own repos before committing.

Open Weights Are Coming, but Not Yet Here

One caveat deserves a plain statement: the weights are not open yet. Meituan says GitHub and Hugging Face releases are planned to follow, but until they land, LongCat-2.5 is an API model, not a model you can run yourself. That distinction matters for teams that need local deployment, fine-tuning, or data residency guarantees. If your use case requires self-hosting, wait for the weights and check the license before building on them.

The pricing, at least on paper, is aggressive. At 0.30 dollars per million input tokens, long-context agent work becomes affordable to experiment with, though the discounted label means you should not budget on that price forever. Compare the full cost of an agent job, including retries and tool calls, not just the headline token price. Cheap tokens that produce wrong answers are still expensive.

How LongCat-2.5 Fits the November Model Wave

LongCat-2.5 arrives in a crowded month. November's tracker already lists it alongside Nace AI's Drex Max, and recent weeks brought Mistral's trillion-parameter Large 4 and Google's Gemini 4 family. The pattern is consistent: vendors are racing to serve agents, not just chat users, and context windows keep growing because agent jobs demand them. Meituan's entry is notable because it comes from a company known for food delivery and local services, not a pure AI lab, which says something about how broadly the agent bet has spread.

For teams evaluating models this month, the practical checklist is short. Test LongCat-2.5 on a real multi-step task from your own work, not a demo. Measure cost per completed job, not per token. And check whether the coming open release fits your deployment needs. The DeepSeek open source coding agent showed how quickly an open agent tool can spread, while GitHub's agent workflows showed how agents are moving into everyday developer tooling. LongCat-2.5 will be judged the same way: not by its parameter count, but by the jobs it actually finishes.