What Meituan announced
What if an AI model could look at an image and help work through several stages of a software project? That is the kind of task Meituan is targeting with LongCat-2.5.
Meituan’s 2 November announcement describes a model with 1.6 trillion total parameters, the adjustable values learned during training. That headline number alone does not tell us how fast or capable the model will be on a particular task. The company says training used Chinese-built computing infrastructure.
The official documentation describes LongCat-2.5 as multimodal: it can process visual information alongside text. The company highlights visual coding, creative design, 3D work and longer software tasks involving tool use. An AI agent is software that can use tools and follow several steps toward a goal. These are provider descriptions, not guarantees for every project.
The LongCat API changelog lists availability from 2 November and provides integration guidance for tools including Codex and Claude Code. An API lets software communicate with a model. The model parameters page lists a one-million-token context window: a limit on the amount of information a request can process, measured in small data units called tokens.
Access and evidence limits
The source video also gives benchmark rankings and a figure of roughly 48 billion active parameters. We have not independently confirmed those details against the official announcement and documentation reviewed here, so they should not be treated as established comparisons. We have not verified the availability of downloadable weights on GitHub or Hugging Face. Check the provider’s current access, pricing and terms before use.
The useful question is practical: how well does it move from an image to a working result under human supervision? A fair answer needs comparable tasks and test conditions.
