What did Meituan release, and when?
On November 2, 2026, Meituan launched LongCat-2.5, which it presents as a preview. TechNode reported the launch the same day, describing a model that adds stronger visual understanding and coding capabilities and is designed for tasks requiring sustained execution by AI agents. Meituan's own launch post on X gives the headline numbers: 1.6 trillion total parameters, about 48 billion active, a 1M-token context window and native multimodality.
Those numbers matter because they describe the shape of the model, not just its size. A mixture-of-experts design with roughly 48 billion parameters active per token is meant to keep per-token compute closer to a much smaller model while holding a very large total capacity. Meituan's post pitches the 1M-token window as the enabler for long jobs, which is exactly where most agent projects fall apart.
A large model is a stack of parts, and the stack only works if every layer holds its weight.What is the model built to do?
Meituan's announcement describes long-horizon work, running from terminals and browsers to GUIs, spreadsheets and design tools. TechNode adds that training used large-scale reinforcement learning on long task sequences and complex codebases, and that failures observed in real agents are converted into reproducible training tasks. On the input side, text and visual content share a unified token framework, which TechNode ties to visual coding, creative design and 3D tasks.
In practice, that means the pitch is less about answering a single question and more about carrying a task across several tools without losing the thread. Anyone who has run an agent past its third or fourth step knows where that thread usually snaps. Our earlier piece on what breaks in long-running agents after step three covers the state problems that show up first, and a bigger context window does not fix them on its own.
What is still unknown?
Three things are missing from the coverage we could verify. The first is open weights: TechNode says GitHub and Hugging Face releases are planned to follow, which means the model was not yet downloadable at launch. The second is benchmarks: we did not find verified scores that compare LongCat-2.5 against other models, so we are not repeating any. The third is pricing and license terms, which we also could not confirm from a primary source.
A preview can change before general availability, so treat the specs above as the starting point for a test, not a commitment you should build around.
What does this mean for solo operators and marketers?
A long context window is useful if your work really is long. A solo operator with a folder of research notes, a year of campaign data or a backlog of product feedback can benefit from a model that reads it in one pass. The risk is that a large window invites you to hand over more and check less. Keep your human approval gates on anything that publishes, spends money or touches customer data.
On cost, the usual solo-operator question applies. Not every step in a pipeline needs the biggest model, and our piece on when the cheap model is the right call is the framework to use before you route work to a 1.6-trillion-parameter system. If your agents already drive browsers or desktop apps, the comparison worth running is against the computer-use tools you have today, such as the ones covered in Holo4 for solopreneurs and in OpenAI's cheaper computer-use agents.
Our take
I have spent twenty-five years in digital marketing, and I have watched the phrase "long context" go from a research footnote to a sales pitch in about three product cycles. A preview model is like a food-truck menu: the specs look exciting, and you should still ask what is in the sauce before you order for the whole office.
My opinion is that Meituan's launch is worth a proper trial and not a rebuild. The 1M-token window is a real asset for anyone who keeps messy archives, and the agent-failure framing in the training description is the right instinct. Still, the only benchmark that matters to your business is one you run yourself, on a task you already know the correct answer to. Pin your current model, route one low-risk workflow to LongCat-2.5, and let the results decide whether it earns a place in the stack. Wait for the weights before you assume anything about self-hosting, because a "planned" release has a way of arriving on its own schedule.
Sources
Meituan LongCat, launch post on X: https://x.com/Meituan_LongCat/status/2103488918788411728
TechNode, "Meituan launches LongCat-2.5 multimodal AI model for extended coding tasks," November 2, 2026: https://technode.com/2026/11/02/meituan-launches-longcat-2-5-multimodal-ai-model-for-extended-coding-tasks/