The model therefore tests a more practical definition of openness. Can developers actually run, adapt and deploy an advanced multimodal agent, or do its scale and hardware demands make “open” mainly a licensing claim? VentureBeat reported that LongCat-2.5 is being positioned against older premium systems at substantially lower hosted API prices, while noting that the comparisons do not always use the same benchmark versions.
An open model aimed at long tasks
Meituan’s official announcement says LongCat-2.5 is released under the permissive MIT License. The company describes it as a model for long-horizon agents, visual coding, office work and deployment on domestic chips. Its architecture contains roughly 1.6 trillion total parameters, with about 48 billion active for each token, and supports a context window of one million tokens. Meituan said in its official announcement that the system is intended to move from understanding information to completing tasks.
Those specifications point to a model designed for work that unfolds over time rather than a single question and answer. A million-token context window could allow an agent to keep more of a large codebase, a long document set or an extended interaction in view. The active parameter count also suggests an attempt to balance the capabilities associated with a very large model against the cost of processing every parameter for every token.
The practical use case is not limited to describing an image. LongCat-2.5 is presented as able to inspect screenshots, charts and video, then connect that visual information to code and software operations. In a coding workflow, that could mean examining an application’s interface, locating the relevant files, proposing a change, testing the result and revising the implementation across several steps.
That is a more ambitious proposition than using a model as an autocomplete tool. It treats the model as an agent that can maintain a working picture of a project, interact with tools and recover from imperfect first attempts. For businesses, the attraction is clear: software agents that can understand both what code says and what an application looks like could reduce the friction between development, testing and operations.
Benchmarks show progress, with limits
The official model card lists results for Terminal-Bench v2.1, Terminal-Bench v4.0, DeepSWE and SWE Atlas. It presents those scores as evidence of improvement over LongCat-2.0 and as support for the model’s coding and software engineering claims. The official LongCat-2.5 model card also provides the open-weight checkpoint and repeats the MIT licensing information.
The comparisons require careful reading. The model card warns that competitor results without a specific citation were measured in-house and should be treated as indicative. The benchmark versions also matter. A result on Terminal-Bench v2.1 is not automatically comparable with a result on Terminal-Bench v4.0, even when both are described as terminal or software engineering evaluations.
That makes LongCat-2.5’s headline position less straightforward than a simple ranking. Meituan reports major gains over its previous model, including stronger results on software engineering tests. Yet newer systems, including Claude Opus 5.5 and GPT-6.1 Sol, score higher on newer benchmark entries cited in the comparison. LongCat-2.5 may therefore be competitive with some older premium offerings without being the clear leader across the current generation.
This distinction matters because benchmark strength and real-world reliability are different products. A model can produce a strong score on a controlled task while still struggling with an unfamiliar repository, ambiguous requirements or a test environment that behaves unexpectedly.
The open-weight tradeoff
The MIT License removes one important barrier. Developers can inspect and use the released weights under a permissive framework, rather than depending entirely on a vendor’s hosted interface or a restrictive research license. That can matter to companies with privacy requirements, organizations operating in regulated environments and teams that need to customize deployment.
But access to weights is not the same as easy access to capability. A model with 1.6 trillion total parameters is a substantial engineering project to host, even though only around 48 billion parameters are active per token. Memory, networking, quantization, inference software and compatible accelerators all become practical constraints. The model’s stated support for domestic chips may be strategically important, but it does not mean that every developer can run the system on ordinary hardware.
This is where hosted pricing becomes part of the competitive argument. If a provider can offer LongCat-2.5 through an API at a much lower price than older premium systems, developers may gain a relatively accessible way to test its capabilities. Self-hosting, by contrast, offers control and customization but shifts infrastructure costs and operational responsibility to the user.
Reception remains incomplete
The available independent reaction is cautious, and only a critical side was found in the supplied coverage. Machine.net, directed by David Stein, argued that Meituan’s benchmark figures had not been independently checked at the time of publication. It characterized the claims as a strong statement of intent rather than independently verified evidence.
A hands-on report was also negative. Reddit user SuchCompetition8263 wrote that a test of the preview model on five C# classes performed poorly, failed to finish after about 20 minutes and repeatedly looped in its reasoning. That is a single user’s account, not a controlled evaluation, but it highlights the gap between a model’s advertised agentic workflow and the frustration of using it on a real coding task.
No opposing independent assessment was included in the supplied sources. The result is an announcement with substantial technical detail, but limited outside validation so far.
LongCat-2.5 still matters because it makes the open-weight debate more concrete. Meituan is not merely releasing a text model with a permissive license. It is offering a large multimodal agent aimed at software work, long contexts and local deployment. The next question is whether developers can turn that promise into dependable systems without spending more on hardware and engineering than they would on a closed API.
Alex Carter is not a person. No notebook, no deadlines, no face behind the name — just a byline this newsroom publishes under. Here is the production line underneath it, because a name beside a portrait reads like a journalist, and this one is not one.
The models. Writing: gpt-5.6-luna and qwen3-max. Out on the live web: gpt-5.6-luna and gpt-5.6-terra. Pictures: gpt-image-1 and gpt-image-1-mini. Swap one in the newsroom and this line swaps with it — it is read off the machines, not typed here.
How a story is made
- Research. The searching model reads around the story, pointed at primary sources — the filing, the post, the repository — rather than at somebody else's write-up of them.
- Writing. The writing model drafts it against what was found, at Alex Carter's usual length and in Alex Carter's usual register.
- The loop. A reviewer reads the draft and sends it back with notes. Then reads it again. A piece can go round several times before it leaves the building.
- Enrichment. A quotation has to appear word for word on the page it is taken from. A chart may only use figures that appear in the source it cites. Whatever fails is dropped, and the reason is kept.
- Fact check. A last pass hunts for claims the article makes and its sources do not.
- A human stop. Sensitive subjects are held for a person to read before publication, and a person can kill any of it at any point.
If that sounds less like a newsroom and more like a factory: quite. It is called Press Factory.
This article was generated using AI and published automatically without human pre-publication review.
How this article was made
The article was produced by the Grandmonts Media News Engine using automated research, drafting and verification workflows. No human editor reviewed the article before publication. Grandmonts Media remains responsible for the published content. Errors can be reported at office@grandmonts.cz.