Nothing here is sponsored — no affiliate links on this page. This breaking article is not the daily buyer-intent post. Facts below come from the sources listed at the end, checked 2 November 2026. We have not tested LongCat-2.5 hands-on, and no hands-on claim is made anywhere in this article.
Meituan released LongCat-2.5 on 2 November 2026, moving from the 2.5-Preview of 25 September 2026 to a full release. The pitch is not a smarter chatbot. It is a model built for the unglamorous middle of real development work: staying on a task across terminals, browsers, graphical interfaces, spreadsheets and design tools, instead of answering one prompt well and losing the thread on the next step.
How we put this together
We verified the story against TechNode, Daily AI Brief and AI Affairs coverage of 2 November 2026. We have not run LongCat-2.5 ourselves, so nothing here is a hands-on finding. Every benchmark below is labelled vendor-reported and unverified, and the disputed parameter figures are flagged as disputed, not repeated as fact.
Verdict: LongCat-2.5 is interesting not because of its headline scores, which are vendor-reported and unverified, but because of what it is optimised for: sustained, multi-surface agent work, with visual and text input in one framework. If your work lives in one editor and one terminal, this changes little today. If your work regularly spans a browser, a GUI app and a spreadsheet, it is worth watching — once independent testing, and the promised open releases, arrive.
What LongCat-2.5 actually is
LongCat-2.5 is a native multimodal model, which in practice means it takes visual and text input inside a single unified token framework rather than bolting a vision encoder onto a text model as an afterthought. That matters for agent work: much of what an agent has to deal with — a browser page, a GUI window, a design canvas, a spreadsheet — is visual first. A model that can genuinely read those surfaces, rather than relying on a scraped text dump of them, has a better chance of doing something sensible with them.
Meituan positions it for long-horizon agentic coding: sustained execution across terminals, browsers, GUIs, spreadsheets and design tools, plus visual coding, creative design and 3D tasks. The through-line is duration and breadth, not a single clever trick. The model is supposed to keep going — and keep context — across tools that normally force an agent to start over.
Two honest caveats belong right here, before any numbers. First, the reported scale — 1.6 trillion total parameters, with about 48 billion activated per token — is disputed: a report noted that the same figures were attached to LongCat-2.0 and carried across to the preview, so treat them as reported, not settled. Parameter counts are a weak proxy for capability anyway; what matters is what the model does on real tasks, and that is still largely unproven for this release. Second, Meituan says it trained on extended agent-action sequences including failed attempts, and optimised 4-bit weights and activations for domestic chips. That is a plausible recipe for a long-horizon agent — learning from dead ends, not just clean demonstrations — but it is the vendor's description of its own training, not an independently confirmed fact.
The vendor-reported benchmarks — read them as claims, not results
Meituan's reported numbers, all vendor-reported and not independently verified, are: Terminal Bench v2.1: 88.4, against 70.8 for LongCat-2.0; AutomationBench: 51.8; BrowseComp: 91.9; and Program Bench: 25.5, against a top entry of 37.
The Terminal Bench jump, if it holds up, would be the most meaningful of the four: terminal work is the backbone of agentic coding, and a leap from 70.8 to 88.4 would suggest the long-horizon training is doing something real. But "if it holds up" is doing a lot of work in that sentence. Vendor benchmarks are run in the vendor's harness, on the vendor's choice of tasks, and they have a long history of not surviving contact with independent evaluation. The Program Bench figure cuts the other way and is worth more attention than it will probably get: 25.5 against a top entry of 37 is a reminder that this model is not claiming to lead everywhere. A model that is strong on terminal and browser automation but middling on program synthesis is a specialist, not a general champion — and specialists are fine, as long as nobody sells them as champions.
The right posture is simple: note the numbers, wait for independent runs, and in the meantime judge the model by what it can demonstrably do in your own workflow, not by a benchmark table.
How you can actually try it — and what is not out yet
LongCat-2.5 is available through longcat.chat and the LongCat API, and it supports Claude Code, Codex, Hermes and other agent tools. That tooling support matters more than the chat window: an agentic model that only lives in a web chat is a demo, while one that plugs into the agent harness you already use is something you can actually test on a real job.
What is not out yet is equally important, and it is easy to get wrong in the rush of a launch day. GitHub and Hugging Face releases are planned, not yet released. Do not go looking for an open-weights download today expecting to find one, and treat any link claiming to be the official LongCat-2.5 weights with suspicion until Meituan actually publishes them. The gap between "planned" and "live" is where launch-day confusion — and copycat repos — thrive.
What this means for developers, honestly
Judge it on sustained jobs, not single prompts. The whole case for LongCat-2.5 is duration across tools. A one-shot code-generation test will not tell you whether it is good at what it claims to be good at. If you try it, give it a job that actually spans a terminal, a browser and a document, watch where it loses context, and compare that failure point with whatever you use today. That is the test that matters.
Visual input is the quiet differentiator. Most coding agents still live in text. If LongCat-2.5's unified visual-text framework genuinely lets it work with GUIs, design tools and spreadsheets rather than around them, that opens up a class of work — updating a dashboard, checking a design against a spec, reconciling a spreadsheet — that text-only agents handle badly. Again, that is a claim to test, not a fact to repeat.
The disputed parameter count is a warning label, not a scandal. Figures that migrate unchanged from one release to the next are a reminder to read launch coverage sceptically, including this article's own sources. It does not make the model bad; it makes the marketing around it unreliable. Capability claims need independent evidence, and parameter counts are not evidence.
Do not rebuild your stack on launch day. The model is new, the open releases are still pending, and no independent benchmark has landed yet. The sensible move is a small, time-boxed trial on a real multi-tool job — inside the agent tool you already use, if it is supported — while waiting for the open release and independent numbers.
What LongCat-2.5 does NOT prove
It does not prove long-horizon agents are solved. One vendor release with vendor benchmarks does not settle a problem every lab is still working on. Sustained execution is hard precisely because errors compound; a model that survives ten steps can still fail at fifty.
It does not prove the parameter figures. The 1.6 trillion / 48 billion activated figures are reported and disputed, as noted above. Treat them as unverified.
It does not prove leadership on program synthesis. Its own reported Program Bench score trails the top entry by a wide margin.
We have not verified any of this hands-on. This article reports Meituan's release and the coverage of it, as listed below. We have not run LongCat-2.5, and no hands-on testing, benchmark reproduction, or performance claim beyond the sources is made here.
The useful takeaway is narrower than the launch headlines, and more durable. The agent race is shifting from "who writes the best function" to "who can stay on a messy, multi-tool job until it is actually finished." LongCat-2.5 is Meituan's bet that the second question is the one that matters — and that training on long action sequences, failures included, plus genuinely multimodal input, is how you answer it. Whether the bet pays off will be decided by independent testing and by the open releases that are still to come, not by today's announcement. Until then, try it on a real job, watch where it breaks, and keep the scepticism switched on.
Sources
Sources: TechNode, "Meituan launches LongCat-2.5 multimodal AI model for extended coding tasks," technode.com, 2 November 2026 (full release from the 2.5-Preview of 25 September 2026; long-horizon agentic coding across terminals, browsers, GUIs, spreadsheets and design tools; visual + text input in a unified token framework; availability via longcat.chat and the LongCat API; Claude Code, Codex and Hermes support; GitHub and Hugging Face releases planned, not yet out; training on extended agent-action sequences including failed attempts; 4-bit weights/activations optimised for domestic chips); Daily AI Brief, "Meituan releases LongCat-2.5 multimodal model," dailyaibrief.com, 2 November 2026 (vendor-reported benchmarks: Terminal Bench v2.1 88.4 vs 70.8 for LongCat-2.0, AutomationBench 51.8, BrowseComp 91.9, Program Bench 25.5 vs top entry 37); AI Affairs, "Meituan releases LongCat-2.5 for long-running coding tasks," aiaffairs.com, 2 November 2026 (multimodal agentic coding positioning; visual coding, creative design and 3D tasks). Parameter figures (1.6 trillion total, ~48 billion activated per token) are reported but disputed — a report noted the same figures were attached to LongCat-2.0 / carried across to the preview — and are presented here as reported, not settled. No hands-on testing was performed for this article, and no benchmarks, prices, or availability beyond the sources above are claimed.
