Meituan LongCat-2.5 Coding Model Explained: Long-Horizon Agent Coding, Benchmarks and the Disputed 1.6T Figure

Illustrative image: a terminal window running code — the kind of long, multi-step task LongCat-2.5 is built for. Image: Wikimedia Commons. This is not a Meituan product image.

TL;DR — Meituan LongCat-2.5 coding model explained in one line: Meituan released LongCat-2.5 on 2 November 2026, a native multimodal model built for long coding jobs that run across many agent actions — editing complex codebases and building whole projects — with sharply higher company-reported benchmark scores than its predecessor, but a headline 1.6 trillion parameter figure that is already disputed.

📌 KEY HIGHLIGHTS

• Released 2 November 2026: LongCat-2.5 follows a preview that went live on the LongCat API platform on 25 September 2026.
• Built for long-horizon work: trained on extended sequences of agent actions, with large-scale reinforcement learning, for complex codebases and complete software projects.
• Big benchmark jumps (company-reported): 88.4 on Terminal Bench v2.1 (up from 70.8 for LongCat-2.0) and 51.8 on AutomationBench (up from 15.8).
• Disputed spec: the reported 1.6 trillion total parameters and ~48 billion active per token were, per OrcaRouter, carried over from the LongCat-2.0 model card — treat them as unconfirmed.
• Available now: through the LongCat API and longcat.chat, working with Claude Code, Codex, Hermes and other agent tools; GitHub and Hugging Face releases are planned.

Meituan LongCat-2.5 coding model explained: what launched today

Meituan is best known as China's food-delivery giant, but its LongCat line is a serious AI effort, and LongCat-2.5 is its latest step. The new model is "native multimodal": it processes visual and text inputs within a single token framework, converting images into tokens at several levels of detail so the main network handles pictures and words together. Meituan connects that design to visual coding, creative design and 3D tasks — jobs where the model must look at something, not just read text.

The pitch is not "a smarter chatbot". It is a model for work that takes many steps: an agent that keeps going through a large codebase, runs commands, checks results and finishes a project, rather than answering one prompt and stopping. That is the "long-horizon" idea, and it is where Meituan says it focused the training.

How LongCat-2.5 was trained: learning from failed runs

Two training choices stand out. First, Meituan says it expanded training data drawn from long sequences of agent actions and used large-scale reinforcement learning — the system gets feedback on the outcome of a whole sequence of actions, not on each reply in isolation. Second, it turns failed runs by real agents into tasks that can be repeated and checked, giving training concrete examples of work that went wrong. The company also describes synthesised data, constructed environments and mixed training across different agent harnesses — the software that lets a model take actions during a task.

Meituan also says it optimised long-context training and W4A4 inference on domestic chips. W4A4 means four bits per model weight and four bits per activation — a low-precision way of running the model that saves compute, but only if the calculations stay consistent with training. The domestic-chip angle matters in China, where running big models on home-grown hardware is a strategic goal; it is the company's account, not an independently measured speed claim.

LongCat-2.5 benchmarks: the table, read honestly

Illustrative image: example programming code. Image: Wikimedia Commons. Benchmarks below are Meituan's own reported scores, not independent measurements.

Benchmark	LongCat-2.5	LongCat-2.0 (for comparison)
Terminal Bench v2.1	88.4	70.8
AutomationBench	51.8	15.8
BrowseComp	91.9	—
PaperBench	82.1	—
3DCodeBench	55.4	—
Vision2Web	78.6	—
Program Bench	25.5 (top entry: 37)	—

Read the table with two cautions. First, these are Meituan's scores from its own benchmark table, reported by Pandaily, and they have not been independently verified — a benchmark score is not a guarantee of how often a real coding task succeeds. Second, the model does not lead everywhere in its own charts: on Program Bench it scores 25.5 against 37 for the top entry. A model that jumps on terminal and automation tests while lagging on program synthesis is a specialist, not an all-rounder, and that is useful information for anyone choosing a coding agent.

Why it matters for World Tech readers

For developers, the practical news is availability, not just scores. LongCat-2.5 works through existing agent harnesses — the LongCat change log lists Claude Code, Codex and Hermes, with setup guides for OpenCode, Cline, Kilo Code, CodeBuddy and WorkBuddy — so teams can try it inside tools they already use rather than learning a new one. For Indian developers and startups, the same logic applies: the agentic-coding race is now about which model sustains a long task without drifting, and every credible new entrant gives smaller teams more choice beyond the US labs. Our earlier post on Claude Code's game-clone ban shows how fast the rules around these agent tools are changing, and the JEPA theorem on AI training explains why training method — not just scale — is becoming the real differentiator.

The disputed parameter count is also a lesson in reading AI launches: hold the figure as unconfirmed until Meituan publishes a proper 2.5 model card, and judge the model on the planned open release, when independent testing becomes possible.

Should you care? ✅

Verdict: Yes, if you build software with AI agents — try it on a real task, but don't buy the hype. LongCat-2.5's long-horizon training targets the exact weakness of today's coding assistants, and its Terminal Bench and AutomationBench jumps are large. But the benchmarks are vendor-reported, the parameter count is disputed, and the open release that would settle both is still "planned". Test it through the API or longcat.chat on your own codebase before switching anything important to it.

FAQs

What is Meituan LongCat-2.5?
It is Meituan's native multimodal AI model released on 2 November 2026, designed for long-horizon coding and agent tasks — work that runs across many actions, like editing a complex codebase or building a complete software project, rather than answering a single prompt.

How many parameters does LongCat-2.5 have?
Reports cite 1.6 trillion total parameters with about 48 billion activated per token, but that figure is disputed: OrcaRouter reported that the numbers were carried over from the LongCat-2.0 model card to the preview documentation. Until Meituan publishes a 2.5 model card, treat the count as unconfirmed.

What can LongCat-2.5 do better than LongCat-2.0?
On Meituan's own benchmark table, it jumps to 88.4 on Terminal Bench v2.1 (from 70.8) and 51.8 on AutomationBench (from 15.8), with strong BrowseComp (91.9) and PaperBench (82.1) scores. It lags on Program Bench (25.5 vs 37 for the top entry), so it is not best at everything.

How do I try LongCat-2.5, and is it open source?
It is available now through the LongCat API and at longcat.chat, and it works with Claude Code, Codex, Hermes, OpenCode, Cline and other agent tools. GitHub and Hugging Face releases are planned but have not landed yet, so full open weights are still to come.

Takeaway: LongCat-2.5 is a credible new entrant in agentic coding with an honest weakness in its own benchmark table — judge it on your own tasks, and watch the open release for independent verification.

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Sources

• TechNode — Meituan launches LongCat-2.5 multimodal AI model for extended coding tasks (2 November 2026)
• AI Affairs — Meituan releases LongCat-2.5 for long-running coding tasks (2 November 2026, citing Pandaily)
• Daily AI Brief — Meituan releases LongCat-2.5, a 1.6 trillion parameter model for agentic coding (2 November 2026)