What happened
On November 2, 2026, Meituan released LongCat‑2.5, the latest addition to its LongCat family of AI models. According to TechNode, the model contains roughly 1.6 trillion parameters in total, with about 48 billion parameters activated for each token processed. LongCat‑2.5 is engineered for tasks that require prolonged execution by AI agents, such as large‑scale software development, visual‑based coding, creative design, and three‑dimensional content creation. Meituan said the model was trained on longer task sequences and that large‑scale was used to improve its ability to work on complex codebases and finish end‑to‑end software projects. The company also described a feedback loop that converts real‑world agent failures into reproducible training tasks, aiming to close performance gaps. LongCat‑2.5 accepts both visual and textual inputs within a unified token framework, enabling “visual coding” where screenshots or UI mock‑ups can be turned into executable code. The model is currently reachable through Meituan’s online chat service and its API platform, with plans to publish the model weights and code on GitHub and Hugging Face in the near future.
Meituan’s announcement positions LongCat‑2.5 as a multimodal AI system that can process both images and text in a single token stream. The model’s architecture reportedly activates roughly 48 billion parameters for each token, a figure that suggests a high degree of compute per inference step.
Training focused on extended task sequences, meaning the model was exposed to long chains of operations that mimic real‑world software projects. was applied at scale to improve the model’s ability to navigate complex codebases, resolve dependencies, and complete multi‑module builds.
A novel aspect highlighted by Meituan is the conversion of real‑agent failures into reproducible training tasks. When an autonomous agent using LongCat‑2.5 encounters an error, that failure is captured, labeled, and fed back into the training loop, aiming to systematically close performance gaps.
The model is already accessible through Meituan’s own chat interface and an API platform, allowing developers to experiment with visual‑coding prompts. Meituan indicated that full model weights and supporting code will be released on GitHub and Hugging Face, though dates and licensing details were not provided.
Why it matters
LongCat‑2.5 marks a notable step in the convergence of multimodal perception and software engineering. By integrating visual understanding directly into the coding pipeline, the model could reduce the friction between design assets and implementation, a bottleneck that many development teams face. The 1.6‑trillion‑parameter scale, combined with the activation of 48 billion parameters per token, places LongCat‑2.5 among the largest publicly discussed models, suggesting a push toward ever‑larger architectures for sustained‑task agents. Meituan’s emphasis on reinforcement‑learning‑based long‑sequence training and on turning agent failures into training data reflects a growing industry focus on reliability and robustness for autonomous coding assistants. If the model lives up to its claims, it could give Chinese developers a home‑grown alternative to Western‑origin coding agents, potentially reshaping the competitive landscape for AI‑driven software development tools. Moreover, the planned open‑source releases on GitHub and Hugging Face could accelerate community experimentation, benchmarking, and downstream applications in fields such as CAD, game development, and UI/UX automation.
The integration of visual inputs directly into a coding workflow could streamline processes that currently require manual translation from design mock‑ups to code, potentially cutting development cycles for UI‑heavy applications.
LongCat‑2.5’s scale and focus on sustained execution address a known limitation of many current coding assistants, which often struggle with long‑running or multi‑step tasks. By training on longer sequences, the model may exhibit better context retention and fewer drop‑offs in lengthy projects.
Reinforcement‑learning‑driven improvement and failure‑to‑training pipelines reflect a maturing approach to model reliability, an area of concern for enterprises that need predictable, safe code generation.
If the upcoming open‑source release includes permissive licensing, it could democratize access to a high‑capacity multimodal coding model, fostering a new wave of community‑built tools and research in visual programming.
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What to watch next
Key signals to monitor include: (1) performance benchmarks released by Meituan or independent researchers that compare LongCat‑2.5’s coding success rate, speed, and error recovery against existing agents like OpenAI’s Code‑Interpreter or DeepSeek’s Super‑Agent; (2) the timeline and licensing terms of the upcoming GitHub and Hugging Face releases, which will determine how quickly third‑party developers can integrate the model; (3) pricing and quota structures for the chat‑service and API access, which remain undisclosed and will affect adoption in startups versus large enterprises; (4) any reported security or safety incidents, especially given the model’s ability to generate and execute code from visual prompts; and (5) market response from Chinese tech firms that may embed LongCat‑2.5 into internal tooling or consumer‑facing products. Observing these factors will clarify whether LongCat‑2.5 transitions from a showcase model to a widely used development assistant.
Benchmark releases: Independent labs or academic groups may publish comparative studies that quantify LongCat‑2.5’s coding accuracy, bug rate, and execution speed against established agents.
Open‑source rollout: The exact timing, licensing (e.g., Apache 2.0 vs. more restrictive terms), and completeness of the GitHub/Hugging Face releases will determine how quickly the broader AI community can adopt and extend the model.
Pricing model: Meituan has not disclosed cost per token, subscription tiers, or enterprise licensing. Pricing will be a decisive factor for startups versus large corporations.
Safety and compliance: Given the model’s ability to generate executable code from images, regulators and security researchers will likely scrutinize its safeguards against malicious code generation.
Ecosystem adoption: Partnerships with IDE vendors, cloud providers, or Chinese tech platforms could accelerate real‑world usage, while resistance or competition from existing agents may shape market dynamics.