Meituan LongCat releases the LongCat-2.5 native multimodal model, with 1.6T parameters targeting long-horizon agent tasks
English text is being prepared.
Meituan LongCat has released its native multimodal model LongCat-2.5, with a total parameter count of 1.6 trillion and approximately 48 billion parameters activated per token, strengthening native visual understanding and agentic coding capabilities on the basis of LongCat-2.0. The model incorporates more long-horizon trajectory data into long-sequence training and, through large-scale long-horizon reinforcement learning, improves end-to-end engineering capabilities such as complex codebase development and complete project construction. LongCat-2.5 represents visual signals as multi-level discrete tokens, enabling the backbone model to process visual and textual inputs in a unified token form and supporting tasks such as creative design and 3D.
Why read this
AI score 67
The original text provides the 1.6T parameter and activation scale, and explains how long-horizon reinforcement learning serves complex codebases and project-building tasks.
Topics
Body
美团龙猫发布LongCat-2.5原生多模态AI模型：1.6T参数，面向长程智能体任务IT之家下载客户端 独家抢先看2026年11月02日 09:18:38 来自山东Updated 2026年11月02日 09:28:45
IT之家 11 月 2 日消息，美团龙猫今日发布面向长程智能体任务的原生多模态模型 LongCat-2.5，模型总参数量为 1.6 万亿，每个 token 激活约 480 亿参数，在 LongCat-2.0 的基础上进一步强化原生视觉理解与智能体编程能力。
据介绍，龙猫在长序列训练中加入更多长程轨迹数据，并通过大规模长程强化学习，提升模型在复杂代码库开发、完整项目构建等任务中的端到端工程能力。围绕数据合成、环境构建与跨 Harness 混合训练，官方持续扩展这条训练链路。此外，龙猫将真实 Agent 的失败轨迹转化为可复现、可验证的训练任务，针对模型能力短板持续补充训练数据。
与此同时，LongCat-2.5 将视觉信号表示为多层级离散 token，使主干模型以统一的 token 形式处理视觉与文本输入。原生视觉理解与智能体编程能力的结合，使模型在 Visual Coding 上表现出色，并支持创意设计和 3D 等任务。
官方表示，这些升级由持续演进的基础设施支撑：优化国产算力上的长上下文训练与 W4A4 推理，并通过训推数值一致性和长上下文 rollout 优化，支撑大规模长程 RL 的高效运行。
IT之家附评测结果：
“特别声明：以上作品内容(包括在内的视频、图片或音频)为凤凰网旗下自媒体平台“大风号”用户上传并发布，本平台仅提供信息存储空间服务。
Notice: The content above (including the videos, pictures and audios if any) is uploaded and posted by the user of Dafeng Hao, which is a social media platform and merely provides information storage space services.”