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原生多模态 GLM 模型，面向高效编程与长程智能体任务

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For responsive coding interactions and everyday task assistance.

For responsive coding interactions and everyday task assistance.

For responsive coding interactions and everyday task assistance.

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原生多模态 GLM 模型，面向高效编程与长程智能体任务

GLM-5.3-Flash is the first open-source, native multimodal model in the GLM-5 series. It employs a hybrid sparse and linear computation architecture with 320 billion total parameters—of which only 18 billion are active—significantly reducing computational load and cache usage. The model supports visual coding, enabling continuous optimization through the synergy of code and interface interactions; it can also autonomously decompose tasks and invoke tools to efficiently deliver high-quality professional outputs such as PPTX and PDF files.

GLM-5.3-Flash is the first open-source, native multimodal model in the GLM-5 series. It employs a hybrid sparse and linear computation architecture with 320 billion total parameters—of which only 18 billion are active—significantly reducing computational load and cache usage. The model supports visual coding, enabling continuous optimization through the synergy of code and interface interactions; it can also autonomously decompose tasks and invoke tools to efficiently deliver high-quality professional outputs such as PPTX and PDF files.

GLM-5.3-Flash 是 GLM-5 系列首个原生多模态模型，以极致低成本架构实现超越 GLM-5.2 的更强智能。采用 320B 总参 / 18B 激活的稀疏+线性注意力混合架构，注意力计算量与 KV 缓存分别降低 3.01 倍和 4.44 倍。视觉能力原生融入 Coding 循环，可主动观察界面、渲染结果与交互反馈，跨代码、浏览器与图形界面协同完成任务。同时拓展至 Office、金融研究及专业文档场景，能自主拆解目标、调用工具并优化输出，交付 PPTX、PDF、DOCX、XLSX 等高质量成品。

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文本模型，已验收基础对话；缓存读取以实际返回用量计费。

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GLM-5.3-Flash is a native multimodal model from Z.ai. It is suited for efficient coding and long-horizon agent tasks.

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