GLM-5.3-Flash

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Z.ai's open-weights multimodal model built for efficient coding and agents

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GLM-5.3-Flash

What it is

GLM-5.3-Flash is a large language model from Z.ai, the company formerly known as Zhipu AI. It uses a mixture-of-experts design with about 320 billion total and 18 billion active parameters, and combines sparse and linear attention to cut long-context serving costs. Z.ai describes it as the first natively multimodal model in the GLM-5 series, accepting text, images, video and files while producing text. It offers a 1M-token context window, up to 128K output tokens, function calling, structured output and a thinking mode. Developers can call it through the Z.ai API, use it in the GLM Coding Plan, or download MIT-licensed weights from Hugging Face.

Key features

1M-token context window
Text, image and video input
Function calling and JSON output
MIT-licensed open weights

What we liked

+ Open weights under a permissive license
+ Long context for large codebases
+ Efficient active parameter count

Worth noting

· Self-hosting the full model needs serious hardware
· Thinking mode cannot be switched off

Editor's notes

More context from AI BirdView

GLM-5.3-Flash is notable for pairing frontier-style ambitions with an open license. Z.ai positions it for coding and long-running agent work, and its hybrid attention design is meant to keep long-context requests affordable to serve, which matters when an agent is reading a large repository or a stack of documents.

Mixture-of-experts architecture with roughly 320B total and 18B active parameters.
A 1M-token context window with up to 128K tokens of output, per Z.ai's documentation.
Native multimodal input covering images, video, text and files.
Function calling, context caching and structured JSON output for building agents.
Weights are published on Hugging Face under the MIT license, and the model card says it outperforms GLM-5.2 at a fraction of the price.

It is best for developers and teams who want a capable coding and agent model they can either call cheaply by API or host themselves. A practical tip: try it first through the Z.ai API or HuggingChat on a real task from your codebase, then decide whether the faster FlashX variant or self-hosting makes more sense for your volume.