GLM-Image is an image generation model adopts a hybrid autoregressive + diffusion decoder architecture. In general image generation quality, GLM‑Image aligns with mainstream latent diffusion approaches, but it shows significant advantages in text-rendering and knowledge‑intensive generation scenarios. It performs especially well in tasks requiring precise semantic understanding and complex information expression, while maintaining strong capabilities in high‑fidelity and fine‑grained detail generation. In addition to text‑to‑image generation, GLM‑Image also supports a rich set of image‑to‑image tasks including image editing, style transfer, identity‑preserving generation, and multi‑subject consistency.
Provider-specific identifiers, limits, and listed prices per million tokens. Every row links back to the provider's own documentation.
| Provider | Provider model ID | Context | Max output | Input | Output | Cache read | Capabilities | Docs |
|---|---|---|---|---|---|---|---|---|
 ZenMux |
z-ai/glm-image |
10.24K | — | — | — | — | Not listed | Docs ↗ |
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zhipuai/glm-imageWeights: Hugging Face ↗
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GLM-Image is an image generation model adopts a hybrid autoregressive + diffusion decoder architecture. In general image generation quality, GLM‑Image aligns with mainstream latent diffusion approaches, but it shows significant advantages in text-rendering and knowledge‑intensive generation scenarios. It performs especially well in tasks requiring precise semantic understanding and complex information expression, while maintaining strong capabilities in high‑fidelity and fine‑grained detail generation. In addition to text‑to‑image generation, GLM‑Image also supports a rich set of image‑to‑image tasks including image editing, style transfer, identity‑preserving generation, and multi‑subject consistency. It is published by Zhipu AI and catalogued here from Models.dev.
GLM-Image accepts up to 10.24K tokens of context.
Provider catalogs list support for image input.
1 provider list this model: ZenMux.
Yes. The weights are published and downloadable from Hugging Face under the MIT license.
The catalog records a release date of 2026-01-19, last verified Sep 19, 2026.
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