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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.

GLM-4.6V-Flash is the free version of GLM-4.6V, representing a significant iteration in the GLM series for multimodal capabilities. It supports toggling reasoning modes, with a training-time context window expanded to 128k tokens. Achieving state-of-the-art (SOTA) visual understanding accuracy at its parameter scale, it is the first visual model to natively integrate Function Call capability into its architecture. This establishes a seamless pipeline from "visual perception" to "executable actions (Action)", offering a unified technical foundation for multimodal agents in real-world business scenarios.

GLM-5 is Zai’s new-generation flagship foundation model, designed for Agentic Engineering, capable of providing reliable productivity in complex system engineering and long-range Agent tasks. In terms of Coding and Agent capabilities, GLM-5 has achieved state-of-the-art (SOTA) performance in open source, with its usability in real programming scenarios approaching that of Claude Opus 4.5.

As a 30B-class SOTA model, GLM-4.7-FlashX offers a new option that balances performance and efficiency. It is further optimized for agentic coding use cases, strengthening coding capabilities, long-horizon task planning, and tool collaboration, and has achieved leading performance among open-source models of the same size on several current public benchmark leaderboards.

As a 30B-class SOTA model, GLM-4.7-Flash offers a new option that balances performance and efficiency. It is further optimized for agentic coding use cases, strengthening coding capabilities, long-horizon task planning, and tool collaboration, and has achieved leading performance among open-source models of the same size on several current public benchmark leaderboards.

GLM-4.6V-FlashX is the the paid version, offering higher capacity and stability， representing a significant iteration in the GLM series for multimodal capabilities. It supports toggling reasoning modes, with a training-time context window expanded to 128k tokens. Achieving state-of-the-art (SOTA) visual understanding accuracy at its parameter scale, it is the first visual model to natively integrate Function Call capability into its architecture. This establishes a seamless pipeline from "visual perception" to "executable actions (Action)", offering a unified technical foundation for multimodal agents in real-world business scenarios.

GLM-4.6V represents a significant evolution of the GLM series into the multimodal domain. It features a 128k-token training context window and sets a new state-of-the-art in visual understanding accuracy for its parameter scale. Pioneeringly, it is the first model to natively integrate tool-calling capabilities into its visual architecture, bridging the gap from visual perception to executable actions. This makes it a unified technical foundation for multimodal Agents in real-world business scenarios. Pricing: The model's official pricing is (GLM-4.6v: Input $0.3, Cached Input $0.05, Output $0.9). Our platform is currently running a limited-time promotion, during which you will receive a discount on the official price.

GLM-4.6 is a flagship model from Zhishen with 355B total parameters and 32B active parameters. The model's context window has been expanded from 128K to 200K, enabling it to handle longer code and agent tasks. In programming capabilities, GLM-4.6's performance is comparable to Claude Sonnet 4 on public benchmarks and real-world programming tasks. The model supports tool calling during inference and features optimized search and tool-use performance within agent frameworks. Furthermore, enhancements have been made to its writing style, readability, role-playing, and cross-lingual task processing abilities.

Pricing: The model's official pricing is (GLM-4.6: Input $0.6, Cached Input $0.11, Output $2.2). Our platform is currently running a limited-time promotion, during which you will receive a discount on the official price.

Pricing: The model's official pricing is (GLM-4.7: Input $0.28 ~ $0.57, Cached Input $0.057~$0.11, Output $1.14~$2.27). Our platform is currently running a limited-time promotion, during which you will receive a discount on the official price. GLM-4.7 is Zhipu’s latest flagship model. Tailored for agentic coding scenarios, GLM-4.7 strengthens coding capabilities, long-horizon task planning, and tool collaboration, and delivers leading performance among open-source models on the latest leaderboards of multiple public benchmarks. Its general capabilities have also improved, with responses that are more concise and natural and writing that feels more immersive. When executing complex agent tasks and invoking tools, it follows instructions more reliably, while the visual quality of artifacts and agentic coding front ends—as well as long-horizon task completion efficiency—are further enhanced.

GLM-4.5 is our latest flagship foundation model, purpose-built for agent-based applications. It leverages a Mixture-of-Experts (MoE) architecture and supports a context length of up to 128k tokens. GLM-4.5 delivers significantly enhanced capabilities in reasoning, code generation, and agent alignment. It supports a hybrid inference mode with two options, a "thinking mode" designed for complex reasoning and tool use, and a "non-thinking mode" optimized for instant responses. Users can control the reasoning behaviour with the reasoning enabled boolean.

GLM-4.5-Air is the lightweight variant of our latest flagship model family, also purpose-built for agent-centric applications. Like GLM-4.5, it adopts the Mixture-of-Experts (MoE) architecture but with a more compact parameter size. GLM-4.5-Air also supports hybrid inference modes, offering a "thinking mode" for advanced reasoning and tool use, and a "non-thinking mode" for real-time interaction. Users can control the reasoning behaviour with the reasoning enabled boolean. Learn more in our docs