Z.ai GLM

Model Instances

GLM

GLM-4.7 continues this trajectory, but represents a more important step forward in the areas that matter most for production agentic workflows: coding agents, terminal-based tasks, tool use, and stability over long multi-turn interactions.

Why should you use GLM-4.7:

Stronger coding agents and terminal workflows. GLM-4.7 shows clear gains on agentic coding benchmarks, matching or surpassing models like DeepSeek-V3.2, Claude Sonnet 4.5, and GPT-5.1. It’s explicitly tuned for modern coding-agent tools such as Claude Code, Cline, Roo Code, and Kilo Code.
Better tool use and web-style browsing tasks. It improves reliability on tool-heavy evaluations and browsing-style tasks, which is exactly where many agent systems fail in production.
Higher quality UI generation. GLM-4.7 focuses explicitly on cleaner, more modern webpages and better layout fidelity for slides.
More controllable multi-turn reasoning. GLM-4.7 builds on interleaved thinking and introduces features aimed at long-horizon stability:
- Interleaved Thinking: thinks before responses and tool calls for better instruction following.
- Preserved Thinking: retains prior thinking across turns in coding-agent scenarios to reduce drift and inconsistencies.
- Turn-level Thinking: lets you enable reasoning only when needed to manage latency and cost.
Check out the details of this feature in their documentation.

If your application involves reasoning, coding, and agentic tasks together, GLM-4.7 is a strong candidate. For teams with limited resources, GLM-4.5-Air FP8 is a more practical choice, which fits on a single H200.

In addition, I also recommend GLM-4.7-Flash. It’s a lightweight 30B MoE model with strong agentic performance and better serving efficiency (e.g., for local coding and agentic tasks).

ASR

Website | Source

GLM-ASR-Nano-2512 is a robust, open-source speech recognition model with 1.5B parameters. Designed for real-world complexity, it outperforms OpenAI Whisper V3 on multiple benchmarks while maintaining a compact size.

Key capabilities include:

Exceptional Dialect Support Beyond standard Mandarin and English, the model is highly optimized for Cantonese (粤语) and other dialects, effectively bridging the gap in dialectal speech recognition.
Low-Volume Speech Robustness Specifically trained for "Whisper/Quiet Speech" scenarios. It captures and accurately transcribes extremely low-volume audio that traditional models often miss.
SOTA Performance Achieves the lowest average error rate (4.10) among comparable open-source models, showing significant advantages in Chinese benchmarks (Wenet Meeting, Aishell-1, etc..).

GLM-4.6V

HuggingFace

The latest open-source multimodal model developed by Z.ai. It features native multimodal tool use, stronger visual reasoning, and a 128K context window.

Two editions are available:

GLM-4.6V (106B) for cloud and high-performance inference
GLM-4.6V-Flash (9B) for local or latency-sensitive deployments

Compared with earlier GLM versions, GLM-4.6V closes the loop between perception, reasoning, and action. It is an ideal option for building visual agents that require both multimodal understanding and real-world task execution.

Key features:

Native multimodal tool calling: GLM-4.6V introduces end-to-end vision-driven tool use. Images, UI screenshots, document pages, and visual snippets can be provided directly as tool parameters without converting them to text first. The model can also interpret visual outputs returned by tools, such as charts, search results, and webpage snapshots, and incorporate them into its reasoning steps. This reduces information loss and lowers system complexity compared with text-only tool pipelines.
Frontend replication and UI interaction: Optimized for frontend tasks, GLM-4.6V can convert UI screenshots into clean HTML/CSS/JS with pixel-level accuracy. Users can highlight areas on a generated page and provide natural-language instructions, such as “change this card to dark blue”, and the model updates the corresponding code.
Long-context multimodal understanding: With a 128K context window, GLM-4.6V can handle high-information-density inputs, such as multi-document financial reports, research papers, 200-page presentation decks, and hour-long videos.

Points to be cautious about:

Limited language support: Currently, the model provides bilingual support for English and Chinese only.
Occasional repetition: The model may over-explain or repeat phrases when handling highly complex prompts. This is a known issue in previous versions like GLM-4.5V.

GLM-Image

Hugging Face

An open-source image generation model from Zhipu AI (Z.ai) that uses a hybrid autoregressive (AR) + diffusion decoder architecture. In general image quality, it’s competitive with mainstream latent diffusion models, but it stands out in two scenarios that many diffusion models still struggle with:

Dense text rendering (especially Chinese and mixed-language typography)
Knowledge-intensive, information-dense generation (posters, menus, infographics, UI-like layouts, instructions)

Under the hood, GLM-Image pairs:

A 9B autoregressive generator initialized from GLM-4-9B that generates a compact sequence of visual tokens for global semantics and layout.
A 7B single-stream DiT diffusion decoder that reconstructs high-frequency details and adds a dedicated Glyph Encoder to improve accurate text rendering in images.

Why should you use GLM-Image:

Best-in-class text rendering among open weights: GLM-Image is specifically designed to generate legible, structured text inside images. If your outputs require typography (signage, posters, UI mockups, packaging), it’s a strong option.
Knowledge-dense generation and better instruction following: The AR module helps with semantic alignment in complex prompts where pure diffusion models can drift or “lose” the information hierarchy.
One model for both generation and editing: GLM-Image supports both text-to-image and image-to-image in the same model, including editing, style transfer, identity-preserving generation, and multi-subject consistency. This simplifies production pipelines.

Points to be cautious about:

Resolution constraints: Target resolution must be divisible by 32, or it will cause errors.
Prompt formatting matters for text: For best text rendering, wrap text intended to appear in the image in quotation marks, and consider prompt enhancement (they recommend using GLM-4.7 to rewrite prompts).

If you care about typography quality and complex prompts more than raw speed, GLM-Image is one of the most practical options.

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Last modified 22 March 2026