GLM - Zai Models

The GLM (General Language Model) series from Zhipu AI represents a powerful family of next-generation AI models built for a variety of intelligent tasks, including reasoning, code generation, vision-language interaction, and agentic workflows. These models use a Mixture-of-Experts (MoE) architecture, which allows them to activate only a subset of their total parameters during inference — enabling more efficient computation without sacrificing model capacity.

One of the flagship models is GLM‑4.5, which reportedly has 355B total parameters with 32B active during inference.

This model supports a huge context length, up to 128K tokens, making it suitable for long-form reasoning, document comprehension, and multi-step agentic operations.

It also offers a “thinking mode” (for deep reasoning, tool use) and a “non‑thinking mode” (for faster responses), giving flexibility depending on use cases.

For more visual and multimodal tasks, GLM‑4.5V is a specialized vision-language model. According to its GitHub page, GLM-4.5V has 106B parameters and 12B active, and it supports image reasoning, video understanding, GUI tasks, long-document parsing, and more.

It also includes a “Thinking Mode” for deeper inference.

There’s also GLM-4.5-Air, a lighter, more efficient variant (106B total parameters, 12B active) optimized to run with lower resource usage while still supporting reasoning and agent workflows.

ZAI also offers other GLM model variants, including a GLM‑4.6 with an even larger context window (200K tokens in some deployments).

Additionally, Zhipu AI’s GLM family extends into speech with GLM‑4‑Voice, which is designed as an end-to-end spoken chatbot in both Chinese and English. It supports real-time speech interaction and can vary tone, speed, and emotion.

In short, ZAI’s GLM models provide a versatile, multi‑purpose AI toolkit: from powerful reasoning agents to efficient multitasking with images and speech. Their MoE architecture enables large-scale reasoning while maintaining cost-efficiency, and their different model variants allow users to pick the right balance between power, speed, and resource footprint.