AutoGLM-Phone

GLM-5.3-Flash

Z.ai

GLM-5.3-Flash is a natively multimodal GLM model built on a newly trained base. Its hybrid sparse and linear attention, manifold-constrained hyper-connections, and 30T-token multimodal pretraining target strong capability at lower long-context serving cost. Native visual perception supports agents that observe interfaces, inspect rendering results, and refine code. The 320B model activates 18B parameters per token and supports million-token contexts. MIT weights and official API/chat access provide both local and hosted options.

GLM-5.3

Z.ai

GLM-5.3 advances coding, long-horizon engineering, and vulnerability-analysis capabilities through post-training on the GLM-5.2 base model. The model remains text-based and supports a million-token context, selectable reasoning effort, and persistent thinking history. Z.ai initially announced hosted availability before releasing downloadable weights. Those weights use the custom GLM-5.3 License rather than the MIT license of earlier GLM-5 models. It is distinct from GLM-5.3-Flash, which introduces a new native multimodal architecture.

GLM-5.2

Z.ai

GLM-5.2 expands the GLM engineering line to a million-token context and improves reliability on sustained coding tasks. IndexShare reuses sparse-attention indexing across groups of layers to lower long-context computation, while multi-token prediction improvements accelerate decoding. Training emphasizes maintaining goals and using noisy repository and tool context over extended sessions. Public MIT weights and official API access support deployment. The million-token limit is an input-context capability, not a guarantee of reliable retrieval or correct execution at every length. GLM-5.2 Fast is a faster-serving route with the same weights, grouped here rather than cataloged as a separate trained model.