GLM-5.3-Flash

Name: GLM-5.3-Flash
Author: Z.ai

Z.aiOpen WeightsPending Human Review

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.

2026-08-26

320B total, 18B activated

Mixture of Experts (MoE) with hybrid sparse/linear attention, mHC, and vision encoder

MIT

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Specifications

Parameters: 320B total, 18B activated
Architecture: Mixture of Experts (MoE) with hybrid sparse/linear attention, mHC, and vision encoder
License: MIT
Context Window: 1,048,576 tokens
Type: multimodal
Modalities: textimagevideo

Benchmark Scores

Advanced Specifications

Model Family: GLM
API Access: Available
Chat Interface: Not Available

Capabilities & Limitations

Capabilities: reasoningcode generationtool usevisual agents
Known Limitations: Self-hosting the full MoE checkpoint requires substantial weight memory.Long-horizon agent results depend on the harness, tools, and reasoning budget.
Notable Use Cases: software engineering agentscoding assistantsresearch workflowsvisual interface automation
Function Calling Support: Yes
Tool Use Support: Yes

Resources

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