Z.ai · Multimodal MoE

GLM-5.3-Flash

The first natively multimodal model in the GLM-5 line: a 320B mixture-of-experts with just 18B active, built on a hybrid of sparse and linear attention. The card evaluates it at context lengths up to 1M — 300K on HLE with tools, 400K on DeepSWE, 164K on BabyVision — and reports Terminal Bench 2.1 at 84.3, ExtractBench Short at 96.3 and Deep SWE at 63.4. MIT-licensed with weights on Hugging Face. Distinct from the GLM-5.3 covered in Week 33: different parameter profile, multimodal from launch, and a different attention design.