Zhipu releases GLM-5.3 and multimodal GLM-5.3-Flash open-weight models
Zhipu (Z.ai) published GLM-5.3 and GLM-5.3-Flash on Hugging Face. GLM-5.3 is a 753B-parameter open-weight model that reuses the GLM-5.2 base model, with all gains coming from post-training: the team says it improves 50% over GLM-5.2 on its in-house Z.ai Code Bench and reaches open-source state of the art on public benchmarks including Terminal Bench 3.0 and Agents' Last Exam, while expanded post-training also produced unexpected cyber capability, leading on CyberGym vulnerability discovery and more than doubling GLM-5.2 on exploitation benchmarks. GLM-5.3-Flash is the first natively multimodal model in the GLM-5 series, with 320B total and 18B active parameters, hybrid sparse/linear attention and mHC; Zhipu says it outperforms GLM-5.2 at one-tenth the price and approaches Claude Opus 4.8 on coding and agentic benchmarks. Both support local deployment via SGLang and vLLM and expose a reasoning_effort parameter (low/high/max, default max).
Rapid open-weight gains on coding and cyber-offense benchmarks are narrowing the gap with closed frontier models, while raising fresh questions about deployment cost and misuse risk.