Z.ai Releases GLM-5.3 Weights After a Safety Hold on Its Cyber Capabilities

Z.ai has published the weights for GLM-5.3, the coding and agent model it launched through its application programming interface (API) and subscription coding plan earlier this month. The repository on Hugging Face is public and ungated, with an FP8 checkpoint of about 756 gigabytes and a BF16 release near 1.5 terabytes. A Russian-language technical channel flagged the drop as a full weights release rather than API access, meaning teams can run and fine-tune the model on their own hardware.

The model is a 743-billion-parameter mixture-of-experts design with roughly 40 billion parameters active per token, and it shares its base with GLM-5.2. Z.ai says every gain came from post-training: more environments, more task diversity, and more compute spent on reinforcement learning over that stack. The company reports 28.3 on Terminal-Bench 3.0, up from 4.6 for the prior release, and 66.9 on DeepSWE v1.1, and it calls the model the strongest open-weights coder available. Those numbers come from the vendor, and independent reproduction on the long-horizon command-line benchmark has not yet been published.

The security profile is the more consequential part. GLM-5.3 scores 84.5 on CyberGym, a benchmark for finding software vulnerabilities, which places it marginally above Claude Mythos 5 and GPT-5.6 Sol on that measure, and Z.ai says its largest gains sit further along the exploitation chain. The company delayed the release after the API launch to run what it described as its most extensive risk review to date, because offensive-security capability had grown faster than it expected. Once weights are downloadable, that review governs nothing about how the model is used.

Analyst Nathan Lambert argued that Chinese labs are now matching the frontier on the practical agentic work engineers actually buy, rather than trailing by a generation. A same-base, post-training-only jump of this size supports that read, and it also suggests the remaining headroom in reinforcement learning environments is large enough that a lab without a new pretraining run can still move the frontier of open models.