Z.ai's GLM-5.3 Got So Dangerous It Delayed Its Own Release
Z.ai's new 753B open-weights model keeps the GLM-5.2 base but doubles down on post-training, topping open-source coding and cyber benchmarks.
PRO- Z.ai released GLM-5.3, a 753B MoE model in FP8 and BF16.
- Same base as GLM-5.2, all gains come from additional post-training.
- Open-source SOTA on Terminal Bench 3.0, Agents' Last Exam, AutomationBench, and CyberGym.
- Terminal-Bench 3.0 jumped from 4.6 to 28.3 over GLM-5.2; ExploitGym roughly tripled.
- Weights were delayed two weeks for safety review after cyber capability spiked.
- Deployable via vLLM, SGLang, Transformers, KTransformers, Unsloth, and Ascend NPU stacks.
Z.ai has released GLM-5.3, a 753B-parameter mixture-of-experts model built on the same base weights as GLM-5.2, with every performance gain driven entirely through additional post-training. Z.ai claims it leads open-weight models on coding and agentic tasks, and its cybersecurity capabilities improved sharply enough during training to trigger a two-week delay before the weights went public.
The model is live on Hugging Face in FP8, with a BF16 variant also available. It has pulled over 151,000 downloads and a trending score above 1,000 since launch.
Post-training doing the heavy lifting
Z.ai kept the GLM-5.2 base and spent another month on post-training with more executable task environments, longer-horizon tasks, and additional reinforcement-learning compute. On Z.ai Code Bench, GLM-5.3 scores 50% higher than GLM-5.2. The shared base makes these deltas unusually clean to interpret: everything in the gap came from post-training.
Benchmark gaps far beyond point-release size
On Terminal-Bench 3.0, the score climbs from 4.6 to 28.3. On DeepSWE v1.1 it moves from 46.2 to 66.9. On AutomationBench it roughly doubles, from 26.2 to 48.2. The gains cluster in agent-shaped tasks involving a terminal, tools, or a changing environment rather than single-turn answers.
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