Z.ai Ships GLM-5.3 Without Retraining the Base Model: Better at Complex Coding and Long-Horizon Tasks
Z.ai just released GLM-5.3. GLM-5.3 runs on the same 743B base model as GLM-5.2. Every reported gain comes from scaled post-training: more task environments, more environment types, longer training. The results land in two places. Coding jumps most on the longest-horizon benchmarks, with Terminal-Bench 3.0 moving from 4.6 to 28.3. Cybersecurity moved further than Z.ai says it expected, with CyberGym reaching 84.5%. Weights are not public yet. Is It Deployable? Partially, GLM-5.3 is live
Z.ai released GLM-5.3, a new version of its language model that uses the same underlying base model as its previous version but achieves improvements through additional post-training on more tasks and environments. The model shows substantial gains in coding tasks, with one benchmark jumping from 4.6 to 28.3, and unexpectedly strong performance in cybersecurity applications like vulnerability discovery, where it outperformed some competing models. The weights are not yet publicly available but will be released in approximately two weeks after safety evaluation, though the model is already accessible through Z.ai's API and coding plan for some users. This matters because the improvements demonstrate that scaling post-training alone, without retraining the base model, can produce significant capability gains in complex tasks like long-horizon coding and security analysis.
Explainer: GLM 5.2: How a 744B Open Model Is Closing the Gap With Claude Opus on Code
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