Z.ai's GLM-5.3 jumps on coding benchmarks via post-training alone

Z.ai released GLM-5.3, which reuses the same 743B-parameter GLM-5.2 base model unchanged, with all gains coming from additional post-training. Terminal-Bench 3.0 rose from 4.6 to 28.3 and DeepSWE v1.1 from 46.2 to 66.9, while cybersecurity benchmark CyberGym reached 84.5% and ExploitBench more than doubled to 54.4%; open weights are expected in about two weeks.

Why it matters: The size of the improvement from post-training alone shows how much capability is still unlocked through better training environments rather than bigger base models, a pattern showing up across several recent releases. The outsized jump on offensive cybersecurity benchmarks is notable given other labs' recent moves to slow development over cyberattack risk, underscoring how capability gains and dual-use risk are rising together industry-wide.