Z.ai, the developer behind the GLM family of models, has released GLM-5.3. According to the team, the new model is built on the same foundation model as GLM-5.2, with the performance delta coming from expanded post-training rather than a new base architecture. On internal coding evaluations, GLM-5.3 improves by 50% over GLM-5.2 on the Z.ai Code Bench. The model also reaches leading performance among open models on public benchmarks such as Terminal Bench 3 and Agents' Last Exam. In the security arena, GLM-5.3 achieved an 84.5% score on CyberGym's vulnerability discovery benchmark. It also shows significant gains on exploit-chain related tests, including ExploitBench and ExploitGym, compared with its predecessor. These results were reported by ChainCatcher, based on the official announcement from Z.ai. The announcement says the improvement comes from scaling up post-training while keeping the underlying foundation model unchanged. For GLM-5.3, the emphasis is on coding and security-related performance, both of which showed measurable gains in the cited benchmarks.
Z.ai has released GLM-5.3, an update that retains the same underlying foundation model as GLM-5.2. The company attributes the performance gains to expanded post-training.
On coding evaluations, GLM-5.3 scores 50% higher than GLM-5.2 on the internal Z.ai Code Bench. It also reaches leading performance among open models on public benchmarks such as Terminal Bench 3 and Agents' Last Exam.
For security, GLM-5.3 recorded an 84.5% score on CyberGym's vulnerability discovery test. ExploitBench and ExploitGym, which focus on exploit chains, also showed significant gains over the previous generation.
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