The GLM-5.3 model collection has been released as open-weight models, building upon the GLM-5.2 base architecture through enhanced post-training techniques. This update specifically targets significant improvements in complex coding and long-horizon reasoning tasks. According to benchmark data, GLM-5.3 demonstrates substantial performance gains over its predecessor across several metrics: it achieves 28.3 on Terminal Bench 3.0 (compared to 4.6 for GLM-5.2), 66.9 on DeepSWE (v1.1) versus 46.2, and 42.5 on SWE-Marathon (v1.1) against 19.4. Additional improvements are noted in ProgramBench (19.0 vs 9.5), AutomationBench (48.2 vs 26.2), and ExploitBench (54.4 vs 24.4). The model supports diverse deployment frameworks, including the Ascend NPU platform and inference engines such as vLLM, SGLang, and xLLM. Developers can integrate the model using the Hugging Face Transformers library via the AutoModelForCausalLM class or deploy it as an OpenAI-compatible API using vLLM or SGLang servers. The release also includes Dockerized deployment options for streamlined environment management.