Zhipu AI has officially released GLM-5.2, a flagship language model engineered specifically for long-horizon tasks and agentic software development. Building on its predecessor, GLM-5.2 introduces a robust 1M-token context window, designed to maintain reliability and precision during extended engineering workflows, including large-scale code generation, automated research, and complex debugging. The model also features dynamic effort-level control, allowing developers to balance computational cost, execution speed, and performance by selecting between High and Max reasoning modes. In long-horizon coding benchmarks, GLM-5.2 establishes itself as the top-performing open-source model. It matches or narrowly trails leading proprietary systems across multiple rigorous evaluations. On FrontierSWE, which tests multi-hour technical projects, GLM-5.2 trails only Opus 4.8 by one percentage point. It secures second place on PostTrainBench and SWE-Marathon, demonstrating strong capabilities in post-training optimization and ultra-long software engineering pipelines. Standard coding evaluations further highlight its gains, with scores on Terminal-Bench 2.1 and SWE-bench Pro significantly surpassing GLM-5.1 while closing the performance gap with frontier closed-source alternatives. Supporting this extended context requires substantial architectural and infrastructural innovations. GLM-5.2 implements an IndexShare mechanism that distributes lightweight indexing across every four transformer layers, drastically reducing computational overhead for long sequences. The model also optimizes multi-token prediction for speculative decoding by sharing key-value caches and applying rejection sampling, improving acceptance rates by twenty percent. To address the memory bottlenecks inherent in 1M-context inference, the underlying serving engine introduces fine-grained memory management, optimized context kernels, and refined CPU-side scheduling, ensuring high throughput and concurrency even under heavy load. The model’s training pipeline leverages the Slime infrastructure for large-scale agentic reinforcement learning. This framework unifies heterogeneous data and supports complex rollout patterns, enabling efficient parallel training across multiple expert models. GLM-5.2 transitions to a critic-based Proximal Policy Optimization approach to handle variable-length execution traces, while incorporating a novel anti-hack module. This security layer detects and neutralizes reward-hacking behaviors in coding agents using combined rule-based filtering and LLM intent verification, ensuring training stability and genuine capability improvement. GLM-5.2 is immediately accessible to developers through the Z.ai Coding Plan and the ZCode desktop agent, which offers remote development and mobile control capabilities. Model weights are publicly available on HuggingFace and ModelScope, with full compatibility for open-source inference frameworks including vLLM and SGLang. This release positions GLM-5.2 as a practical, production-ready foundation for sustained, long-context AI engineering workflows.