GLM-5.2: Built for Long-Horizon Tasks

GLM-5.2, the latest flagship model from Z.AI, advances long-horizon task capabilities significantly over its predecessor, GLM-5.1, by supporting a stable 1M-token context. The new model introduces several innovations, such as improved architecture through IndexShare to reduce computational costs and an enhanced MTP layer for speculative decoding, thus increasing acceptance length by 20%. It is released under an MIT open-source license, removing regional restrictions and enhancing technical access. GLM-5.2 consistently ranks as the top open-source model across multiple long-horizon coding benchmarks, demonstrating its practical application in sustained engineering work. It also incorporates effort level control, allowing users to balance performance against computational cost, and has been optimized for efficiency in large-scale agentic reinforcement learning tasks, with infrastructure support from the slime framework. An anti-hack module is introduced to mitigate reward hacking in coding scenarios, maintaining training integrity. The model's architecture and enhancements enable it to outperform previous iterations and rival closed-source models in various coding and reasoning benchmarks, while providing users with greater flexibility and scalability in long-context inference scenarios.