GLM-5.2: Built for Long-Horizon Tasks
Zhipu AI introduces GLM-5.2, a flagship model for long-horizon tasks with a solid 1M-token context and an MIT license. Architecture innovations include IndexShare, which reuses the sparse attention indexer across four layers to cut per-token FLOPs by 2.9× at 1M context, and an improved MTP layer that raises speculative decoding acceptance length by 20% through IndexShare, KV sharing, rejection sampling, and end-to-end TV loss. Agentic RL post-training is backed by the slime framework, and an anti-hack module detects and blocks reward-hacking behaviors like fetching evaluation files or curl-downloading answers. GLM-5.2 ranks as the top open-source model on long-horizon benchmarks such as FrontierSWE (only 1% behind Opus 4.8) and Terminal-Bench 2.1 (81.0), making it relevant for engineers building coding agents and long-context inference systems.