GLM 5.1

GLM 5.1 is positioned as a next-generation flagship model aimed at agentic engineering and long-horizon software tasks, where the goal is not just a strong single-turn answer but sustained, autonomous productivity. The design intent centers on keeping a model effective across many hours of work: it can plan, execute, debug, and iterate on a single task for more than eight hours without losing direction, breaking ambiguous problems into pieces, running experiments, reading the results, and revising strategy along the way. The release notes describe it as achieving comprehensive capability alignment with Claude Opus 4.6 while pushing further on engineering intelligence, autonomous planning, sustained execution, and tool use over extended sessions. In practice this shows up as a model meant to feel less like a chatbot answering a question and more like an engineer that can carry a project from initial plan through final delivery on its own.

The lineage behind GLM 5.1 combines a very large open-weight base with training shaped specifically for long, multi-turn agentic work. Release notes describe a post-training stack of multi-turn supervised fine-tuning, reinforcement learning, and a process-quality evaluation framework aimed at improving stability, consistency, and tool use on extended tasks. The open-weight build reported through public mirrors sits at roughly 756B parameters with an effectively 198K-token context window in the standard configuration, and a 1M-token lossless context mode is highlighted as a defining capability for whole-repository or large research workloads. Benchmark-wise, the model is reported as achieving state-of-the-art results on SWE-Bench Pro and as leading its predecessor by a wide margin on NL2Repo repository generation and Terminal-Bench 2.0 real-world terminal tasks, with the framing that earlier models exhaust their repertoire quickly while GLM 5.1 continues to make progress when given more time. That combination of open weights, long-horizon training, and strong agentic coding results makes it a natural fit for autonomous coding agents, repository-scale refactors, and multi-step engineering workflows where persistence and tool use matter as much as raw answer quality.