GLM-5

Z.ai launched GLM-5 on 2026-02-12, scaling from GLM-4.5's 355B parameters (32B active, 23T tokens) to 744B parameters (40B active, 28.5T pre-training tokens) and integrating DeepSeek Sparse Attention to cut long-context deployment costs. The new slime asynchronous RL infrastructure enables more fine-grained post-training iterations at scale. GLM-5 targets complex systems engineering and long-horizon agentic tasks, with Z.ai reporting best-in-class open-source performance on reasoning, coding, and agentic benchmarks and narrowing the gap to Claude Opus 4.5 on CC-Bench-V2 and Vending Bench 2. Weights were released under MIT on Hugging Face and ModelScope, with API access via api.z.ai and BigModel.cn and Claude Code/OpenClaw compatibility.

GLM-5.2 from Z.ai offers a 1M token context window, high and max reasoning modes, and free access. Learn how to set it up in Claude Code, OpenClaw, and Cline.

Independent analyst Maxime Labonne corroborates Z.ai's February 11, 2026 GLM-5 release as a 744B-parameter MoE with 40B active parameters and a 200K-token context window enabled by DeepSeek Sparse Attention. GLM-5 reportedly tops Artificial Analysis among open-weight models and reaches first among open models on LMArena Text Arena with a score of 1452, while posting 77.8% on SWE-bench Verified, 92.7% on AIME 2026, and 86.0% on GPQA-Diamond. Labonne reports GLM-5 was trained entirely on Huawei Ascend chips using the MindSpore framework with no NVIDIA dependency, a notable constraint given Zhipu's January 2025 placement on the U.S. Entity List. Weights are MIT-licensed on Hugging Face and distributed via Z.ai API and OpenRouter, with the model beating Gemini 3 Pro and GPT-5.2 on SWE-bench Verified while trailing Claude Opus 4.5.

Z.AI's official release-notes index documents the evolution of the GLM-5 line beyond the base model, confirming successive variants released by Z.AI. The page lists GLM-5.1 (April 7, 2026), GLM-5.2 (June 16, 2026), GLM-5.3 (August 18, 2026), and GLM-5.3-Flash (August 26, 2026), establishing GLM-5 as the foundation later iterations extended. These entries also provide context about the family direction: GLM-5.3 claims a 50% gain over GLM-5.2 on Z.ai Code Bench, and GLM-5.3-Flash adds native visual capabilities across code, browsers, and GUIs. The notes serve as first-party lineage evidence for the GLM-5 model family without making claims about Amazon Bedrock availability.

InferenceX provides a third-party technical overview confirming GLM-5's core specifications: 744B parameters with 40B active, 28.5T tokens of pre-training data, integration of DeepSeek Sparse Attention (DSA) to cut training and inference cost while maintaining long-context fidelity, and the "slime" asynchronous RL infr The overview also documents the GLM-5.1 follow-up point release (repository created 2026-04-03, public release April 7, 2026), described by Z.ai as the next-generation flagship for agentic engineering with state-of-the-art performance on SWE-Bench Pro and leading improvements on NL2Repo and Terminal-Bench 2.0. The arXi