Master GLM-5.1 with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Explore the key features that make GLM-5.1 powerful for automation & workflows workflows.
GLM-5.1 is a large language model in the GLM-5 family released by zai-org (Z.ai), distributed as open weights on Hugging Face. It targets complex systems engineering and long-horizon agentic tasks such as multi-step coding, reasoning, and tool use. The model uses a Mixture-of-Experts architecture with 744B total parameters and 40B active per forward pass. Z.ai also offers a managed API on the Z.ai API Platform for users who prefer not to self-host.
The model weights are free to download from Hugging Face, so there is no licensing fee to run it yourself. Real costs come from compute: serving a 744B-parameter MoE model requires multi-GPU infrastructure, typically high-VRAM datacenter GPUs. If you prefer a hosted endpoint, Z.ai offers a paid managed API on the Z.ai API Platform (pricing listed there). Quantized variants accessible via Ollama or LM Studio can lower hardware requirements significantly.
On the published benchmarks, GLM-5 leads on HMMT Nov. 2025 (96.9 vs Gemini 3 Pro 93.0 and Claude Opus 4.5 91.7) and is competitive on AIME 2026 I (92.7) and SWE-bench Multilingual (73.3, ahead of Gemini 3 Pro's 65.0). It still trails frontier models on Humanity's Last Exam (30.5 vs Gemini 3 Pro 37.2) and GPQA-Diamond (86.0 vs 91.9–92.4). For open-source coding and agentic workloads, GLM-5 is the strongest contender Z.ai has shipped.
The Hugging Face card documents three primary paths. With vLLM, you run pip install vllm then vllm serve "zai-org/GLM-5" to expose an OpenAI-compatible endpoint on port 8000. SGLang supports a similar flow via python3 -m sglang.launch_server with --model-path "zai-org/GLM-5" on port 30000. For lighter use, Docker Model Runner (docker model run hf.co/zai-org/GLM-5), Ollama, or LM Studio with quantized variants work well on smaller hardware.
Yes. The chat template natively handles a tools field and emits structured tool calls inside <tool_call>...</tool_call> XML blocks, with arg_key/arg_value pairs for each parameter. The model is explicitly tuned for long-horizon agentic tasks, which is a stated focus of the GLM-5 release. Note that the format is custom XML rather than OpenAI's JSON function-calling schema, so you may need a small adapter when migrating existing OpenAI agent code.
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Tutorial updated March 2026