Z.ai Releases GLM-5.1 Focusing on Agentic Engineering

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Z.ai has published GLM-5.1 on Hugging Face under the MIT licence. The 754B parameter MoE model is engineered for advanced coding and long-horizon agentic workflows.

Key points

  1. Open release on Hugging Face under the MIT licence using Safetensors (BF16/F32) with 754B parameters.
  2. Uses the glm_moe_dsa architecture, targeting English and Chinese.
  3. Engineered for agentic engineering, designed to maintain performance across hundreds of iterations and thousands of tool calls.
  4. Benchmark scores include 58.4% on SWE-Bench Pro, 42.7% on NL2Repo, and 63.5% on Terminal-Bench 2.0 (Terminus-2).
  5. Local serving support via SGLang (v0.5.10+), vLLM (v0.19.0+), xLLM, Transformers, and KTransformers.

Why it matters

Many language models plateau quickly when running complex agentic loops. GLM-5.1 is specifically built to sustain performance over long horizons by refining strategies and conducting iterative experiments.

Analysis

Z.ai positions GLM-5.1 as a flagship model advancing beyond initial first-pass generation. The authors argue that previous iterations, including GLM-5, often exhausted initial techniques without benefiting from longer runtime. By contrast, GLM-5.1 is designed to handle ambiguous tasks, isolate blockers, and sustain multi-step problem solving. Its benchmark outcomes on SWE-Bench Pro and terminal tasks put it in close competition with proprietary frontier models.

Found in

Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.

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