GLM-5.1 Deployment Guide: 744B SWE-Bench Pro Leader Self-Hosted Rollout
GLM-5.1 is a 744B parameter MoE model with 40B active tokens, and it is best deployed for SWE-Bench Pro workloads when you match stack, quantization, and API behavior to your latency and tool-call requirements. This guide gives practical production defaults for vLLM, SGLang, and Ascend, with a DeepSeek-V3.1 baseline comparison and a live-check workflow you can apply in less than a day. What makes GLM-5.1 deployment hard in SWE-Bench Pro workflows? GLM-5.1 is designed for long-horizon coding work, and SWE-Bench Pro is exactly that: 1,865 tasks with enterprise-grade difficulty, split across public/held-out/commercial sets, so the first-turn success rate is only part of the story. In deployment terms, GLM-5.1 is not just a large model; it is an orchestration surface where token routing, tool-calling behavior, request queue depth, and prefill-recompute tradeoffs decide whether you can sustain coding sessions. On the Hugging Face leaderboards, GLM-5.1 reports around 58.4 on SWE-Bench Pro and is positioned above multiple high-end competitors, but a bad parser setting or poor precision choice can erase that advantage under real call patterns. The same 1,865-task pressure that drives benchmark score also magnifies edge cases like malformed JSON, stale routes, and silent retries. The key operational lesson is that tool-loop reliability beats single-shot token quality, because SWE-Bench chains typically fail on orchestration before they fail on first-pass reasoning. The takeaway: for SWE-Bench Pro, deployment engineering decides production quality more than raw model score. ...