# Kimi K2.7 Code vs. GLM-5.2: which open-weight coding model to self-host on vLLM > Source: > Published: 2026-07-16 11:51:53+00:00 Member-only story # Kimi K2.7 Code vs. GLM-5.2: which open-weight coding model to self-host on vLLM You’ve just finished reading the sixth “open-source model beats GPT-5.5” post this month, and you’re still no closer to an infrastructure decision. Your team needs a coding agent backbone, your legal department won’t sign off on sending proprietary code to a third-party API, and your cloud GPU budget is real money with real accountability. In June 2026, two serious candidates landed days apart: Kimi K2.7 Code from Moonshot AI on June 12 and GLM-5.2 from Z.ai (Zhipu AI) on June 13. Both are open-weight MoE (Mixture-of-Experts) models. Both support vLLM and SGLang. Both are explicitly designed for agentic coding workflows. And both come with benchmark numbers that look impressive on a blog post but require careful reading before you commit eight H200s to them. This article is a hardware-honest, numbers-driven comparison. By the end, you’ll understand the architecture of each model, what their benchmark results actually mean, how to configure vLLM for each one, when self-hosting breaks even against the respective APIs, and which model wins for which use case. ## Core concepts: what makes these models tick Before diving into the comparison, it helps to understand the shared architectural pattern that defines both…