Kimi K2.7 Code vs. GLM-5.2: which open-weight coding model to self-host on vLLM
Author(s): allglenn
Originally published on Towards AI.
Core concepts: what makes these models tick
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.
After the lead, the article compares Kimi K2.7 Code and GLM-5.2 across architecture (both are MoE with router-selected experts, meaning compute scales with active parameters while VRAM scales with total weights), quantization (K2.7 uses QAT INT4; GLM-5.2 uses FP8 and can also ship AWQ variants), and key architectural serving impacts (GLM-5.2’s IndexShare attention and MTP speculative decoding reduce long-context cost; K2.7’s “thinking” mode and parser requirements affect tooling). It then breaks down vendor-reported vs independently verified benchmarks—highlighting GLM-5.2’s stronger public benchmark story (e.g., SWE-bench Pro, Terminal-Bench, MCP-Atlas, GPQA-Diamond) versus K2.7’s largely proprietary benchmark set—and translates those differences into deployment reality via detailed VRAM sizing guidance (H100/H200 requirements, KV-cache considerations, and recommended vLLM flags). The piece provides concrete vLLM serving commands and OpenAI-compatible endpoint wiring, walks through a self-hosted agentic coding use case with tool calls and PR creation, and finishes with break-even math and a practical decision guide: choose K2.7 when you have H100s and need cost-effective INT4 deployment within shorter contexts; choose GLM-5.2 when you can invest in H200-class hardware, need 1M-context workflows, and care about independent benchmark validation and high-concurrency agent performance.
Read the full blog for free on Medium.
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