GLM-5.2
Everything on Z.ai's open-weights GLM-5.2 coding model: how to access it free and cheap, what it costs to run, how it stacks up against DeepSeek, Qwen, and the frontier labs, and why an MIT-licensed model is beating closed ones on real benchmarks.
6 articles50 min total read time
6 of 6 posts published
01
Where to Run GLM-5.2 Free and Cheap: Providers Compared
GLM-5.2 is an MIT-licensed open-weights coding model. Cheapest routes: OpenRouter hosts from about $0.36 per million input tokens, Z.ai's API, or self-host.
7 min read|Read →
02
GLM-5.2 Cost Math: When Open-Weights Coding Models Actually Save You Money
GLM-5.2 cost math: when Z.ai's open-weights coding model actually saves money, with per-token pricing, a worked cost-per-task example and a decision guide.
9 min read|Read →
03
GLM-5.2 vs DeepSeek V4 vs Qwen3: The Open-Weights Coding Model Showdown (2026)
GLM-5.2 vs DeepSeek V4 vs Qwen3 vs Kimi K3: open-weights coding models compared on benchmarks, pricing, context windows, self-host footprint and best picks.
14 min read|Read →
04
GPT-5.5 Has a 3x Higher Hallucination Rate Than MIT-Licensed GLM-5.2
GPT-5.5 hallucinates 86% of the time on AA-Omniscience when it does not know, versus 28% for MIT-licensed GLM-5.2. What the metric measures and why it matters.
6 min read|Read →
05
GLM-5.2 Local Deployment: Running Z.ai's 744B Model on Consumer Hardware
Run GLM-5.2 locally with Unsloth dynamic quantization: hardware math for a 256GB Mac or 24GB GPU, quantization tradeoffs and lessons from the HN thread.
7 min read|Read →
06
GLM 5.2 and the AI Margin Collapse Thesis
GLM 5.2 and the AI margin collapse thesis: why open-weights models could squeeze frontier lab margins, where HN pushes back, and what it means for model choice.
7 min read|Read →
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