TL;DR
A companion guide to the GLM 5.2 video: an open-weight model positioned against GPT-5.5, walked through with benchmarks, pricing, and a live OpenCode demo. Here is what the video covers and where to go deeper.
| Resource | Description |
|---|---|
| Watch: GLM 5.2 in 9 Minutes | The full walkthrough on the DevDigest channel |
| OpenCode | The coding environment used for the live demo |
GLM 5.2 in 9 Minutes explains GLM 5.2 as an open-weight rival to GPT-5.5. The video reviews the model, works through benchmarks and pricing, and finishes with a live demo running GLM 5.2 inside OpenCode.
This post is a companion to the video above. Watch the nine-minute walkthrough for the benchmarks and the live demo, then use the links here to place GLM 5.2 in context.
An open-weight model aimed squarely at a frontier closed model. GLM 5.2 is framed as a direct rival to GPT-5.5, which is the interesting part: the comparison is not open versus closed in the abstract, it is one specific open-weight release measured against a specific proprietary one.
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Three angles make this worth a look:
GLM 5.2 sits in a crowded field of open-weight coding models. If you are weighing access and cost, GLM 5.2 free and cheap access covers how to try it without a big commitment. For the closed-model side of the comparison, the GPT-5.5 hallucination benchmark against GLM 5.2 looks at where each model lands.
The path the video demonstrates is simple: try GLM 5.2 inside a coding environment like OpenCode and judge it on your own tasks. Start with a small, well-scoped job so you can compare its output against a model you already trust before leaning on it for anything larger.
Watch the full GLM 5.2 in 9 Minutes walkthrough above, then run the model against a task of your own and see how the open-weight option holds up.
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