GLM-5.3: How Chinese labs keep stride with the frontier

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Housekeeping: I’m traveling so cannot make a voiceover for this post. EDIT — I added a bullet point 5 on the Chinese data industry after sending the email out.Today, Z.ai announced their GLM-5.3 model, currently only available in the coding plan, coming soon to their API and in two weeks’ time to Hugging Face (open weights). This model looks exceptional, with a somewhat astounding increase in scores. On many benchmarks the model has surpassed Moonshot AI’s Kimi K3 and on some it’s surpassed Claude Fable 5 or GPT-5.6-Sol.Here’s a more complete comparison:This puts the model more or less at the frontier of agentic coding benchmarks, with only ~750B parameters – a third of Kimi K3! The Z.ai blog post is rather straightforward, and starts with a bold sentence:Scaling post-training is all we did for GLM-5.3.GLM-5.3 is the same base model as GLM-5.2 with substantially extended post-training. To risk a broad oversimplification, Z.ai seems to have a strength in post-training when compared to Kimi, which is more of a pretraining masterpiece. Following this release there have been a lot of discussions wondering how China can keep up so well? How can such a small model be matching the leading public American models? Are these results real?Subscribe nowThe simplest explanation is that Z.ai is very good at what they do – it’s worth recalling that they’ve been working on this line of models longer than almost anyone in the industry. Here’s a brief history of the GLM models.Zhipu AI Founded – 2019GLM (General Language Model) — March 2021 — released by THUDM, Tsinghua University’s Data …

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