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

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. 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. 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…