My theory is that these models serve the purpose of being relatively easy to run/tweak for researchers, and mainly serve to demonstrate the effectiveness of new techniques in training and inference, as well as the strength of AI labs that created them.
They are not designed to be state of the art commercial models.
By choosing bigger model sizes, running more training epochs, and drilling the models a bit more on benchmarking questions, I'm sure the Chinese could close the gap, but that would delay these models, make them more expensive and harder to run without showing any tangible research benefit.
Also my 2c: I was perfectly happy with Sonnet 3.7 as of a year ago, if the Chinese have a model really as good as that (not only one that benchmarks as well), I'd definitely like to try it.
GLM-4.7 like a mix of Sonnet 4.5 and GPT-5 (the first version not the later ones). It has deep deep knowledge, but it's often just not as good in execution.
They're very cheap to try out, so you should see how your mileage varies.
Ofcourse for the hardest possible tasks that GPT 5.2 only approaches, they're not up to scratch. And for the hard-ish tasks in C++ for example that Opus 4.5 tackles Minimax feels closer, but just doesn't "grok" the problem space good enough.
Measured by the DCI the Chinese AI models are about 1.5 years ahead of US models.
DCI = Dust42 Capability Index: MBP Max 64GB, Qwen3-80B MLX 4bit quant, 40 tokens per second. It is not on Claude Opus level but very, very useful if you have no internet, i.e. on a flight. And occasionally it surpasses even Opus by far and large. Opus is a pain in the neck once the coding task at hand surpasses its capabilities. Qwen3 is much better to guide to get step by step to a solution.