AI commentator Teortaxes claims GLM 5.2 narrows the gap between Chinese and Western frontier models to seven months

@teortaxesTex i think its hard to overstate how much the harness / provider makes a difference when evaluating these models. ~7 months seems reasonable tho (i'd say its closer for coding and further away for things like medical / legal / etc)

@_xjdr there is decay the further you move from the main dimension of competition, ofc. Ultimately the big 3 invest a whole lot more into long tail data. Still, GLM kinda holds up even on legal, it's surprising.

Is'nt it possible that there is some (unvolontary) backchannel from private evals to the big western labs.

While *models* may not be universally superhuman at pattern extraction from low bitrates, deep learning *systems*, if you include the backpropagation phase absolutly are.

Actually excluding all bits of the test set in the training set is more or less impossible without extremely strict date cutoffs, and assuming big western labs have access to private western corporate datasets that aren'nt available to chinese labs, I would assume more bits are leaked (and the opposite being true for chinese specific data of course).

@teortaxesTex 能力分水岭其实比算力更深

The compute point is the important one here. If the gap closed even on hard private evals, then "it's just compute" was always too clean a story.

Compute is necessary, not sufficient. Method, data curation, and post-training do work that more TPUs alone don't buy — which is exactly why a lab with less raw compute can compress a 7-month gap.

Worth flagging though: "7 months" is a snapshot, not a trajectory. Gaps this elastic can widen again the moment the next frontier release lands. The closing is real — just not necessarily monotonic.