You can absolutely extrapolate the results, because what this shows is that even when "reasoning" these models are still fundamentally repeating in-sample patterns, and that they collapse when faced with novel reasoning tasks above a small complexity threshold.

That is not a model-specific claim, it's a claim on the nature of LLMs.

For your argument to be true would need to mean that there is a qualitative difference, in which some models possess "true reasoning" capability and some don't, and this test only happened to look at the latter.