We know for sure the architecture of the open weights models since llama.cpp understands the architecture it needs to build to plug the weights into to run them. It's always possible that the latest closed model is doing something architecturally different than the open weights ones we know about, but judging by how close the large open weight models such as DeepSeek are to SOTA performance, this seems unlikely. When OpenAI first came out with their near-mythical "Strawberry" (aka "o1") thinking model there was all sorts of speculation that they had made some sort of architectural breakthough, but then DeepSeek replicated the capability and published how they did it, proving that it was just better training, not any architectural change.

There have been minor changes to the architecture over the years, but these are basically all efficiency tweaks such as various types of attention (some pioneered in the open by DeepSeek) that better scale to large context lengths, and the confusingly named "mixture of experts" architecture, but what's more notable really is how little the architecture has changed. The capability gains have been coming from better training and better data.