I think the reason Google hasn't prioritized larger models that are more intelligent than everyone else's, even though they probably could, is because they have 4 billion active users already. For example, they send AI Overviews for a large portion of Google searches now.
So I think they have to prioritize scaling for their models to a higher degree than other groups. Being within say 5% or so in most cases is probably adequate and matters more overall for their user base than being the absolute best coder. So they may be setting compute constraints for training or inference that are firmer than other teams.
This is probably true in a smaller way for OpenAI
When you have a lot of free users the business demands that you serve them with the best cheap model you can build
And time spent building that may provide dividends (eg OpenAI has very good RL and reasoning) but it might take resources away from the larger model training
(I have no inside knowledge, so please consider this to all be speculation)