The tracking of models sounds very official, but it doesn't automatically mean this data is immediately useful for a daily research dataset.
It's a bit of hype to assume that simply counting models means they're ready to integrate; for instance, a new model might be "available" but completely unstable or riddled with security flaws.
What actually changes for someone trying to use it if the new options constantly break existing integrations?
Just like when a new school software update is released, we see new features, but they often crash our systems, forcing us back to the old way of doing things.