That said, if you're talking about models you can actually use on a single regular computer that costs less than a new home, the current crop of open models are very capable but also have noticeable limitations.
Small models will always have limitations in terms of capability and especially knowledge. Improved training data and training regiment can squeeze out more from the same number of weights, but there is a limit.
So with that in mind, I think such a question only makes sense when talking about specific tasks, like creative writing, data extraction from text, answering knowledge questions, refactoring code, writing greenfield code, etc.
In some of these areas the smaller open models are very good and not that far behind. In other areas they are lagging much more, due to their inherent limitations.
That said, open models are not far behind SOTA, less than 9 months gap.
If what you're asking about those models that you can run on retail GPUs, then they're a couple years behind. They're "hobby" grade.
That’s the conventional view. I think there’s another angle: train a local model to act as an information agent. It could “realize” that, yeah, it’s a small model with limited knowledge, but it knows how to fetch the right data. Then you hook it up to a database and let it do the heavy lifting.