TL;DR Option 1 is the best choice IMHO
It seems to me there needs to be a better rationale as to the justification of free threading in Python - why would we want that, what use cases are we looking to enable exactly? Is it worth (potentially) upending the ecosystem for these use cases?
Alas I propose the mere fact that it can be done is not a good enough reason.
In my 12+y of practical experience in using Python (previously 18y+ with Java & other languages) for web, full stack and data science use cases within an enterprise context, I have never found any problem with the GIL. For CPU bound parallelism multiprocessing works fine, if more parallel processing power is needed, multi-node is the way to go; for latency bound threading is usually a good option, although multiprocessing works in this case too.
One argument that is often brought up in my discussions with fellow devs is along the lines of “I don’t want to choose between threading or multiprocessing, I just want the code to do what it does already in parallel & faster”. While it seems sensible to want that, of course it is not that easy. With this expectation, the GIL most of the time becomes a point of frustration only because the first thing that gets tried is multi threading. It seems natural to do so: threads are among the top ideas that come up when looking up ways to make computers do things concurrently. Next thing you know, there is a complaint about the GIL limitting performance, when in fact the root cause is the wrong choice of the concurrency model.
I suggest that we should raise awaress of the best practices to parallel and concurrent task execution. We should also highlight that the general concensus (afaik) is that shared-nothing approaches work best and that you should avoid shared-object concurrency if you can. In this view having the GIL is a blessing in disguise.