I write something similar for my own problems. Usually a single instance alone isn’t going to cut it, so we run a portfolio of solvers and treat it like a multi armed bandit problem as to choose which solver with its specific hyper parameters is finding solutions / counter examples faster. A pretty common trick when you do this is to dynamically resize your portfolio with new solvers introduced with a high likelihood of solving fast based off the parameters of other solvers that are solving fast, a particle filter of sorts. This takes care of setting and handling individual solver timeouts, while allowing the user to specify not only a timeout for the whole portfolio, possibly lengthening when new solvers are introduced, but also a max delay between any two solutions.

Either way, you’re still forced to consider if the model can return sat or unsat, or a timeout. If it’s a timeout we don’t swap in the faster code, if it’s unsat then same deal, but if it’s sat we can get some new code to jump to

That's very unlikely to happen, since finding a counterexample or proving there isn't one is much easier than finding a program candidate, so it'll usually time out in that phase.