I've used python-mip extensively, though only on problems with up to ~10k variables, and only using basic LP features (no advanced solver callbacks). python-mi

I've used python-mip extensively, though only on problems with up to ~10k variables, and only using basic LP features (no advanced solver callbacks). python-mip is substantially faster than cvxpy, pulp, and pyomo for constructing _and solving_ a very simple LP with 10k variables. It's on par even with cylp using vectorisation (maybe I haven't used cylp as well as I could (either way cylp is dreadful to use)). I say _and solving_ because for my use case it's the time to construct and solve relatively simple & small problems (like, <100ms overall) that I'm interested in, which includes anything like serialising the problem to a file and invoking the solver. But keep in mind these kinds of benchmarks can depend a lot on how you're using the library. Like, are you spending almost all time actually solving the model? Then it probably doesn't matter what library you use to construct the model. python-mip in general is pretty nice. It supports advanced solver features if you're into that sorta stuff. But their typing is a bit whacky (no py.typed file, use numbers.Real for typing which makes type checking a bit whacky). And it relies on a funky pre-compiled CBC binary (I had to compile it from scratch at a very specific commit to get it working on M1). python-mip creates/modifies variables in the underlying solver library (CBC/Gurobi) as you create/modify variables through the python interface (I believe this is an uncommon approach, but good). The internal data model of variables & linear expressions is quite clean and tidy. Overall would recommend python-mip if you just need to solve MILP problems. From my scrappy benchmark constructing and solving a very simple LP with 10k variables: 'benchmark_python_mip' took: 40.33 ms 'benchmark_cvxpy' took: 738.11 ms 'benchmark_pulp' took: 152.68 ms 'benchmark_pyomo' took: 191.66 ms 'benchmark_cylp' took: 38.91 ms