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Sudoku may be a poor example for illustrating some of the overarching points of this series of blog posts, which I take to be that modern SAT solvers are, as someone described them in a previous thread, "little diamonds of engineering", that they embody decades of research, that you can exploit their power cheaply and easily, and that it might take significant effort to beat them if you have a non-trivial problem.

Since specialized solvers do thrash SAT-based solvers for conventional Sudoku, the author focuses the example less on absolute performance and more on simplicity, rapid prototyping, and comparison to casual non-SAT implementations. But conventional Sudoku is a small problem, and in most cases the SAT-based solver makes so few decisions that it doesn't benefit much from CDCL. Consider here[1] the comparison to JCZSolve(ZSolver) that the author mentions in part 1 of the series:

For 17-clue puzzles (which are generally very easy), JCZSolve is 50x as fast as Minisat, and it makes on average ~1.9 decisions per puzzle compared to Minisat's ~3.0. But if you look at the hardest dataset JCZSolve is only 10x as fast as Minisat, and it makes on average ~365 decisions per puzzle compared to Minisat's ~121. I'm not aware of a highly specialized and optimized Solver for 16x16 clue or larger Sudoku, but given this trend my guess is that it would take a lot of effort to write one that's faster than what you get with Minisat for free.

[1] https://github.com/t-dillon/tdoku/blob/master/benchmarks/GCE...