The major reason people use threads is to speed up compute intensive tasks. Ideally, pure computation like the one in your example should get a speed up linear

The major reason people use threads is to speed up compute intensive tasks. Ideally, pure computation like the one in your example should get a speed up linear to number of threads. 2 threads -> 2x faster. 3 threads -> 3x faster, up to a number of cores. If that's not happening then you have excessive locking. In Python excessive locking is caused by GIL - Global Interpreter Lock. As the name implies, there's a single, global lock and execution in a single threads effectively blocks all other threads because it holds GIL captive. What this particular benchmarks is showing is that GIL is as bad as everyone says: instead of getting 16x speedup, you get speedups that are almost within margin of error for such a coarse measure.