Good data. Thanks. I got a bit of a speed-up on python-3.12.5 - 0.599 down from 0.76863, but still slower than 2.7 on the same (old) i7-6700k CPU. { EDIT: All

Good data. Thanks. I got a bit of a speed-up on python-3.12.5 - 0.599 down from 0.76863, but still slower than 2.7 on the same (old) i7-6700k CPU. { EDIT: All versions on both CPUs compiled on gcc-13 -O3, FWIW. } On a different CPU i7-1370P AlderLake P-core (via Linux taskset) I got a 1.5X time ratio (py3.12.5 being 0.371 and py2.7 0.245). Anyway, I should have qualified that it's likely the kind of thing that varies across CPUs. Maybe you are on AMD. And no, I sure don't have access to the CPUs I had back in 2007 anymore. :-) So, there is no danger of this being a very scientific perf shade toss. ;-) Same pypy3 also showed very minor speed-up for being inside a function. So, I think you are probably right about PyPy's optimization there and maybe it does just vary across PyPy versions. Not sure how @mg several levels up got his big 2X boost, but down at the 10s of usec to 10s of ms scale, just fluctuations in OS scheduler or P-core/E-core kinds of things can create confusion which is part of the motivation for aforementioned https://github.com/c-blake/bu/blob/main/doc/tim.md https://github.com/c-blake/bu/blob/main/doc/tim.md. I should perhaps also have mentioned https://github.com/yglukhov/nimpy https://github.com/yglukhov/nimpy as a way to write extensions in Nim. Kind of like Cython/SWIG like stuff, but for Nim.