The PyPy website suggests this is the case.

https://www.pypy.org/features.html

> Our main executable comes with a Just-in-Time compiler. It is really fast in running most benchmarks—including very large and complicated Python applications, not just 10-liners.

> There are two cases that you should be aware where PyPy will not be able to speed up your code:

> Short-running processes: if it doesn't run for at least a few seconds, then the JIT compiler won't have enough time to warm up.

> If all the time is spent in run-time libraries (i.e. in C functions), and not actually running Python code, the JIT compiler will not help.

It depends on the code. You don't get speed for free by using C extensions with CPython. You have to write code specifically for that extension. To get significant speed benefits from numpy, for example, you need specific knowledge of numpy and the code produced will be completely different from regular Python. Pypy, on the other hand, is essentially a free speed boost when you have standard python code.

I've never heard of that.

What I have heard, though, is that string operations are more expensive in pypy than cpython.

Regardless, performance gains and regressions are unlikely to generalize; what's true for someone else's project may not be true for yours. If you're concerned about performance, I recommend benchmarking your own codebase.

It's true. Source: I benchmarked some PyGame pixel pushing. HPy might alleviate that in the future, but C extensions need to switch to HPy for that.