Why PyPy Can Run Python Faster Than C: JIT Explained with Benchmarks

Why PyPy Can Make Python Faster Than C

Guido van Rossum once said that to make code run faster you should use PyPy. For researchers who need to prototype ideas quickly, Python is ideal but its execution speed is much slower than compiled languages such as C or C++.

PyPy solves this problem by providing a just‑in‑time (JIT) compiler that can make Python code run faster than even native C implementations.

import time
from termcolor import colored

start = time.time()
number = 0
for i in range(100000000):
    number += i

print(colored("FINISHED", "green"))
print(f"Ellapsed time: {time.time() - start} s")

The benchmark compares the default CPython interpreter, PyPy, and a C version of the same loop. CPython takes about 10 seconds, PyPy finishes in 0.22 seconds, and the C program needs 0.32 seconds, showing that PyPy’s JIT can outperform even compiled C in this case.

Ahead‑of‑Time (AOT) Compilation

Languages such as C, C++, Swift, Haskell, and Rust are typically compiled ahead of time. The compiler translates source code into machine code that runs directly on the hardware.

Interpreted Languages

Languages like Python, JavaScript, and PHP are interpreted: the source code remains unchanged and an interpreter reads and executes it line by line each time the program runs.

Just‑in‑Time (JIT) Compilation in PyPy

PyPy uses JIT compilation, which combines the flexibility of interpretation with the speed of ahead‑of‑time compilation. Before execution, parts of the code are compiled into machine code, allowing the runtime to optimize performance.

Because of this JIT approach, PyPy can execute Python programs much faster, and users seeking speed should consider switching from the default interpreter.