Just some extra info to add to this for OP/others. The keyword to Google to learn more about this would be a "JIT compiler". If you were to run each of those be

Just some extra info to add to this for OP/others. The keyword to Google to learn more about this would be a "JIT compiler". If you were to run each of those benchmarks to loop only a few times (or steps for the n-body example), say n=10, they would appear much slower because the JIT compiler needs lines of code to be run multiple times before it can begin making optimizations. "pypy" is an alternative runtime for Python which can do JIT optimizations like Javascript interpreters do. Another commenter in a different subthread already posted a quick speed comparison involving pypy, it's much faster than the standard python runtime (called CPython). But pypy isn't necessarily a synonym for Python (which almost always means CPython). It doesn't have 100% compatibility with 3rd party libraries (it's probably 99.9% but just 1 unsupported dependency is enough to prevent your whole project from going pypy). And also with most Python projects, the python code isn't the slow part of the stack. If you're making a web app, your cache and database is probably where 99% of your request time is spent. For scientific computing, data analysis, or ai training, you're usually using 3rd party libraries written in a "fast" language like C/Fortran/Rust with python hooks, and your python just acts as glue code to pass data between these library calls.