[arc776]

Of course bigints require more than one instruction to add them, but even then you can reduce the work at compile time down to a series of integer operations, whereas the above code requires interpretting the program before it even gets to the add.

In your example text processing in `unicode_concatenate` is going to be very, very much slower than a bulk load of the native numerical data directly from memory and processing it. For each character, Python needs to check a number is still a number at run time then convert the result to a native numeric. I can only assume this string processing is at worst performed once and cached(?), because otherwise it doesn't seem like it would run well at all and surely Python's bigint performance is pretty important.

> Python code can be made more high performance if there's some way to tell the implementation the types, either explicitly or by inference or tracing.

At that stage, I would just use Nim and get better performance and a decent static type system included and either call it from Python, or call Python from Nim.

[eesmith]

You did write "two numbers" ;)

Guess I could also have used 5j + 3 as a counter-example.

If this is an issue then at this stage, many Python people switch to use one of the alternatives mentioned here, like Cython, which is a Python-like language which includes a static type system (including support for C++ templates) and can easily generate C extensions that can call and be called from Python.