The performance speedup obtained when using C++ instead of Python can be significant, but it varies widely depending on the nature of the task, the quality of the code, the specific libraries used, and the optimization settings for the C++ compiler. Therefore, it's difficult to provide a precise "typical" speedup factor.

However, in general, C++ is often considered to be faster than Python for a few reasons:

1. **Compiled vs Interpreted**: C++ is a compiled language, which means the source code is translated into machine code before it's run, while Python is an interpreted language, which translates the code into machine language as it runs. Compiled languages like C++ typically run faster.

2. **Low-Level Language**: C++ is a lower-level language compared to Python. It allows more direct control over the hardware, such as memory and CPU usage, which can lead to more efficient code execution.

3. **Type Checking**: C++ is statically-typed and Python is dynamically-typed. This means type checking in C++ is done at compile time, while in Python, it's done at runtime, which can slow down the execution.

In terms of concrete speedup, it's not uncommon to see C++ programs run an order of magnitude faster (i.e., 10 times faster) than equivalent Python programs, or even more for highly computational tasks. But again, this depends heavily on the specifics of the code.

However, it's important to note that while C++ can provide speed advantages, Python has its own strengths. Python code is typically quicker and easier to write and debug, thanks to its simple and clean syntax. Python also has a rich ecosystem of libraries and frameworks, which make it a great language for tasks like data analysis, machine learning, and web development. For many tasks, the ease and speed of development in Python can outweigh the runtime speed benefits of C++. 

Moreover, Python offers ways to improve speed where it counts. For instance, critical parts of the code can be written in C or Cython, or you can use libraries like NumPy and SciPy that have underlying C or Fortran code for heavy computations. Using such methods, Python's performance can approach, and even sometimes match, that of C++ for certain tasks.
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