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Cython vs Numba in 2026: Pricing, Setup

Cython and Numba compared with PyOverdrive for what they do, costs, setup, daily use and questions to ask before choosing.

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Cython compiles Python and an extended Cython language ahead of time, and Numba turns supported Python and NumPy code into machine code while the program runs. [1] [2] PyOverdrive patches supported NumPy functions so existing code uses faster paths while unsupported calls run on stock NumPy. [3] Cython's sources describe compiling Python and Cython language code, Numba's sources describe translating supported Python and NumPy code at runtime, and PyOverdrive's claims describe changing supported NumPy calls without changing existing code. [1] [2] [3]

OptionPriceLicenseSetup
CythonFreely available under the open source Apache 2.0 License. [1]Apache 2.0 License. [1]Setup details not published in the provided facts.
NumbaPrice not published. [2]Open source JIT compiler. [2]Python 3.9-3.12 support stated. [2]
PyOverdrivePrice not published. [3]Open source under the MIT License. [3]Install with pip straight from the GitHub repository; needs Python 3.12 or newer. [3] [3]

The short answer

Cython is an optimising static compiler for Python and the extended Cython language. [1] Numba is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code. [2] PyOverdrive lets existing NumPy code use faster paths after calling pyoverdrive.enable(), while unsupported calls run on stock NumPy. [3]

Cython's sources describe a tool for compiling Python and Cython language code. [1] Numba's sources describe a tool for translating supported Python and NumPy code at runtime. [2] PyOverdrive's claims describe a way to patch supported NumPy functions and keep unsupported calls on stock NumPy. [3]

What Cython, Numba is built for

Cython's own page says Cython is an optimising static compiler for both the Python programming language and the extended Cython programming language. [1] Cython is freely available under the open source Apache 2.0 License. [1]

Numba's page says it is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code. [2] Numba translates Python functions to optimized machine code at runtime using the LLVM compiler library. [2] Numba is designed to be used with NumPy arrays and functions. [2] Numba also works with Jupyter notebooks for interactive computing and distributed execution frameworks like Dask and Spark. [2]

What PyOverdrive is built for

PyOverdrive is made by the team that publishes this site.

PyOverdrive lets existing NumPy code use faster paths after calling pyoverdrive.enable(), and calls it does not support run on stock NumPy. [3] A fast path runs only when a check confirms the input is in its measured range, and anything else or a fast path that raises an error goes to stock NumPy. [3]

PyOverdrive can compare every fast path with stock NumPy on the developer's own machine using python -m pyoverdrive --selfcheck. [3] It can retime threaded paths at their own thresholds on the CPU and switch off any that do not pay there using python -m pyoverdrive --calibrate. [3]

PyOverdrive can show which path a call would take and why without running the call. [3] A developer can turn off one fast path with disable_path() or PYOVERDRIVE_DISABLE, or restore the original NumPy functions with pyoverdrive.disable(). [3]

Pricing and fees

Cython is freely available under the open source Apache 2.0 License. [1] Numba is open source, and its page describes it as an open source JIT compiler. [2] PyOverdrive is open source under the MIT License. [3]

Cython's source mentions PayPal fees for small non-EUR payments to the project, where PayPal takes 5-15% fees. [1] The sources provided here do not publish a price for Numba. [2] The sources provided here do not publish a price for PyOverdrive. [3]

Setting it up and daily use

Cython's sources say Cython 3.0.x supports Python 2.7 and 3.5 and later. [1] Cython's sources say support for the CPython Limited API and free-threading CPython is available in Cython 3.1 but is considered experimental. [1]

Numba's sources say it supports Python 3.9-3.12. [2] Numba's sources say it supports Intel and AMD x86, POWER8/9, and ARM CPUs including Apple M1. [2]

PyOverdrive needs Python 3.12 or newer. [3] PyOverdrive is installed with pip straight from the GitHub repository. [3] The package is marked OS independent, and the test suite has been run on Windows and Linux x86-64. [3]

Things to check before you choose

Check whether you need a compiler for Python code, runtime translation of supported Python and NumPy code, or changes to existing NumPy calls. Cython, Numba and PyOverdrive describe different ways of working with code. [1] [2] [3]

Check the Python versions and operating systems that matter for your project. The sources give different compatibility details for each tool. [1] [2] [3] [3]

Check the code patterns you need to improve and whether the tool's documented approach matches them.

How to choose

If you need an optimising static compiler for Python and the extended Cython language, Cython's source describes that use. [1]

If you need a JIT compiler that translates a subset of Python and NumPy code into machine code at runtime, Numba's source describes that use. [2]

If you need existing NumPy code to use supported faster paths after enabling a package, PyOverdrive's claims describe that use. [3]

If you need checks on your own machine before using a fast path, PyOverdrive's claims describe self-checking and calibration commands. [3] [3]

Frequently asked questions

Is Cython free to use?

Cython is freely available under the open source Apache 2.0 License. [1] The provided facts do not state another Cython price. The sources here describe Cython as an optimising static compiler for Python and the extended Cython language. [1]

What is Numba used for?

Numba is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code. [2] Numba's page says it is designed to be used with NumPy arrays and functions. [2] It also states support for Jupyter notebooks and distributed execution frameworks like Dask and Spark. [2]

How does PyOverdrive change NumPy code?

PyOverdrive patches supported NumPy functions after pyoverdrive.enable() is called so existing code uses faster paths, while calls it does not support run on stock NumPy. [3] A fast path only runs after a check confirms the input is in its measured range. [3]

What Python versions do these tools support?

Cython's page says Cython 3.0.x supports Python 2.7 and 3.5 and later. [1] Numba's page states Python 3.9-3.12 support. [2] PyOverdrive needs Python 3.12 or newer. [3]

Can PyOverdrive check its fast paths?

PyOverdrive can compare every fast path with stock NumPy on the developer's own machine using python -m pyoverdrive --selfcheck. [3] It can also retime threaded paths at their thresholds on the CPU and switch off paths that do not pay there using python -m pyoverdrive --calibrate. [3]

It is open source under the MIT License. [3]

Try PyOverdrive

Sources

  1. Cython, cython.org, read 2026-10-07
  2. Numba, numba.pydata.org, read 2026-10-07
  3. PyOverdrive's own description of itself (the product of this site's publisher)

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