SayDeploy Plain comparisons of developer tools, AI coding tools included

Numba vs Pythran in 2026

Numba, Pythran and PyOverdrive compared on what each does, what each costs, how each is set up and what to check before you choose one.

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Numba turns Python and NumPy code into fast machine code at runtime, so a developer can speed up numerical functions without rewriting them [1]. Pythran compiles Python code ahead of time, so a developer gets a faster binary to run for scientific computing work [2]. Neither source gives a price. One call turns on faster paths for supported NumPy functions in existing code, and calls that are not supported run on the standard NumPy [3]. The product is open source under the MIT License [3].

OptionPriceLicencePython versionsInstallHow it works
NumbaNot publishedOpen source [1]Python 3.9-3.12 [1]Not publishedCompiles Python to machine code at runtime using LLVM, a widely used compiler toolkit [1]
PythranNot publishedBSD-3-Clause [2]Python 3.7 and upward only [2]Not publishedAhead of time compiler for a subset of Python [2]
PyOverdriveNot publishedMIT License, open source [3]Python 3.12 or newer [3]pip straight from the GitHub repository [3]One call speeds up supported NumPy functions [3]

The short answer

Numba translates Python functions to optimized machine code at runtime using the LLVM compiler library [1]. Pythran is an ahead of time compiler for a subset of Python aimed at scientific computing [2]. One call speeds up supported NumPy functions in existing code, and calls that are not supported run on the standard NumPy [3].

The Numba page claims that numerical algorithms compiled by Numba can approach the speeds of C or FORTRAN [1]. PyOverdrive's claims cover parallel versions of common math functions on large arrays, so a developer gets faster math on big data without changing code [3]. A faster path runs only for inputs it has been checked on, and anything else runs on the standard NumPy [3].

What Numba and Pythran are built for

Numba. The page says numerical algorithms compiled by Numba can approach the speeds of C or FORTRAN [1]. Numba works with Python versions 3.9 through 3.12 [1].

Pythran. Its page says it is an ahead of time compiler for a subset of Python aimed at scientific computing [2]. The page says it now only supports Python 3.7 and upward [2]. It is BSD-3-Clause licensed [2].

What PyOverdrive is built for

PyOverdrive is made by the team that publishes this site.

One call turns on faster paths for supported NumPy functions in existing code, and calls that are not supported run on the standard NumPy [3]. A faster path runs only for inputs it has been checked on, and anything else, or a faster path that raises an error, runs on the standard NumPy [3].

For large arrays, the product runs common math operations across multiple CPU cores instead of one, so a developer gets faster results on big numeric data without changing code [3]. For large batches of small 3x3 matrices, the product uses a faster method for certain linear algebra operations [3]. Large einsum calls use NumPy's own planner, which can make them faster [3]. For certain lookups on one-dimensional arrays, the product can use a faster method than the default [3].

The developer can run a check on their own machine that compares each faster path with the standard NumPy [3]. The developer can also run a demo that times the main operations with and without the product in about 20 seconds [3]. The developer can ask which path a call would take and why, without running the call [3].

Pricing and fees

Numba is open source [1]. The Numba facts given here state no price, fee or paid plan, so a price is not published here.

Pythran is BSD-3-Clause licensed [2]. The Pythran facts given here state no price, fee or paid plan either, so a price is not published here.

PyOverdrive is open source under the MIT License [3]. Its claims state no price and no paid tier, so a price is not published here.

A licence describes how you may use the code.

Setting it up and daily use

The Numba facts given here do not describe installation steps. Its page lists Python 3.9-3.12 [1]. The sources here say nothing more about its setup or routine use.

Pythran's page says it now only supports Python 3.7 and upward [2]. The sources here say nothing about how it is installed or used day to day.

PyOverdrive is installed with pip, which is the standard tool for installing Python packages, straight from the GitHub repository [3]. It needs Python 3.12 or newer [3]. The test suite has been run on Windows and on Linux x86-64 [3]. The version is 1.0.0 and it is an early release [3].

Daily, the developer calls one function once to turn on the faster paths [3]. A calibration command re-times the faster paths on the developer's own CPU and switches off any that do not help there [3]. The developer can turn off one faster path, or restore the original NumPy functions [3].

Things to check before you choose

Ask which Python version your project runs. Numba's page lists Python 3.9-3.12 [1], Pythran's page says it supports Python 3.7 and upward [2], and PyOverdrive needs Python 3.12 or newer [3].

Ask how much of your code falls inside the subset each compiler handles. Check whether Numba supports the Python and NumPy functions your project uses [1], and check whether Pythran covers the Python your scientific computing code relies on [2].

Ask where the code will run. The PyOverdrive test suite has been run on Windows and on Linux x86-64 [3]. For Numba and Pythran, the facts here do not cover which platforms they run on, so check their own pages.

Ask how mature the tool is. PyOverdrive is version 1.0.0 and it is an early release [3].

Ask for evidence on your own data. The developer can run a check on their own machine that compares the faster paths with the standard NumPy [3]. For Numba and Pythran, the facts here do not cover install steps or support terms, so check their own pages.

How to choose

If you want to speed up numerical Python functions by compiling them to machine code at runtime, Numba's page describes that [1].

If you want numerical algorithms in Python that can approach the speeds of C or FORTRAN, Numba's page says that [1].

If you want an ahead of time compiler for a subset of Python aimed at scientific computing, Pythran's page describes that [2]. If you run Python 3.7 or newer, Pythran's page says it supports those versions [2].

If you want one call to speed up supported NumPy functions in existing code, with calls that are not supported running on the standard NumPy, PyOverdrive's claims describe that [3]. If you want to check which path a call would take and why, or turn faster paths off, its claims cover that too [3] [3].

Frequently asked questions

Is Numba free to use?

Numba is open source [1]. The facts given here state no price, fee or paid plan for Numba, so a price is not published here.

Is Pythran open source?

Pythran is BSD-3-Clause licensed [2]. The facts given here state no price for Pythran, so a price is not published here. Its page says it compiles a subset of Python ahead of time into a native binary for scientific computing [2].

Which Python versions do Numba, Pythran and PyOverdrive support?

Numba's page lists Python 3.9-3.12 [1]. Pythran's page says it now only supports Python 3.7 and upward [2]. PyOverdrive needs Python 3.12 or newer [3]. Compare the version your project runs today against each line before you install, and check the project pages for the latest list.

How do you install PyOverdrive?

You install it with pip, which is the standard tool for installing Python packages, straight from the GitHub repository [3]. It needs Python 3.12 or newer [3]. The test suite has been run on Windows and on Linux x86-64 [3]. The version is 1.0.0 and it is an early release [3]. The facts given here do not cover install steps for Numba or Pythran.

Does Numba or Pythran use more than one CPU core?

The facts given here do not say whether Numba or Pythran use several CPU cores, so check their own pages. PyOverdrive runs some math operations across multiple CPU cores on large arrays, so a developer gets faster results on big numeric data without changing code [3].

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

Try PyOverdrive

Sources

  1. Numba, numba.pydata.org, read 2026-10-07
  2. Pythran, github.com/serge-sans-paille/pythran, read 2026-10-07
  3. PyOverdrive's own description of itself (the product of this site's publisher)

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