JAX vs MLX in 2026

Q: Is JAX free to use?

The JAX facts used here give its licence as Apache-2.0 . They do not state a price, so no price is published on this page. A licence sets the terms for using the code, and a price is a separate question. Beyond that licence, the JAX facts used here say nothing about what using it costs. Check the project's own pages for any terms beyond the licence before you rely on it.

Q: Does PyOverdrive need Python 3.12 or newer?

Yes, PyOverdrive needs Python 3.12 or newer . It is installed with pip straight from its GitHub repository . The package is marked OS independent, and its test suite has been run on Windows and on Linux x86-64 . Its version is 1.0.0 and its status is Beta .

JAX and MLX compared on what each does for a developer, their licences, how each is installed, and what to check before you choose between them.

By SayDeploy · 2026-10-30

SayDeploy is published by LunarWerx, which makes some of the tools compared here.

PyOverdrive is also set out here, because it patches supported NumPy functions so that existing NumPy code can use faster paths ^[1].

Option	Licence	Install	Price
JAX	Apache-2.0 ^[2]	pip install -U jax for the CPU build ^[2]	not published
MLX	not published	not published	not published
PyOverdrive	MIT License ^[1]	pip, straight from the GitHub repository ^[1]	not published

The short answer

JAX uses XLA to compile NumPy programs so they run on TPUs, GPUs and other hardware accelerators ^[2]. MLX computes its arrays only when they are needed ^[3], and it has a C++ API that closely follows its Python API ^[3]. PyOverdrive is built for code you already have, and a single call to switch it on patches supported NumPy functions so your existing code uses faster paths ^[1]. Which one fits depends on the kind of work you do, as the sections below explain.

What JAX and MLX are built for

The JAX page says it uses XLA to compile and scale NumPy programs on TPUs, GPUs and other hardware accelerators ^[2]. Its licence is Apache-2.0 ^[2].

The MLX page says its arrays are only computed when needed ^[3]. The MLX page says its design is inspired by frameworks like PyTorch ^[3]. It also has a C++ API that closely follows its Python API ^[3].

What PyOverdrive is built for

PyOverdrive is made by the team that publishes this site. A single call to switch it on patches supported NumPy functions so your existing code uses faster paths, and calls it does not support run on stock NumPy ^[1]. A faster path runs only when a check confirms the input is in its measured range, and anything else, or a faster path that raises an error, goes to stock NumPy ^[1]. For some functions, such as np.sin, np.exp and np.log on C-contiguous float64 or float32 arrays, the threaded versions run above a size floor set for each function and data type ^[1]. Large np.einsum calls go through NumPy's own optimize=True planner once they pass a measured size threshold ^[1]. A built-in check compares every faster path with stock NumPy on your own machine ^[1]. A built-in timing run takes about 20 seconds and compares the headline operations with stock NumPy on your machine ^[1].

Pricing and fees

The JAX facts used here give its licence as Apache-2.0 ^[2]. They do not state a price, so no price is published on this page.

The MLX facts used here do not state a licence or a price, so neither is published on this page.

PyOverdrive is open source under the MIT License ^[1], and no price is published for it here.

A licence sets the terms for using the code, and a price is a separate question. A licence alone does not tell you what you will pay. Check each project's own pages for current terms before you commit to a setup.

Setting it up and daily use

The JAX page gives pip install -U jax as the command for the CPU build ^[2]. The MLX facts used here give no install steps, so check the MLX documentation before you start.

PyOverdrive is installed with pip straight from its GitHub repository ^[1]. Once it is installed, one call in your code switches it on ^[1]. A check can report which path a call would take and why, without running the call ^[1]. You can switch off one faster path with a setting or a call, or undo everything with one call that restores the original NumPy functions ^[1]. A calibration step re-times the threaded paths on your CPU and switches off any that do not pay there ^[1].

Things to check before you choose

Ask which hardware your code runs on. The JAX page says it compiles NumPy programs for TPUs, GPUs and other hardware accelerators ^[2]. The MLX facts used here do not name the devices it runs on, so check its documentation for that.

Ask whether you need a C++ route. MLX has a C++ API that closely follows its Python API ^[3], so check whether your project is written in C++, Python or both.

Ask whether your code already uses NumPy. PyOverdrive works on supported NumPy functions, and calls it does not support run on stock NumPy ^[1].

Ask how you want unsupported inputs handled. A faster path runs only when a check confirms the input is in its measured range, and anything else goes to stock NumPy ^[1].

Ask which Python version you use. PyOverdrive needs Python 3.12 or newer ^[1].

Ask whether a Beta release fits your work. PyOverdrive's version is 1.0.0 and its status is Beta ^[1].

Ask which operating systems matter to you. The package is marked OS independent, and its test suite has been run on Windows and on Linux x86-64 ^[1].

How to choose

If you need NumPy programs compiled for TPUs, GPUs and other hardware accelerators, the JAX page describes that ^[2].

If you want arrays that are only computed when needed, the MLX page describes that ^[3].

If you want a C++ API that closely follows the Python one, the MLX page describes that ^[3].

If you want to keep your existing NumPy code and have supported functions take faster paths, PyOverdrive's claims describe that ^[1].

If you want to compare those faster paths with stock NumPy on your own machine, PyOverdrive's claims describe a built-in check for that ^[1].

If you want to know which licence applies, JAX is Apache-2.0 ^[2] and PyOverdrive is under the MIT License ^[1].

Frequently asked questions

Is JAX free to use?

The JAX facts used here give its licence as Apache-2.0 ^[2]. They do not state a price, so no price is published on this page. A licence sets the terms for using the code, and a price is a separate question. Beyond that licence, the JAX facts used here say nothing about what using it costs. Check the project's own pages for any terms beyond the licence before you rely on it.

What licence does MLX use?

The MLX facts used here do not state a licence, so none is named on this page. They do say that its arrays are only computed when needed ^[3], and that its design is inspired by frameworks such as PyTorch ^[3]. To find its licence and any price, check the MLX documentation before you decide.

Can PyOverdrive make existing NumPy code run faster?

A single call to switch it on patches supported NumPy functions so your existing code uses faster paths ^[1]. Calls it does not support run on stock NumPy ^[1]. A faster path runs only when a check confirms the input is in its measured range, and anything else goes to stock NumPy ^[1]. A built-in timing run compares the headline operations with stock NumPy on your machine ^[1].

Does PyOverdrive need Python 3.12 or newer?

Yes, PyOverdrive needs Python 3.12 or newer ^[1]. It is installed with pip straight from its GitHub repository ^[1]. The package is marked OS independent, and its test suite has been run on Windows and on Linux x86-64 ^[1]. Its version is 1.0.0 and its status is Beta ^[1].

Can PyOverdrive compare its faster paths with stock NumPy?

A built-in check compares every faster path with stock NumPy on your own machine ^[1]. You can switch off one faster path, or call one function to restore the original NumPy functions ^[1]. A check also reports which path a call would take and why, without running the call ^[1]. Together these steps let you verify the behaviour in your own environment before relying on it.

It is open source under the MIT License. ^[1]

Try PyOverdrive