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JAX vs Taichi in 2026

JAX and Taichi compared on what each is built for, their licenses and install steps, with PyOverdrive as a NumPy speed-up option for comparison.

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JAX's sources describe differentiation, which works out how a result changes as an input changes, in reverse-mode and forward-mode forms [1]. Taichi is a language built into Python for writing portable, high-performance parallel programs [2]. PyOverdrive patches supported NumPy functions so existing code uses faster paths [3].

OptionLicensePriceInstall
JAXApache-2.0 license [1]not publishedCPU install command: pip install -U jax [1]
Taichinot publishednot publishedpip install taichi -U [2]
PyOverdriveMIT License [3]not publishedpip, straight from the GitHub repository [3]

The short answer

JAX's sources describe differentiation, which works out how a result changes as an input changes, in reverse-mode and forward-mode forms [1]. Taichi is a language built into Python for writing portable, high-performance parallel programs [2]. Its sources also describe an automatic differentiation system [2].

For automatic differentiation, JAX's sources describe that feature [1], and Taichi's sources do too [2]. For faster existing NumPy code, PyOverdrive's claims describe a path that applies to supported functions only, with other calls running on stock NumPy [3].

What JAX and Taichi are built for

JAX's own page says it supports reverse-mode differentiation, also called backpropagation, and forward-mode differentiation [1]. Its page gives the CPU install command as pip install -U jax [1]. Its license is Apache-2.0 [1].

Taichi's page describes it as a domain-specific language, which means a language built for one kind of task, embedded in Python for writing portable, high-performance parallel programs [2]. The page also describes an automatic differentiation system, and shows an example of soft robot locomotion control powered by that system [2]. Its contact address is [email protected] [2].

What PyOverdrive is built for

PyOverdrive is made by the team that publishes this site. Turning it on patches supported NumPy functions so your existing code uses faster paths, and calls it does not support run on stock NumPy [3]. 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 [3]. A built-in self-check compares every faster path with stock NumPy on your own machine [3]. A calibration run re-times the threaded paths on your processor and switches off any that do not pay there [3]. You can ask which path a call would take and why, without running the call [3]. You can turn off one faster path, or restore the original NumPy functions [3].

Threaded paths cover sine, cosine, tangent, exponential, logarithm and tanh on large arrays of 64-bit or 32-bit decimal numbers stored in one continuous block, once an array passes a size set for each operation and number type [3]. For large stacks of 3x3 matrices, a faster method runs for a QR calculation, and for float batches of 2x2 and 3x3 matrices above a size threshold, a faster method runs for eigenvalue calculations [3]. Large einsum calls go through NumPy's own planner once they pass a measured size [3]. On one-dimensional text or object arrays, np.isin can use a hash-based lookup instead of the default method [3].

Pricing and fees

JAX's sources give its license as Apache-2.0 [1]. No price for JAX appears in these sources, so none is published here.

Taichi's sources give no price and no license here, so neither is published on this page.

PyOverdrive is open source under the MIT License [3]. No price for PyOverdrive is published here.

Setting it up and daily use

JAX's page gives the CPU install command as pip install -U jax [1]. For Taichi, the install line is in the comparison table above. The sources here say nothing more about daily use for either project, so check each project's own documentation.

PyOverdrive installs with pip straight from the GitHub repository [3]. It needs Python 3.12 or newer [3]. The package is marked OS independent, and its test suite has been run on Windows and on Linux x86-64 [3]. The version is 1.0.0 and its status is Beta [3]. A built-in demo times the headline operations against stock NumPy on your own machine in about 20 seconds [3].

Things to check before you choose

Which license does your project need? JAX is Apache-2.0 [1], and PyOverdrive is under the MIT License [3]. The sources here give no license for Taichi, so check that project's terms directly.

Does your code use NumPy? PyOverdrive works on supported NumPy functions, and calls it does not support run on stock NumPy [3].

Is your Python new enough? PyOverdrive needs Python 3.12 or newer [3].

Do you need automatic differentiation? JAX's sources describe differentiation [1]. Taichi's sources describe an automatic differentiation system [2]. Compare the details in each project's documentation.

How to choose

If you need differentiation in your code, JAX's sources describe reverse-mode and forward-mode differentiation [1]. Taichi's sources describe an automatic differentiation system [2].

If you want to write portable, high-performance parallel programs in Python, Taichi's sources describe a language for that [2].

If you have NumPy code and want supported functions to use faster paths, with other calls running on stock NumPy, PyOverdrive's claims describe that [3].

If you want to compare results with stock NumPy on your own machine, PyOverdrive's self-check does that [3].

Frequently asked questions

Is JAX free to use?

JAX is licensed under Apache-2.0 [1]. A license sets the terms on which the code can be used, so read the license text before you rely on JAX in your project. No price for JAX appears in the sources used for this page, so no price is published here. The project's own pages are the place to confirm any terms that apply to your use.

Is Taichi free to use?

The sources used for this page give no license and no price for Taichi, so neither is published here. Its contact address is [email protected] [2]. Its page describes a language for writing portable, high-performance parallel programs [2]. Check the project's own license terms and any pricing before you use Taichi in a commercial setting.

How do you install JAX, Taichi and PyOverdrive?

JAX's CPU install command is pip install -U jax [1]. For Taichi, the install line is in the comparison table above. PyOverdrive installs with pip straight from the GitHub repository [3], and it needs Python 3.12 or newer [3]. Check the install step in each project's documentation before you start, since the sources used here do not go into more detail.

Does PyOverdrive change how NumPy works?

PyOverdrive patches supported NumPy functions so existing code uses faster paths, and calls it does not support run on stock NumPy [3]. A faster path runs only when a check confirms the input is in its measured range, and anything else goes to stock NumPy [3]. You can compare results with stock NumPy on your own machine using the self-check [3].

Does JAX support automatic differentiation?

JAX supports reverse-mode differentiation, also called backpropagation, and forward-mode differentiation [1]. Taichi's sources describe an automatic differentiation system as well [2]. The facts used here do not compare the two in detail, so read each project's documentation for the specifics you need before you choose.

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

Try PyOverdrive

Sources

  1. JAX, github.com/jax-ml/jax, read 2026-10-07
  2. Taichi, taichi-lang.org, read 2026-10-20
  3. PyOverdrive's own description of itself (the product of this site's publisher)

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