Which Python Version Should You Use?

Use Python 3.14 for most new projects, or Python 3.15 when your required packages and deployment platform support it. As of October 2026, both are stable releases, but the newest release is not automatically the best fit for every project. For existing code, start with its runtime requirements, then check support status, choose an installer, and test the result in a clean environment.

What Python Version Numbers and Support Stages Mean

Python versions have three components: major, minor, and patch. In a version such as 3.14.8, the first component identifies the language generation, the second identifies the feature release, and the third identifies a maintenance update within that release.

For new work, use Python 3, not the unsupported Python 2 series.

The important distinction is between a version being stable and it receiving the kind of maintenance your project needs:

Bugfix support: The branch receives ordinary bug fixes and security fixes. This is the preferred stage for a new project when its dependencies are compatible.
Security-only support: The branch receives security fixes, but not ordinary bug fixes. Upstream releases at this stage are generally source-only rather than accompanied by new Windows and macOS installers.
End of life: Upstream maintenance has stopped, including security fixes.

New language features arrive in new minor releases, not in routine patch updates. A bugfix-supported branch therefore does not keep gaining language features throughout its supported life.

Alpha, beta, release-candidate, and development builds are useful for compatibility testing. They are not the default choice for learning Python or starting an ordinary production application.

How to Choose and Set Up the Right Python Version

Choose the newest supported Python branch that satisfies the project’s documented runtime, package, tooling, and deployment requirements.

Step 1: Check the Project’s Requirements and Active Interpreter

Check the project’s documentation, pyproject.toml, .python-version, container configuration, and continuous integration configuration for an existing runtime requirement. For an existing application, a known-working, supported runtime takes precedence over a general recommendation. A requires-python entry in pyproject.toml describes an allowed Python range. A minimum version alone does not prove that the application and all its dependencies work on every newer release.
Check which interpreter your terminal currently uses. In the active project environment’s terminal, run:
```
python --version
```
On systems where Python is available under the Python-specific command, run:
```
python3 --version
```
These commands check the interpreter that the particular command resolves to, not every Python installation on the computer. If both commands work, they may resolve to different installations.
Check your required package versions. Use the installation documentation and release information for the versions your project actually needs, including pinned older dependencies. Packages may not yet support the newest Python release. For packages with compiled extensions, also check for a compatible wheel, the prebuilt package format that avoids compiling the package locally. Availability can depend on the Python version, operating system, and processor architecture.

The table matches common project situations to recommended starting branches and the compatibility checks needed to confirm the choice:

Your Situation	Recommended Starting Point	Deciding Check
Learning Python, writing scripts, or starting a general-purpose application	Python 3.14	Required packages and target operating system
Starting a project that can adopt the newest stable branch	Python 3.15	Package, tooling, and deployment support
Joining or maintaining an existing application	Its documented, supported Python branch	Repository configuration and passing tests
Working with machine learning, GPU tools, or specialized extensions	The newest supported branch shared by the required tools	Framework installation matrix and compatible binary packages

Step 2: Choose a Supported Branch

Choose the newest supported Python branch that your entire project can run, not merely the newest one you can download.

Account for the remaining support window. Python 3.15 has a scheduled end of life of October 2031; Python 3.14 has a scheduled end of life of October 2030, per the Python Developer’s Guide. Both are currently in bugfix support. Those end dates include the later security-only phase; they do not mean ordinary bug fixes and installers will continue until then.
Choose the latest patch release within your selected branch. Do not deliberately start with an initial patch release merely because a tutorial used it. Later patches carry fixes while remaining within the same feature branch.
Treat compatibility as something to verify. A successful installation is necessary, but application tests determine whether your code works. If the newest branch cannot run a required dependency, choose a compatible supported branch rather than forcing an unsupported combination.

Step 3: Download the Right Python Build

Choose standard CPython unless the project requires something else. CPython is the standard implementation distributed through Python.org and the default assumption for most Python tutorials and packages. Alternative implementations and optional free-threaded builds require their own compatibility checks.
Match the download to your operating system and architecture. Choose the installer intended for your machine, such as an ARM64 build for an ARM-based system or an x86-64 build for an x86-64 system. Avoid mixing architectures unless your project deliberately requires an emulated environment. Python 3.14.8 is a maintenance release with binary installers, as confirmed in Python Insider. When downloading this branch, use that release or a newer patch if one has become available.
Use the appropriate installation route. On Windows and macOS, use the official installer or a maintained Python distribution your project already uses. On Linux, use the distribution’s packages or install a separate project interpreter; do not replace the operating system’s Python executable. If an older branch offers only a source release, obtain an updated build from a maintained distributor or build the current source. An older official installer is not equivalent to the latest security release.

Step 4: Create a Clean Project Environment

A virtual environment keeps a project’s installed packages separate from other projects. It uses the interpreter that creates it.

Open a terminal in the project directory and create the environment. These examples use the default recommendation. If you selected another branch, invoke that branch’s interpreter instead. On Windows, open Command Prompt with the Python launcher available:
```
py -3.14 -m venv .venv
```
On macOS or Linux, with that interpreter installed and available on your executable search path:
```
python3.14 -m venv .venv
```
If the interpreter is not found, resolve its installation or executable path before continuing. Do not silently create the environment with a different version.
Activate the environment. In Windows Command Prompt:
```
.venv\Scripts\activate.bat
```
In Bash or Zsh on macOS or Linux:
```
source .venv/bin/activate
```
The Windows activation command above is for Command Prompt, not PowerShell.
Install the project’s dependencies into that environment. If the project supplies a requirements.txt file, run:
```
python -m pip install -r requirements.txt
```
Using pip through the active interpreter keeps the package installation tied to that interpreter. If the project uses another dependency manager, use its declared installation workflow with the selected runtime. An empty learning project needs no additional packages just to run Python.

Step 5: Verify the Environment Before Adopting It

Confirm the version and executable location.
```
python -c "import sys; print(sys.version); print(sys.executable)"
```
The version should match your chosen branch, and the executable should be inside the project’s .venv directory.
Check installed dependency requirements.
```
python -m pip check
```
Resolve any reported missing or incompatible dependencies before treating the environment as ready. Declared package requirements do not prove that the application behaves correctly.
Run the application’s tests and a representative task. If the project uses pytest and includes it among its development dependencies, run:
```
python -m pytest
```
Also launch the application or execute its normal entry point. For data work, run a representative calculation or notebook; for a web application, start it and exercise an important request. For a new, empty learning project, successfully running the interpreter is enough to begin. For an existing application, adopt the environment only after its dependency checks and relevant tests pass.

When to Use Older Python Releases

An older supported branch can be the right choice when an application, dependency, or hosting platform requires it. Age alone, however, does not make a release more reliable for your workload.

Python 3.11 vs. Python 3.12

If these are your only compatible choices, prefer Python 3.12 unless the project specifically needs the older branch. Its security support continues until October 2028, while Python 3.11 reaches its scheduled end of life in October 2027.

Both are stable, security-only branches. The newer option gives you more remaining upstream support, but neither provides the ordinary bugfix maintenance available on a current bugfix-supported branch.

Downloading them also requires care:

Python 3.12.15 is a source-only security release, without new official Windows or macOS installers.
Python 3.11.17 is also source-only. Python 3.11.9 was the last release in that series with official binary installers.

Do not choose the last available installer simply because it is easier to download. It lacks fixes delivered in later releases. A distributor-provided build needs its own documented update policy.

Python 3.13

Python 3.13 can remain a reasonable compatibility choice, but it has entered security-only maintenance.

Python 3.13.16 is the last full maintenance release in that series and includes binary installers. Subsequent releases contain security fixes only. Choose this branch when it fits a dependency requirement and you have a workable plan for obtaining future security updates.

Python 3.10 and Earlier

Python 3.10 reached end of life on October 1, 2026. Do not start a new project on it when ongoing upstream security maintenance is required.

Python 3.10.22 is its final release, is source-only, and will receive no further security updates. Python 3.10.11 was the last release in the series with official binary installers.

Installing the final patch does not restore support. For an existing application, the useful endpoint is a tested migration to a supported branch, not merely reaching the last available patch of an unsupported one.

Choosing Python for Machine Learning and Deployment

Machine learning projects, deployed applications, and reusable libraries require Python versions verified against their packages, platforms, and supported runtimes.

Machine Learning and GPU Packages

For machine learning, the framework’s complete installation requirements matter more than a generic “best Python version” recommendation.

Check the combination of:

Framework version, such as PyTorch or TensorFlow.
Numerical libraries and compiled extensions.
Operating system and processor architecture.
Accelerator packages, GPU runtime, and driver requirements where applicable.

A package’s Python metadata may allow a version even when no matching prebuilt wheel exists for your platform. That can turn a straightforward installation into a source-build task requiring compilers and native libraries.

An installation failure therefore does not necessarily mean Python itself is broken. A message about no matching distribution can indicate an unavailable package build; compiler errors can indicate that installation has fallen back to building from source.

For managed notebooks, the provided environment may determine the available runtime. Choose compatible packages for that environment or move the project to a runtime you control.

Web Applications, Containers, and Hosted Runtimes

For a deployed application, the production runtime is a compatibility requirement, not a detail to decide after development.

A hosting platform or serverless service may support fewer Python branches than you can install locally. Pick a branch supported by the deployment target, then use it for development and continuous integration.

A container carries its own Python interpreter, independently of the host’s Python installation. However, packages with native components can still depend on the container’s operating system libraries and architecture. A successful local installation on one platform is not a substitute for testing the deployment image.

Do not copy a local virtual environment into a different operating system or container and assume it will work. Install the dependencies within the target environment.

Reusable Python Libraries

A library author has a different problem from an application developer: the library may need to support several Python branches.

Declare the supported range in package metadata, and test the lowest supported interpreter as well as newer supported interpreters. Testing only on your preferred local version can miss accidental use of syntax or standard-library features unavailable to your users.

An application can standardize on one deployment runtime. A reusable library needs evidence for the compatibility range it advertises.

How to Keep Python Environments Consistent

A correct installation is not enough if the editor, notebook, and deployment system use different interpreters.

Before settling on the runtime, confirm that:

Your editor selects the project environment. Its run and debug actions should not silently use a machine-wide installation.
Your notebook selects the intended kernel. A notebook kernel can use a different interpreter from the terminal that launched the notebook server.
Continuous integration declares its runtime. Do not rely on whatever Python happens to be preinstalled on a runner.
Deployment declares its runtime independently. Dependency files usually do not, by themselves, select the interpreter used by a hosting service or container.

Record the intended Python branch in the project’s setup instructions and runtime configuration. Record the exact patch where reproducibility requires it, but keep a process for adopting security updates rather than freezing that patch indefinitely.

Treat patch updates and minor-version upgrades differently: keep up with patches within the chosen branch, while testing a move to another branch in a separate environment before switching the application.

Once the checks pass, make the project environment your editor’s default and commit the configuration needed to reproduce it.

Frequently Asked Questions

Why Would a Project Still Use Python 3.7?

A project may remain on Python 3.7 because of an old dependency, an embedded runtime, or an unfinished migration, not because it is a good current default. Upstream support ended on June 27, 2023. Reproducing legacy behavior can require that version temporarily, but a maintained application needs an upgrade plan.

Can I Install Multiple Python Versions on One Computer?

Yes. Multiple interpreters can coexist, with each project using a virtual environment created by its selected interpreter. Installing another Python version does not automatically change an existing virtual environment; create a new environment and reinstall the project’s dependencies when changing branches.

Does a Newer Python Version Always Run Faster?

No. Performance depends on the application, its dependencies, and which parts of the workload execute in Python rather than native extensions or external services. Benchmark a representative task before choosing a runtime for speed, and keep compatibility and support status as requirements.

How do you select a particular Python version to run?

If the py.exe launcher is available, py -3.7 chooses Python 3.7. Microsoft Store installations may not include the py command. With a Microsoft Store installation, use python3 or python3.7 instead.