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uv is usually several times faster than pip for Python dependency installs, and Astral’s published figures range from 8 to 10 times faster on a cold cache to 80 to 115 times faster on a warm cache. Those multipliers come from Astral, the company that builds uv, and they depend heavily on what is being measured. The sections below explain which number applies to which situation, why the default settings are not a perfectly even contest, and how to measure the gap on your own dependencies.
The published speed figures
Astral has published three headline numbers, and they describe different scenarios. Quote them with their scope attached.
| Claim | Scenario | Source and date | Limits stated by the source |
|---|---|---|---|
| 8 to 10 times faster than pip and pip-tools | Without caching | Astral’s original uv announcement, 2024-02-15 | Vendor-reported, measured in the scenarios Astral tested |
| 80 to 115 times faster than pip and pip-tools | Warm cache, recreating a virtual environment or updating a dependency | Same announcement, 2024-02-15 | Vendor-reported; applies only to the warm-cache case |
| 10 to 100 times faster than pip | Broad positioning, no workload specified | Astral’s uv documentation overview, page dated 2026-03-13 | A general statement, not a benchmark specification |
| Syncing 43 locked packages: resolve 11 ms, install 208 ms | Example command output, uv only | Astral’s uv documentation | Illustrative output. It contains no pip comparison and is not a promise of runtime |
The honest summary is that uv’s advantage is large in Astral’s tests, and it is largest when packages are already in the cache. Anyone who quotes “100 times faster” without naming the scenario is repeating the broad marketing range, not a measured result for a clean first install.
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Astral’s benchmark documentation, dated 2024-08-20, says the project benchmarks uv against earlier releases and against tools such as pip and Poetry. It points readers to the project’s GitHub repository for current results and methodology. Those pages are the place to check whether a newer figure supersedes the 2024 numbers. At the time of writing, this article did not find an independent reproduction with a fully documented setup that could be cited alongside Astral’s figures.
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Why cold and warm installs differ
The gap between the 8 to 10 times figure and the 80 to 115 times figure is mostly a caching effect. Astral says uv keeps a global cache so that it does not re-download or rebuild dependencies it has already processed. On filesystems that support them, uv also uses copy-on-write and hardlinks to place cached files into an environment rather than copying them byte by byte.
That means the speed you see depends on the state of your machine:
- Clean cache, first install: network download and build work dominate. This is the case closest to Astral’s 8 to 10 times figure.
- Warm cache, recreated environment: most files are already available locally, so the work left is largely linking and resolution. This is where the very large multipliers come from.
- Warm cache, one dependency updated: only the changed package needs new work, which is the second scenario Astral describes.
pip also caches downloaded wheels, so a warm-cache comparison is only fair if pip’s cache is warm too. Measure both tools in the same state.
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Default behavior that changes the timing
The two tools are not identical by default, and one difference affects install time directly. Astral’s compatibility documentation states:
“Unlike pip, uv does not compile .py files to .pyc files during installation by default (i.e., uv does not create or populate __pycache__ directories).” (Astral, “Compatibility with pip” documentation, live page accessed 2026-10-05.)
In practice this means pip does extra work at install time that uv skips unless you ask for it:
- pip: compiles bytecode during install by default. Use
--no-compileto turn this off. - uv: skips bytecode compilation by default. Add
--compile-bytecodeto enable it.
Enabling compilation in uv can lengthen the install, but it can improve startup time for later runs in some workflows. A timing comparison is only fair when both tools perform the same bytecode step, so align the flags before measuring.
Compatibility differences that affect the comparison
uv is designed as a drop-in replacement for common pip and pip-tools workflows. It is not an exact clone, and several differences can change both speed and results.
- Environment targeting:
uv pip installanduv pip synctarget an active or discovered virtual environment by default. pip installs into the global interpreter when no virtual environment is active. A timing run that accidentally installs into different environments is not a valid comparison. - Package index selection: the tools handle index configuration differently, so confirm that both point at the same index before testing, especially if you use a private index.
- Resolver priorities: the two resolvers can choose different versions in some cases. If the installed package set differs, the timings are not measuring the same work.
Check required flags and reproducibility expectations before switching a production workflow, not only the speed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to measure the difference on your own dependencies
The only reliable answer for your project is a controlled test on your own requirements. The steps below assume a Linux or macOS shell, with commands timed using time.
- Choose the operation to measure: a fresh install into an empty environment, a sync of a locked requirements file, or an update to an existing environment. Do not compare different operations.
- Fix the inputs: the same Python version, operating system, filesystem, package index, network connection, and requirements file for both tools. Note the package versions that resolve.
- Set the bytecode behavior to match. For example, run
pip install --no-compile -r requirements.txtanduv pip install -r requirements.txt(without--compile-bytecode), or enable compilation on both sides. - Run the clean-cache case first. Clear pip’s cache with
pip cache purgeand uv’s withuv cache clean. Activate a fresh virtual environment, then time the install withtime pip install -r requirements.txt. - Repeat the timing with a fresh environment but a warm cache, and record it separately. Do not average clean and warm results together.
- Run each case several times and report the median, along with the Python version, operating system, and package count.
If the results look wrong
- uv appears to have installed into the wrong place: confirm that a virtual environment is active before running
uv pip install, or the install may have targeted a different environment than pip did. - Package sets differ between runs: compare the installed lists with
pip freezeand the uv equivalent, then fix the resolver inputs before comparing times. - The gap is far smaller than published: check whether the cache was actually cleared, whether the network or index is the bottleneck, and whether bytecode compilation is aligned.
What to take from the numbers
For a clean first install, expect a multiple closer to Astral’s 8 to 10 times figure than to the warm-cache range, and verify it on your own dependency set. For repeated environment rebuilds on a machine with a warm cache, the advantage can be much larger, as Astral’s 80 to 115 times figure suggests. Either way, the number you can defend is the one you measured under stated conditions.
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