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In a September 2026 benchmark, DEV Community author Remdore reported that installing a roughly 63-package dependency set into a newly created environment took about 13.2 seconds with pip and 0.056 seconds with uv when the package cache was warm. The test ran in a clean Python 3.12 container, and the result is specific to that setup—not a general guarantee that uv installs dependencies in 56 milliseconds.
What the 13-second versus 56-millisecond result measures
Remdore’s test used a requirements file with 20 top-level packages resolving to around 63 packages. It included packages such as FastAPI, uvicorn, SQLAlchemy, Alembic, Pydantic, Celery, Redis, pandas, numpy, and pillow. The author tested pip and uv in a clean Python 3.12 container, installing into fresh virtual environments, and reports running each of four conditions three times. The reported figures are rounded results from that test, not independently reproduced measurements.
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| Cache condition | pip | uv | What was tested |
|---|---|---|---|
| Cold | About 26 seconds | About 5.4 seconds | Download cache cleared before installing into a fresh environment. |
| Warm | About 13.2 seconds | About 0.056 seconds | Package cache retained while the environment was rebuilt. |
All times are Remdore’s reports for this package set and Python 3.12 container. The author says selected packages’ resolved versions matched between the two environments. That is useful evidence about this test, but it does not establish identical behavior for every dependency set or workflow. The article does not provide raw per-run timings or a multi-machine comparison.
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“Cold means the download cache was wiped first, the state a CI runner is in without caching. Warm means the cache was kept but the environment rebuilt, the state your laptop is in all day,” Remdore writes. In other words, the headline compares recreating an environment with package artifacts already available against doing the same with pip—not downloading everything from scratch.
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Remdore attributes uv’s especially short warm run to its global package cache and its ability, when the filesystem allows, to link cached package files into the new environment rather than copying them. The author reports that uv took 0.32 seconds when forced into copy mode. Both the explanation and that timing come from the same benchmark, not an independent test.
Filesystem layout matters. Astral’s uv cache documentation says that when the cache and Python environment are on different filesystems, linking may not work and uv may need to copy files instead. The 56-millisecond result therefore should not be assumed for a machine or CI runner with a different cache location, mount arrangement, or environment setup.
What uv’s documentation confirms—and what it does not
Astral describes uv pip as a pip-compatible interface that works directly with virtual environments. It also says uv does not rely on or invoke pip, and cautions that the interface does not exactly implement every behavior of the tools it resembles. See the uv pip interface documentation.
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Check environment semantics before switching commands
Elapsed time is only a fair comparison if both tools are doing the job you actually need. In particular, uv pip install and uv pip sync do not have the same effect on packages already present in an environment.
uv pip installgenerally leaves already-installed packages in place unless they conflict with the requested requirements.uv pip syncremoves packages that are absent from the requirements or lock input, bringing the environment into sync with that input.
Astral explains this distinction in its locking guide. Choose the operation that matches your intended environment lifecycle before comparing speed; otherwise, the tools may not be performing equivalent work.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to reproduce the comparison on your machine
- Use one dependency specification. Run both tools against the same requirements file, using the same Python version and equivalent fresh virtual environments.
- Separate cold and warm runs. For a cold run, clear the relevant download cache before installation. For a warm run, retain the package cache but rebuild the environment. Record which condition each result represents.
- Record filesystem placement. Note whether the cache and environment are on the same filesystem. A separate filesystem can prevent linking and lead to copying.
- Repeat each condition. Run the same setup several times and retain individual timings as well as a summary. One machine’s results should not be treated as a universal estimate.
- Check what was installed. Compare resolved package versions and make sure both commands have the intended install or sync behavior.
This approach tells you whether the benchmark’s cache advantage applies to your dependency set and filesystem. Its outcome may differ from Remdore’s even when you use the same tools, because cache state, environment lifecycle, and file placement are part of the workload.
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