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In one benchmark, developer Remdore reported that installing a roughly 63-package dependency set into a fresh environment took about 13.2 seconds with pip and 0.056 seconds with uv when the package cache was warm. The test used a clean Python 3.12 container and is one author’s result—not a general guarantee that uv will install dependencies in 56 milliseconds.
What the 13-second versus 56-millisecond result measured
Remdore’s September 13, 2026, DEV Community article compared pip and uv against a requirements file with 20 top-level packages resolving to around 63 packages. The list included FastAPI, uvicorn, SQLAlchemy, Alembic, Pydantic, Celery, Redis, pandas, numpy, and pillow. The author tested cold and warm cache conditions, installed into fresh virtual environments, and ran each of the four conditions three times. Selected packages’ resolved versions matched across the two environments, according to the author. Read the benchmark article.
| Cache condition | pip | uv | What the test did |
|---|---|---|---|
| Cold | About 26 seconds | About 5.4 seconds | The author cleared the download cache before installing into a fresh environment. |
| Warm | About 13.2 seconds | About 0.056 seconds | The author retained the package cache but rebuilt the environment. |
These are the author’s reported timings for that package set and container, not independently reproduced figures or results shown to be typical across machines. The report does not provide raw per-run timings.
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“Warm” here does not mean reusing an already-populated virtual environment. The author kept downloaded package artifacts available but created a fresh environment. In the author’s words, “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.”
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That distinction matters: a warm-cache result measures what happens when package data is already on hand, while a cold-cache result also includes obtaining packages. Neither timing alone describes every install workflow. For example, reusing an existing environment is a different lifecycle from rebuilding one, so it should be measured separately if that is how your project works.
How caching and filesystem layout can affect uv’s result
Remdore attributes uv’s fast warm result to its global package cache and its ability, by default, to link cached files into a new environment when the filesystem allows. Linking can avoid the file-copying work that would otherwise be needed. In the same article, the author reports that forcing uv into copy mode took 0.32 seconds. That is another result from this test, not a general estimate for copy mode or a verified CI-runner benchmark.
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Astral’s uv cache documentation confirms that cache behavior depends on dependency type and warns that placing the cache on a different filesystem from the Python environment can prevent linking and lead to slower copying. A cache restored onto one mount and an environment created on another may therefore behave differently from a setup where both are on a compatible filesystem. The documentation explains this mechanism; it does not independently validate Remdore’s timings.
What uv’s pip-compatible interface does—and does not—mean
Astral describes uv pip as a pip-compatible interface that operates directly on virtual environments. The documentation also says uv does not rely on or invoke pip, and cautions that the interface does not reproduce every behavior of the tools it resembles. See the uv pip interface documentation.
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In particular, check whether your workflow needs install or sync. According to the uv locking guide, uv pip install generally leaves already-installed packages in place unless they conflict. uv pip sync removes installed packages that are absent from the requirements or lock input. Choosing the faster command is not useful if it changes the environment behavior your project expects.
How to compare pip and uv on your own setup
Use a comparison that matches the work you actually perform. Keep the inputs and environment lifecycle constant, and record the cache and filesystem conditions rather than treating “warm” as a universal state.
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- Use the same dependency specification and Python version. Start from the same requirements file and interpreter for both tools. If exact dependency resolution matters, compare the resolved package versions, not just the top-level requirements.
- Match the environment lifecycle. For a fresh-environment test, rebuild the environment for every run. If your normal workflow updates an existing environment, benchmark that separately rather than comparing it to a fresh install.
- Define cache states explicitly. For a cold run, clear the relevant download cache before installing; for a warm run, retain it while rebuilding the environment. Record what you did so the measurements can be interpreted.
- Record filesystem placement. Note whether the package cache and target environment are on the same filesystem. If they are on separate filesystems, linking may not be possible and copying may change the result.
- Repeat the runs and report the conditions. Run each case multiple times, keep the individual timings, and identify the machine or runner, Python version, package set, cache state, and environment lifecycle. A single result should not be presented as a general expectation.
- Check workflow semantics as well as speed. Confirm whether your use case calls for
installorsync, and verify that the resulting environment contains the packages and versions you expect.
For cache maintenance, uv documents commands for clearing or refreshing cached data in its cache guide. Use those documented operations when setting up controlled comparisons rather than assuming a cache is empty or equivalent across tools.
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