Repo Compare › Deep learning

PyTorch vs TensorFlow vs JAX

A side-by-side look at how actively PyTorch, TensorFlow and JAX are developed on GitHub — popularity, contributor base, commit activity and how quickly issues and pull requests move.

What the numbers say

• TensorFlow is the most-starred of the 3, with 201k stars — about 5.5× JAX's 36k.
• Over the 52 weeks to the snapshot, PyTorch recorded the most commits (18k), against 7.8k for JAX.
• PyTorch has closed or merged the most pull requests in its lifetime: 134k.
• Relative to its popularity, TensorFlow carries the lightest open-issue load: 3.2 open issues per 1,000 stars, versus 135.8 for PyTorch.

Head-to-head

Snapshot taken 30 September 2026. Refresh with live data

The wide bar is that repository’s share of the largest value in its own row — half the length means half the number. The thin gauge beneath it is the same row rescaled from its smallest value to its largest, which makes a close gap such as 61k against 59k visible; on that gauge the row’s lowest value always sits at zero however big it is, so read it as a spread and never as a quantity. Bars compare repositories with each other and never one metric against another. Colour identifies the repository, matching the dot beside its name. On the two issue rows a longer bar means more open work, which is usually the opposite of better. The highlighted cell is the highest value in a row. “Active / Slowing / Dormant” is a simple heuristic from how recently code was pushed and open issues per star — a starting point, not a verdict. Stars measure popularity, not quality.

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