Comparison · reviewed 2026-09-02
marimo vs Jupyter: reactive .py notebooks or the classic stack?
Should your next Python notebook be a marimo notebook or Jupyter? This page compares on the evidence recorded in FlyPython's Project Radar review — no paid placements, no winner-crowning, review dates shown.
Side by side
marimo vs Jupyter
Assessment date: 2026-09-02. Radar-reviewed sides additionally carry their last review date and evidence below.
marimo
Strengths. Reactive, deterministic execution; notebooks are real .py files — diffable, testable, and reproducible.
Trade-offs. Requires a modern browser workflow; replaces Jupyter rather than extending it; low AI familiarity — agents often write stale APIs.
Jupyter
Strengths. The default everywhere: nbconvert, papermill, VS Code and PyCharm support, and a decade of tutorials and team muscle memory.
Trade-offs. Hidden cell state makes runs non-reproducible by default; JSON notebooks diff poorly; output-before-truth is easy to miss.
Verdict
What we would do
marimo stores notebooks as plain executable .py files with reactive, deterministic execution — versionable, testable, and git-friendly. Jupyter keeps the deepest ecosystem: every extension, training material, and teammate habit already targets it. Choose marimo for reproducible data tools you intend to ship; choose Jupyter when ecosystem plugins or kernel-state debugging are the job. Note the AI gap: models know Jupyter cold, while marimo's reactive APIs postdate common training cutoffs — verify agent-written marimo code.
Honesty note: this comparison restates dated, human-reviewed evidence; it is not a benchmark, and neither side sponsored it. When the underlying radar entry is re-reviewed, this page is regenerated in the same change.