Jupyter Notebook vs marimo
Pick marimo if hidden state and unreviewable notebook diffs are your real problem: it stores notebooks as plain Python and re-runs dependent cells automatically, so the file you commit is the file that runs. Pick Jupyter if you need its ecosystem: non-Python kernels, specific widgets and extensions, or the format every tutorial and reviewer expects. The two read almost identically; the difference is what happens when a cell changes.
At a glance
| Jupyter Notebook | marimo | |
|---|---|---|
| What it is | The open-source notebook standard: a browser interface over a local Python (or R, Julia) kernel | An open-source reactive Python notebook: change a cell and everything downstream re-runs, so there is no stale hidden state |
| AI | None built in; assistance comes from extensions such as Jupyter AI, which you install and key yourself | An in-editor assistant, plus `marimo pair` to attach an external coding agent such as Claude Code or Codex to a running notebook |
| Compute | Whatever you run it on: your laptop, a lab box, or a server you provision | Local by default. molab, its hosted workspace, is in public preview on CoreWeave with roughly 4 CPUs and 32 GB RAM, opt-in GPUs, and 12-hour sessions, free while capacity is not oversubscribed (checked August 2026) |
| File format | .ipynb JSON with embedded outputs, which is hard to diff, and cells can run out of order | Pure .py files that diff, import, and test with pytest; `marimo convert` reads .ipynb and `marimo export ipynb` writes it |
| Collaboration | None natively; share files, or run JupyterHub for your team | Share as an app, as slides, or as a public molab link; no live multiplayer editing |
| Cost | Free | Free; molab free during preview |
| License | Open source (BSD-3-Clause), governed by Project Jupyter | Open source (Apache-2.0) |
The core difference
Jupyter is a scratchpad that the field promoted to a research artifact. Cells execute in whatever order you click them, so the state in your kernel is a history that the file does not record. That is why so many published notebooks fail to run top to bottom for anyone else: a failure mode marimo cites at length, and one anybody who has inherited a colleague's notebook recognizes.
marimo attacks that at the format level. It parses the dependency graph between cells, so changing one cell invalidates and re-runs everything downstream, and it stores the notebook as ordinary Python: meaning `git diff` is readable, `pytest` can import it, and `python notebook.py` runs it as a script. Interactive widgets are built in rather than bolted on, and a notebook can be served as an app.
What you give up is gravity. Jupyter has non-Python kernels, a decade of extensions and widgets, and universal familiarity: it is what your reviewers, your students, and every tutorial already use. marimo is Python-only, and while adoption is climbing fast (roughly 22k GitHub stars as of mid-2026), some of your dependencies will still assume .ipynb.
Pick Jupyter Notebook if…
- You work in R, Julia, or another non-Python kernel.
- Your workflow depends on specific Jupyter extensions or ipywidgets.
- You are teaching, or handing work to people who only know Jupyter.
- You want the format with the broadest tool support, full stop.
Pick marimo if…
- Notebook diffs are unreviewable and it is hurting your team.
- You have been burned by a notebook that only ran in one person's kernel.
- You want interactive UI elements without wiring up a separate app framework.
- You want the same file to work as a notebook, a script, and a test target.
Where Clusy fits
That's usBoth are notebooks you drive by hand, and both leave compute to you. Clusy (our product, so weigh accordingly) is the other axis: an agent plans and runs the notebook for you on managed CPUs or GPUs up to H100/H200, and you review the cells it wrote. If your complaint about Jupyter is reproducibility, marimo is the honest answer; if it is that you are doing all the typing and all the ops, that is the gap we built for.
Try Clusy freeFrequently asked questions
- Can marimo open my existing .ipynb notebooks?
- Yes. marimo ships a converter that turns .ipynb files into marimo's Python format. Conversion is mechanical, but because marimo enforces a dependency graph, notebooks that relied on out-of-order execution or on redefining the same variable in several cells will need edits before they run cleanly.
- Is marimo actually faster than Jupyter?
- Not in raw execution: both run the same Python. It feels faster because you stop paying the re-run tax: you do not have to hunt for which cells to execute after a change, and you do not restart the kernel to prove the notebook still works. On a long-running cell, reactivity can cost you time instead, which is why marimo lets you mark expensive cells to run lazily.
- Does marimo work with AI coding agents?
- That is one of its arguments: because notebooks are plain Python, ordinary coding agents can read and edit them, and `marimo pair` attaches an external agent such as Claude Code to a running notebook. Jupyter's route is the jupyter-ai extension. In both cases you are configuring the agent and supplying your own model keys.
- Which is better for reproducible research?
- marimo, by construction: deterministic execution order plus a plain-text file that a reviewer can read in a diff. Jupyter can get there with discipline and tooling (papermill, nbdime, jupytext, a CI run that executes the notebook), but it is discipline rather than a guarantee.
Sources
Every claim about Jupyter Notebook and marimo on this page comes from their own documentation, last checked . Quotas, hardware tiers, and pricing move; check the vendor before relying on a number. Spotted something out of date? Tell us and we will fix it.