Clusy vs NotebookLM
These two get compared because they share a word. NotebookLM is a reading tool: it grounds answers in documents you give it. Clusy is an execution tool: it writes and runs code against data. Almost nobody needs both for the same task.
At a glance
| Clusy | NotebookLM | |
|---|---|---|
| What it does | Researches the question, then writes and runs code against your data and reports what it found | Answers questions from documents you upload, grounded in the source text |
| Runs code | Yes: that is the product | No |
| Input | Datasets from uploads, Hugging Face, Databricks and Snowflake, plus papers and PDFs the agent finds or you supply | Documents: PDFs, docs, slides, links, capped by plan |
| Output | A real notebook with executed cells, charts, metrics, and artifacts | Cited answers, summaries, and Studio formats such as Audio Overviews |
| Compute | free 8 vCPU / 8 GB RAM CPU sandbox up to H100 / H200 GPUs (141 GB VRAM) | None you control |
| Best question | What does this data say, and can you model it? | What do these documents say? |
The core difference
NotebookLM is a source-grounded assistant. You upload documents, and Gemini answers only from them, with citations pointing back to the passage it used, plus Studio outputs like Audio Overviews, mind maps and slide decks. Its whole value is that it will not stray beyond what you gave it, which is exactly what you want when the risk is invention.
Clusy is a computational notebook with a research step in front of it. The agent can search arXiv and OpenAlex, read papers and uploaded PDFs, and cite them, but that is the setup rather than the deliverable. Then it writes Python, executes it on a managed sandbox, reads the actual output, and iterates until there is a result: a chart, a metric, a trained model.
So the word notebook means different things here. NotebookLM's notebook is a curated folder of sources you interrogate. Clusy's notebook is a program that ran. If nothing needs to execute for your answer to be useful, NotebookLM is the better tool and the comparison ends there.
Choose Clusy if…
- The answer requires running code, not only reading prose.
- You need a trained model, an evaluation, or a chart from real numbers.
- You want the reading and the experiment done by one agent.
- The work has to be reproducible and re-runnable later.
Choose NotebookLM if…
- You have a corpus of documents and questions about it.
- You want answers that cannot drift beyond your sources.
- You want a summary, a briefing, or an audio walkthrough.
- No code needs to run for the answer to be useful.
Frequently asked questions
- Can Clusy read my PDFs and summarize them?
- It can parse uploaded PDFs and cite them, and it searches arXiv and OpenAlex for papers on its own. What it is not built for is a standing library of documents you keep asking questions of, with the source caps, grounding guarantees and Studio outputs NotebookLM is designed around. If reading a corpus is the whole job, NotebookLM does it better.
- Can NotebookLM analyze a dataset?
- It can describe a document about a dataset. It cannot execute code, so it cannot compute a statistic you did not already write down, fit a model, or produce a chart from raw numbers.
- Do people use both?
- Some researchers do, at different stages: NotebookLM to get across the reading, then a computational notebook to run the experiment. They do not overlap, so there is nothing to reconcile.
Sources
Claims about NotebookLM come from its own documentation, last checked : NotebookLM, NotebookLM help. Quotas, hardware tiers, and pricing move; check the vendor before relying on a number, and tell us if we have something wrong.
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