Clusy vs OpenAI deep research
Both are agents that work autonomously for minutes at a time and hand back something substantial. What they do in those minutes could not be more different: one reads the internet, the other runs your code.
At a glance
| Clusy | OpenAI deep research | |
|---|---|---|
| What the agent does | Researches with citations, then writes and executes code on your data and reports measured results | Browses, reads, and cross-checks sources, then writes a cited report |
| Breadth of reading | Focused: arXiv, OpenAlex, uploaded PDFs, and the web, in service of the work | Very broad: hundreds of sources across any subject |
| Runs your code | Yes, on managed compute | No |
| Source of truth | Published sources for the reading, then your data, measured now | Published and web sources, as reported |
| Output | A re-runnable notebook with cells, charts, metrics, and artifacts | A cited report in the conversation |
| Compute | free 8 vCPU / 8 GB RAM CPU sandbox up to H100 / H200 GPUs (141 GB VRAM) | None you control |
| Typical runtime | Minutes to hours, depending on the training run | Roughly 5 to 30 minutes per report |
The core difference
Deep research is an agent pointed at the world's writing. It decomposes a question, browses widely, reads across sources, cross-checks, and produces a structured report with citations. For a literature scan, a market landscape, or getting current on a subfield, it does in half an hour what used to take a day.
Its ceiling is that everything it returns already existed. It can tell you that three papers report a method beating a baseline; it cannot tell you whether that holds on your data, because that experiment has not been run by anyone.
Clusy's agent does the same kind of work on the way in, searching arXiv and OpenAlex, expanding the citation graph, screening sources and citing them, and it can clone a paper's reference implementation and read the source rather than the description. Then it keeps going: it writes and runs the cells on managed compute, reads the actual metrics rather than the reported ones, and iterates, with branching so several configurations can be compared side by side. The output is an executed notebook, a different kind of object from a report, and one you can inspect, correct, and run again next month. On breadth of reading, deep research wins and it is not close.
Choose Clusy if…
- The number you need does not exist yet and has to be measured.
- The work involves training, fine-tuning, or evaluation on real hardware.
- You need an artifact somebody can re-run and audit.
- The data is yours and is not on the public web.
Choose OpenAI deep research if…
- You are scoping a field or a market and need it summarized with sources.
- The answer is published somewhere and you need it found and cross-checked.
- You want a written report rather than an executed notebook.
- No code needs to run for the output to be useful.
Frequently asked questions
- Is Clusy a deep research tool?
- It does agentic research with citations, over scholarly sources as well as the web, and it does not stop at the write-up: the same run can implement and measure what it found. But it reads a narrower slice than deep research does, and for a broad survey of an unfamiliar field across hundreds of sources, deep research is the better instrument.
- Can deep research analyze my dataset?
- It can reason about data described in documents and produce analysis in prose. It does not execute code on your files or provision a GPU, so measured results on your own data are out of scope.
- What does the combined workflow look like?
- Deep research to establish what has been tried and what was reported, then Clusy to implement the promising approach and measure it on your data. The report tells you where to aim; the notebook tells you whether it worked.
Sources
Claims about OpenAI deep research come from its own documentation, last checked : Introducing deep research, Deep research 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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