When doing in-depth research, don't mix these four types of tools. ChatGPT Deep Research is more like a controllable research agent, Gemini Deep Research is more suitable for Google ecosystem users, Perplexity is strong in quick evidence collection and source tracking, and NotebookLM is not a network-wide research tool, but your own data organizer. Many people choose the wrong thing, not because the tools are not good, but because "searching for external information" and "digesting their own information" are mixed together.
| tools | Who is it better for? | Not for anyone |
|---|---|---|
| ChatGPT Deep Research | People who want to make controlled, step-by-step, exportable research reports | People who just want to search for a quick answer in minutes |
| Gemini Deep Research | Heavy Google users | People who are not in the Google workflow |
| Perplexity | Quickly collect evidence, compare sources, and query users frequently on a daily basis | People who want to organize complex research processes for a long time |
| NotebookLM | People who already have a bunch of PDFs, Docs, notes to digest | People who mainly rely on public web pages for real-time research |
How to divide the scene the fastest
- What you want is to "go online to find information and organize it into a report", preferably look at ChatGPT Deep Research or Gemini Deep Research.
- What you want is "find out the source first and quickly judge credibility", Perplexity tends to be more frequent.
- You already have a bunch of meeting minutes, PDFs, course materials, and internal documents, and if you want to understand them thoroughly, NotebookLM is more suitable.
If you often do industry analysis, solution comparison, and market intelligence, ChatGPT Deep Research and Gemini Deep Research are more like formal research tools; If you're doing a quick fact-finding every day, Perplexity saves time; If your core problem is "too much information to read", NotebookLM is more valuable. First distinguish whether you are "looking for information" or "eating information", and the recommendation results will not be messy.
