The term “Deep Research” has quickly gained traction among AI tools claiming to offer PhD-level literature reviews from a single prompt. The promise is clear: instead of sifting through dozens of papers yourself, you get a well-organized, detailed review in minutes.

But while the term shows up everywhere, its meaning is still fuzzy. Influential voices like Tyler Cowen and Ethan Mollick have highlighted its potential, but what actually counts as Deep Research? And how is it different from a Google search or a typical AI-generated summary?

Deep Research Definition: “A report generation system that takes a user query, uses large language models (LLMs) as agents to iteratively search and analyze information, and produces a detailed report as output” [1]

This marks a departure from traditional web search engines or standard AI summaries. While a Google search returns ranked links in milliseconds and most AI assistants summarize top results in seconds, Deep Research systems may take several minutes or more to generate long-form, structured reports.

These tools are designed to replicate, and partially automate, the key stages of a literature review: retrieving, analyzing, synthesizing, and presenting relevant research.

As Aaron Tay [2] outlines, Deep Research tools are typically defined by two traits:

• Agentic Search Methods: These tools don’t just pull up the top links. They run through loops, refine their queries, and revise their outputs – almost like a junior research assistant would.

• Long-Form Output: Instead of a quick paragraph or bullet list, the goal is a proper report. These are often several pages long and aim to mimic the depth of a human-written literature review.

A Crowded and Confusing Landscape

Not all Deep Research tools are built the same. Some, like OpenAI’s Deep Research or Stanford’s STORM, rely on multi-agent systems with extensive training. These tools can reason through complex queries, revise their approach, and adapt as they go. Others, like Elicit’s “Research Reports,” take a more structured, rule-based approach. They offer more transparency and control, but often at the cost of flexibility.

Graph from Han Lee (2025). "The Differences between Deep Research, Deep Research, and Deep Research."

To help make sense of it all, Han Lee offers a useful framework: a simple quadrant map based on two key traits – how deep the tool searches and how trained the underlying system is.

This framework helps distinguish between tools like GPT-Researcher (handcrafted and shallow) and OpenAI Deep Research (trained and deep), offering clarity in an otherwise crowded and often overhyped landscape.

How It Differs from Traditional Literature Reviews

At their core, Deep Research tools mimic many steps of a traditional literature review. What used to take hours of database searches, reading, note-taking, and synthesis can now be partly automated.

These tools don't replace the researcher. But they reshape the front end of the process, helping with initial exploration, summarizing emerging themes, and surfacing sources you might have missed.

In short, Deep Research is:

• Not a “literature review” silver bullet

• Not a substitute for judgment or subject expertise

• But a fast-moving category that's changing how early-stage research gets done

We hope that this help explain “Deep Research”, but there is no denying the term is being used liberally – and inconsistently – across platforms. From OpenAI to Gemini to Perplexity, everyone seems to be pointing at each other and claiming the same label.

And yes, we think that’s a bit confusing too.

Conclusion

In summary, while “Deep Research” is an exciting and rapidly evolving category, it's also one that’s still finding its shape. As new tools continue to emerge, understanding what truly sets them apart—from agentic workflows to the depth of their outputs—will be essential for anyone looking to use AI meaningfully in research. Clarity around this term isn’t just a semantic issue; it’s a necessary step in building trust, setting expectations, and making the most of these new capabilities.

Read the Full Report

This post is part of our newly published report benchmarking today’s top AI research tools. If you're interested in diving deeper, exploring side-by-side comparisons, and seeing which tools actually deliver on the Deep Research promise—click here to access the full report for free.

References

1. Han Lee (2025). "The Differences between Deep Research, Deep Research, and Deep Research."

2. Aaron Tay (2025). "The Rise of Agent-Based Deep Research: Exploring OpenAI’s Deep Research, Gemini Deep Research, Perplexity Deep Research, Ai2 ScholarQA, STORM, and More in 2025."