What Is Deep Research AI?

Deep Research AI Goes Beyond Simple Chatbots to Perform Multi-Step Investigations

You might be familiar with large language model (LLM) assistants—chatbots that produce text or answer questions based on pre-trained knowledge. Deep Research AI goes a step further. These tools don't just respond; they plan and perform multi-step investigations into a topic, often by browsing the web or consulting large databases. They assemble the findings into structured reports with references, simulating the work of a diligent research analyst.

In other words, traditional LLM-powered assistants tend to rely on one-shot Q&A—answering based on existing training. Deep Research AIs, on the other hand, can:

Formulate sub-questions and hypotheses (like an outline).
Search for relevant information online (or in specified data sources).
Evaluate the credibility of sources.
Synthesize results into cohesive documents with citations.

This approach aims to reduce the user's workload by handling the grunt work of collecting, reading, and summarizing data from across the internet. While that vision has been around for a while, the past few months have seen a leap in practical, polished "deep research" offerings. Below, we explore three major contenders: Google's Gemini Deep Research, OpenAI's ChatGPT Pro Deep Research, and Stanford's STORM.

1. Google Gemini Deep Research Offers Speed and Accuracy in Web-Based Research

Multi-Step Research Process Delivers Actionable Reports in Minutes

When Google released the Deep Research feature for its Gemini model in December 2024, it grabbed headlines. Gemini was already regarded as a powerful multimodal LLM, but Deep Research turned it into an agentic tool that systematically planned searches, browsed the web, and compiled findings into well-referenced reports. Its main workflow:

Research Plan: When given a complex query—say, "Compare cutting-edge carbon capture technologies and their current investment trends"—Gemini proposes a multi-step plan that you can revise or approve.
Autonomous Web Browsing: It then executes the plan, iteratively searching for sources, reading them, and refining its queries to fill knowledge gaps.
Structured Output: Within minutes, the AI compiles a coherent, cited report featuring bullet points, headings, and links to the articles or studies it relied on.

All of this happens under your supervision. By default, you can see how many sources it has read and optionally pause to guide it if the search direction feels off. The final product aims to be a comprehensive overview with verifiable backing from the original URLs.

Advanced Web Search Loops Eliminate Most AI Hallucinations

Similarity: Like older AI chatbots, Gemini uses a large language model to understand your prompt and craft fluent text.
Difference: Gemini's Deep Research actively uses the web in multiple loops. It does not just rely on a single retrieval or pre-trained knowledge. This multi-step approach cuts down on inaccuracies and "hallucinations" because the model regularly cross-checks new information.

Another point: Gemini's Deep Research shares a name with OpenAI's recently announced "Deep Research" feature for ChatGPT Pro. Both revolve around agentic web search, though each has proprietary technology under the hood—leading many industry watchers to say the naming can be confusing. More on that soon.

Latest Tests Show Major Improvements in Speed and Accuracy

Gemini Deep Research has been praised for its ability to handle large-scale tasks quickly. In one test, it scanned and summarized over 80 relevant news articles and whitepapers on solar panel supply chains in under four minutes—an effort that might take a human hours.

Premium Features Drive Performance but Cost Limits Access

Strengths:
- Extremely fast at scanning a wide breadth of data.
- Clear citation structure for each claim, allowing verification.
- Integrates smoothly with Google Docs and other Google products.
Weaknesses:
- Only available to "Gemini Advanced" subscribers in English for now.
- The output can be somewhat shallow if your query requires deep domain insight.
- While hallucinations are lower, they still happen on less-documented topics.

2. ChatGPT Pro Deep Research Prioritizes Thoroughness Over Speed at a Premium Price Point

Research Process Combines Depth with Systematic Source Coverage

OpenAI launched ChatGPT Pro in December 2024 with a price tag of $200/month. This tier gave users enhanced models with near-unlimited usage. Last week, OpenAI took it further by releasing a Deep Research feature for ChatGPT Pro subscribers—a direct parallel to Google's offering, albeit with some unique twists.

According to OpenAI's official blog post, ChatGPT Pro's new feature:

Research Path Creation: Once you toggle "Deep Research" mode in the interface, ChatGPT Pro tries to break down your query into subtopics and outlines a plan. This plan is displayed in a side panel, showing you each step the AI will take.
Autonomous Tool Use: The system can browse the web, parse PDFs, tap into code execution (if needed), and chain these steps together—similar to Gemini. Importantly, it harnesses the "o1-Pro" model's advanced reasoning.
Iterative Updates: With each step, ChatGPT Pro explains what it's searching for and why. You can skip steps, add new ones, or let the AI run its full sequence automatically.
Consolidated Research Document: Finally, ChatGPT Pro presents a research doc that references each source, includes direct quotes or data tables, and outlines pros/cons, key takeaways, or other requested details.

Benchmark Data Shows Major Gains in Research Reliability

OpenAI shared a few early benchmarks:

High Marks on Humanity’s Last Exam: Across 3,000 expert-level questions spanning 100+ subjects, the deep research model scored 26.6% accuracy—far surpassing GPT-4o (3.3%) and OpenAI o1 (9.1%). It excelled especially in chemistry, humanities, social sciences, and mathematics by effectively sourcing specialized information.
Leading Performance on GAIA: On GAIA’s real-world benchmarks, it set a new state of the art with a 67.36 average (pass@1) and 72.57 (cons@64). This eclipsed the previous top scorer’s 63.64, showcasing robust reasoning, multi-modal fluency, and tool-use capabilities.
Advanced Multi-Step Research Approach: By harnessing browsing and Python tools, the model consolidates and verifies data from multiple sources. This integrated strategy boosts accuracy and yields comprehensive, well-referenced outputs across diverse domains.

Speed vs Depth: How Leading Platforms Compare Today

Naming Confusion: Both features are called "Deep Research," leading some reviewers to note the potential for user mix-ups.
Research Approach: Functionally, both revolve around planning, multi-step web browsing, and summarizing. Gemini leans heavily on Google's search index, while ChatGPT Pro uses Bing (or other search plugins) and can also parse local files more readily.
Speed vs. Thoroughness: Preliminary reviews indicate Gemini might be faster at raw scanning, while ChatGPT Pro (especially with its "o1-Pro chain-of-thought" approach) can be more thorough for complex reasoning. For example, a test by "AI Tools Weekly" found ChatGPT's Deep Research took 9 minutes to deliver a more in-depth 15-page report, whereas Gemini finished in 5 minutes but with fewer details.

Premium Analysis Justifies High Cost for Enterprise Users

Strengths:
- High factual accuracy due to chain-of-thought reflection plus extensive source integration.
- Flexibility to parse not just websites but also PDFs, spreadsheets, or code snippets in a single session.
- Tighter synergy with advanced ChatGPT Pro features (like code execution or large context windows).
Weaknesses:
- Cost: At $200/month, it's geared toward professionals and enterprises.
- Can be slower in "deep mode" because the AI is spending more time verifying and analyzing.
- Still not immune to hallucinations—OpenAI's own tests show an ~8–13% margin of error on complex queries, though that's an improvement over prior versions.

Despite the cost barrier, the release shows that "agentic research" is the future of enterprise AI. Users wanting the convenience and thoroughness of an AI that can systematically gather data might find ChatGPT Pro Deep Research worth the investment—especially if they were already heavy ChatGPT users.

3. Stanford's STORM Provides a Free, Open-Source Alternative with Academic Rigor

Multi-Agent System Creates Comprehensive Research with Citations

Stanford's STORM (Synthesis of Topic Outlines through Retrieval and Multi-perspective question-asking) provides a university-led, open window into Deep Research. First introduced in early 2024, and arguably the first true Deep Research tool, STORM remains a free prototype that systematically compiles a long-form, Wikipedia-like article on any given topic, complete with citations.

Here's how STORM stands out:

Multi-Agent Collaboration: STORM emulates a panel of experts—each with its own angle (historical, technical, economic, etc.). These agents coordinate to produce a well-rounded outline.
Citation by Design: Everything STORM writes is supposed to be verifiable. It inserts footnotes with links and references, though its accuracy can still vary depending on source quality.
Co-STORM Mode: Introduced in November 2024, Co-STORM lets you co-author the piece with the AI, adjusting sections, adding your own knowledge, or specifying certain references to prefer.

Open Architecture Enables Innovation at Performance Trade-off

Open-Source Approach: Unlike Google and OpenAI's proprietary systems, Stanford publishes much of STORM's code and methodology, letting the community replicate or customize it. This fosters transparency.
Academic Emphasis: STORM is built by a university lab, meaning real-time commercial polish might lag behind, but innovative features (like multi-perspective agent teams) can appear earlier.
Performance: It's slower than Gemini or ChatGPT Pro—STORM might need several minutes to produce a final article. However, the final structure can be impressively comprehensive and balanced.

Academic Focus Delivers Depth at Cost of Speed

Strengths:
- Rich, structured "encyclopedic" output.
- Free to use and fully open, encouraging advanced tinkering.
- Co-STORM allows human-in-the-loop adjustments.
Weaknesses:
- Occasional misalignment between references and claims.
- Not as user-friendly or rapid as corporate solutions.
- In some cases, requires robust hardware or cloud resources if you self-host.

Despite those trade-offs, STORM showcases how quickly open-source and academic communities are catching up to commercial Deep Research AI. Meanwhile, additional open initiatives—like DeepSeek R1 (available on Poe and Perplexity) and LlamaIndex's LlamaReport—are ensuring many of these capabilities are available at no cost to developers.

Open-Source Tools Are Rapidly Catching Up to Premium Deep Research Solutions

Though not central to the ChatGPT Pro or Gemini sections, it's important to acknowledge the tectonic shift happening in open-source AI. Projects like DeepSeek R1 (matching proprietary performance at a fraction of the cost) and frameworks like LlamaIndex (empowering custom search-and-summarize workflows) promise to bring advanced Deep Research capabilities into the hands of any developer. Observers predict these initiatives might rival or even surpass the big players within months, as they iterate quickly and share innovations openly.

For Stanford's STORM, the Co-STORM feature also underscores this trend. The barrier to entry for advanced multi-agent research setups has never been lower. This competition could push commercial providers to accelerate improvements and potentially lower costs—so both the enterprise user and the casual enthusiast stand to benefit in the near future.

Deep Research AI is Transforming Knowledge Work Across Industries

Deep Research AIs aren't just another novelty. They can genuinely save time and resources across industries:

Market Analysts and Consultants can let Gemini or ChatGPT Pro handle the initial hours of fact-finding, so they focus on strategic insights.
Academics and Researchers can use STORM or ChatGPT Pro to gather references, quickly check across journals, or get a starting outline for a literature review.
Product Teams can accelerate user research, scanning thousands of social media posts or support tickets in a fraction of the usual time.

Yet the cost factor is significant, especially for ChatGPT Pro ($200/month) which might deter smaller businesses. Meanwhile, Gemini's advanced features remain locked behind a subscription tier (pricing undisclosed but rumored to be corporate-grade). STORM is free but not as polished or quick. Over time, as open-source solutions refine their retrieval pipelines, we could see a wave of freely available or low-cost clones that keep big tech on its toes.

A Word on AGI Speculation

Deep Research systems highlight how LLMs are evolving from simple Q&A chatbots into complex, semi-autonomous agents. Sam Altman, in a brief January 2025 interview, reiterated that these developments are "a natural stepping stone toward more general AI," while adding that "autonomous research might be the single biggest productivity lever we see this decade." Whether or not this implies true AGI is near remains a debate, but few doubt the massive shift in knowledge work that's coming.

For now, the pragmatic lesson is clear: Deep Research AI is already changing how we gather and verify information. A single prompt can accomplish the research of an entire day—or more—provided we remain vigilant about verifying results. The name "Deep Research" might have become a brand arms race, but the underlying concept is transforming everything from R&D to journalism to strategic planning.

Key Takeaway: Deep Research AI Tools Are Evolving from Simple Chatbots to Autonomous Research Agents

All three reflect how AI is moving from a single-step text generator to a robust "agent" that can plan, read, and verify. The product names may overlap, but the trend is unified: humans increasingly rely on AI to handle the busywork of research, letting us focus on interpretation and decision-making.

As these systems continue improving on factual accuracy, speed, and cost, we'll likely see them become as ubiquitous as office productivity software. Already, the leaps made are striking—and with OpenAI's brand-new Deep Research for ChatGPT Pro, the competition is set to intensify. Whether you're a startup founder, a seasoned researcher, or a curious observer, it's worth exploring how these tools might revolutionize your workflow. After all, the era of "robotic research assistants" has only just begun.

What's Next?

For more insights on the transformative role of AI in research, check out our in-depth article on Supercharging M&A Research with AI: From Data Overload to Deal Insights.

If you're interested in how AI is reshaping investment banking, you might also want to read AI Tools for Sell-Side Investment Banking.

Looking for a broader view of AI's impact on deal-making? Visit our main blog page for More AI Insights or compare industry leading solutions on Deliverables AI vs ChatGPT.

At Deliverables AI, we're excited to see the progress in Deep Research AI and are working on integrating these tools into our platform. If you're interested in using Deep Research AI, please contact us to learn more.