TLDR: Deep Research is an AI-powered research tool that can conduct in-depth, multi-step investigations and produce structured reports using information from multiple online sources. This tool can be particularly useful for Small and Medium-sized Businesses (SMBs) that need to perform tasks such as competitive analysis, market research, and high-stakes purchasing decisions. While both Deep Research and ChatGPT Standard Browsing can access and process online information, Deep Research offers more comprehensive analysis, structured output, and clear citations. However, users should be aware that Deep Research may also produce incorrect facts or flawed inferences.
Deep Research is an AI-powered agent from OpenAI designed for in-depth, multi-step research on the internet. It is powered by a version of the upcoming o3 model, optimized for web browsing and real-world data analysis. Unlike standard ChatGPT, which provides quick responses, Deep Research can autonomously find, analyze, and synthesize information from numerous online sources to produce structured reports.
How Deep Research Works
OpenAI’s Researcher is an AI tool that is trained using reinforcement learning on real-world browsing and reasoning tasks and builds on OpenAI’s advancements in reasoning models. It follows an iterative, step-by-step research process, improving its ability to synthesize complex topics into structured reports. The AI can browse the web, analyze multiple sources, and synthesize large amounts of information into a research report, which may take 5–30 minutes; additionally, it can use Python tools for data analysis and can embed graphs and images in its responses.
Key Features of Deep Research
- Well-Structured Reports: Generates organized reports with clear sections, appropriate formatting, and effective use of bullet points.
- Multi-Step Investigations & Comprehensive Analysis: Capable of handling complex, multi-step investigations and synthesizing information into a useful format.
- Multiple Source Referencing & Citations: References and cross-references multiple sources, with citations placed immediately after the relevant information for easy fact-checking.
- Real-World Data Analysis & Tool Use: Optimized for analyzing real-world data and proficient in using relevant analysis tools.
How Deep Research Can Be Useful for SMBs
- Competitive analysis: Deep Research can be used to conduct competitive analysis or trend forecasting.
- Market research: It can gather information from multiple sources, analyze it, and synthesize it.
- High-stakes purchasing decisions: Deep Research can assist with making high-stakes purchasing decisions.
- Identifying Niche Information: It is particularly effective at finding niche, non-intuitive information that would require browsing numerous websites.
- Saving Time: Deep Research frees up valuable time by allowing you to offload and expedite complex, time-intensive web research with just one query.
Deep Research vs. ChatGPT’s Standard Browsing
| Feature | Deep Research | ChatGPT Standard Browsing |
|---|---|---|
| Purpose | In-depth analysis, optimized for web browsing & real-world data analysis. | Quick answers and summaries for real-time, multimodal conversations. |
| Output | Structured reports with clear formatting and citations; designed to be used as a work product. | Quick responses with limited synthesis and no citations. |
| Analysis | Autonomous synthesis of information from multiple sources; cross-references data. | Can browse, but does not synthesize or cite sources. |
| Complexity | Optimized for complex, multi-faceted tasks requiring many sources & synthesis. | Suited for real-time conversations, not in-depth analysis. |
| Citations | Clear citations placed directly after the information they reference, for easy fact-checking. | Lacks structured citations. |
Limitations of Deep Research
- Hallucination and Incorrect Inferences: Deep Research may sometimes hallucinate facts or make incorrect inferences, although this occurs less frequently than with existing ChatGPT models.
- Distinguishing Authoritative Information: The tool may struggle to differentiate between authoritative information and rumors.
- Confidence Calibration: Deep Research exhibits weakness in confidence calibration, often failing to accurately convey uncertainty.
- Formatting Errors: At launch, Deep Research may have minor formatting errors in reports and citations.
- Compute Intensive and Limited Access: Tasks may take longer to start, and Deep Research is currently very compute intensive. This limits access to Pro users with 100 queries per month. Access is planned to expand to Plus and Team users, followed by Enterprise, with higher rate limits for all paid users in the future when a more cost-effective version is released.
Access
Deep Research is currently available to Pro users with a limit of 100 queries per month, and OpenAI plans to expand access to Plus, Team, and Enterprise users soon.
In essence, Deep Research is a powerful tool for SMBs looking to enhance their research capabilities, but users should remain aware of its limitations and verify the information provided.
Conclusion
Deep Research offers SMBs a powerful AI tool for in-depth insights, streamlining research tasks from competitive analysis to purchasing decisions. Its structured reports and citations enhance efficiency, but users should be aware of potential inaccuracies. Despite limitations, Deep Research is a compelling asset for informed decision-making and growth, as OpenAI continues to refine and expand its access.
