Deep Research is a newly launched AI agent developed by OpenAI and powered by a version of the o3 model. It is designed to go beyond traditional chatbot responses by autonomously browsing the internet, analyzing data from multiple sources, and generating well-structured, multi-step reports. This tool is aimed at users who require a higher level of information synthesis than what is typically possible with a standard AI interaction.
Unlike conventional ChatGPT sessions that deliver rapid but shallow summaries, Deep Research is capable of iterative thinking. It references multiple websites, cross-verifies information, and takes a methodical approach to information gathering. It’s more than just a chatbot; it acts as a digital research assistant designed to replace hours of manual browsing.
The Problem Deep Research Solves
Modern research tasks, whether academic, journalistic, or business-related, often require sifting through dozens of articles, white papers, blog posts, and datasets. This process is time-consuming and prone to errors due to human bias or oversight. Deep Research tackles this problem by using a step-by-step methodology to gather and filter information from a broad array of sources. It can then organize this data into coherent, objective summaries.
For example, a user trying to choose a new car might spend days comparing reviews, calculating cost-of-ownership, evaluating performance specifications, and checking for recalls. Deep Research can take that entire workflow and perform it in minutes, offering a complete, unbiased overview of available models and their trade-offs.
Differentiation from ChatGPT
ChatGPT, particularly the Pro version with browsing enabled, can already answer questions based on real-time data. However, these answers are usually generated in a single pass and are optimized for speed rather than depth. Deep Research, on the other hand, breaks down questions into sub-questions, conducts extensive web searches, compiles data from multiple perspectives, and then reassembles everything into a coherent, contextually accurate report.
Deep Research uses a multi-step architecture. It begins by identifying the core objective of a prompt. It then plans a research route, issues web queries, retrieves information, assesses credibility, summarizes key points, and compiles the results into a structured document. This iterative approach closely mirrors how a human researcher would work.
Key Capabilities and Use Cases
Deep Research is useful in a variety of professional and personal contexts. Professionals in sectors such as finance, science, healthcare, and public policy often need to make decisions based on complex and ever-evolving information. With Deep Research, they can generate well-cited research summaries, conduct trend analysis, or review regulatory developments.
Students and researchers can use Deep Research to explore a new topic, identify relevant studies, and generate annotated bibliographies. Consumers can use it to make better purchasing decisions by evaluating pros and cons of products based on verified sources. Writers and journalists benefit from its ability to cross-check facts, ensuring greater reliability in their output.
Its most valuable use cases lie in tasks that require gathering information from scattered sources, reconciling conflicting data points, and assembling an accurate narrative. Deep Research excels at answering questions where the answer cannot be found on a single page, but requires the merging of multiple strands of evidence.
Technology Behind Deep Research
The backbone of Deep Research is the o3 model, a yet-to-be-released update in the GPT family. This model builds upon the advancements in natural language reasoning and context retention. In Deep Research, the model is fine-tuned using reinforcement learning on real-world research tasks. The agent is trained not just to answer questions, but to navigate the web strategically, evaluate data critically, and deliver trustworthy results.
The system also incorporates tool use, including Python for data analysis and automated source validation methods. When combined, these tools enable the AI to process charts, perform calculations, interpret complex visualizations, and summarize reports with a high degree of accuracy.
Early Performance Benchmarks
Initial benchmarks show that Deep Research significantly outperforms earlier models in complex research tasks. On the Humanity’s Last Exam, a challenging evaluation involving over 100 academic disciplines, Deep Research scored 26.6 percent—more than doubling the performance of its predecessors. This benchmark measures how well an AI can synthesize expert-level content across disciplines such as chemistry, history, mathematics, and linguistics.
On the GAIA benchmark, which assesses an AI agent’s ability to answer real-world, web-based queries using reasoning, browsing, and tool use, Deep Research also leads the current field. It showed especially strong performance on Level 3 tasks, which require multi-step investigation and synthesis. These results suggest Deep Research is setting a new standard for AI-driven research.
How to Use OpenAI’s Deep Research Effectively
Accessing Deep Research
At the time of writing, Deep Research is available only to Pro-tier users on OpenAI’s platform. Each user is limited to 100 research queries per month. OpenAI has indicated that access will expand in the future to include Plus, Team, and Enterprise accounts. The tool is integrated directly into the chat interface, making it accessible without requiring additional setup.
When launching a Deep Research query, users can provide a detailed prompt describing the topic or question they want to explore. The more context provided at the start, the more accurate and relevant the resulting report will be.
Research Workflow Behind the Scenes
Deep Research uses a structured, iterative research pipeline. Upon receiving a prompt, it begins by breaking the question into manageable components. It then plans a research approach by determining which queries need to be sent out and in what order. The system performs web searches, selects relevant documents, and processes content to extract meaningful information.
Once it has gathered a sufficient amount of data, the system evaluates and organizes the results by relevance, accuracy, and diversity of perspective. It then synthesizes the findings into a coherent output that addresses the original question. The tool may also conduct follow-up searches if it identifies missing information or inconsistencies during analysis.
This loop of questioning, browsing, reasoning, and synthesis enables Deep Research to mimic the behavior of a human analyst who actively thinks, revisits sources, and updates conclusions.
Best Practices for Prompting Deep Research
To get the best performance from Deep Research, users should craft prompts that are specific and goal-oriented. Vague prompts can lead to shallow results, while overly rigid prompts might limit the model’s creativity. A good prompt sets boundaries but leaves room for the agent to interpret and plan.
Here are several elements to consider when writing effective prompts:
Define the objective clearly
State the exact outcome you’re looking for. Instead of asking for “information on electric vehicles,” try asking for “a comparative analysis of electric SUVs under $60,000 released in the past two years based on range, price, and reliability.”
Specify the structure you want
If you need a table, timeline, or list of pros and cons, state that clearly. Deep Research will format the results accordingly.
Include context or constraints
If you want sources only from the past year, or content written for a general audience, include that in your prompt.
Avoid redundancy
The model is trained to handle complexity, so you do not need to over-explain or rephrase your prompt multiple times. A clean, concise instruction often works better.
Let it think
Allow the model time to work through its steps. Avoid interrupting or restarting the task unless necessary.
Prompt Example and Strategy: AI Ecosystems
One effective use case for Deep Research is understanding technology ecosystems. For instance, the landscape of AI products from a major tech company can be fragmented across blogs, conference keynotes, product pages, and academic papers. Users often struggle to get a comprehensive overview without consulting a dozen different sources.
To test Deep Research, a prompt might look like this:
“Provide an up-to-date overview of all major AI tools, platforms, and models currently developed by Google, including their use cases, technical specifications, and target users. Structure the output into categories and include any major upcoming releases or internal projects that have been publicly mentioned.”
Deep Research responds by identifying known products like Gemini, Imagen, and Veo, then searches for associated details such as versions, applications, release notes, and associated research papers. It often discovers internal initiatives or early-access tools that are not yet broadly discussed.
The output from this type of prompt is significantly more detailed than what would be achievable through a standard chatbot. It organizes tools by category, explains capabilities, and cites developments mentioned in recent public events.
Limitations You Should Expect
Despite its strengths, Deep Research is not immune to errors. Some of the common issues include:
Outdated or incorrect information
The model might occasionally retrieve cached or older versions of content, leading to outdated conclusions.
Misinterpreted data
While the model attempts to evaluate credibility, it may still draw incorrect inferences, especially when interpreting ambiguous or technical language.
Surface-level synthesis in some cases
On particularly broad topics or when poorly prompted, the tool might prioritize speed over depth, summarizing without meaningful analysis.
Limited access to gated or proprietary content
Deep Research cannot access information behind paywalls or within private databases, which limits its coverage of specialized topics.
These limitations mean users should review outputs critically and treat them as a foundation for further refinement, rather than as final answers.
The Importance of Review and Validation
It is essential to validate any important findings delivered by Deep Research. Users should treat its results as high-quality drafts that still require fact-checking. For professional or high-stakes use, reviewing cited sources and cross-referencing with trusted data is a best practice.
Over time, the tool is likely to improve its internal fact-checking mechanisms and expand its ability to self-correct. Until then, combining the tool’s efficiency with human judgment produces the most reliable results.
Evaluating Deep Research Through Benchmarks
The Role of Benchmarks in AI Assessment
Benchmarks are essential tools for understanding how well an AI model performs across a range of real-world tasks. They provide a standardized way to measure accuracy, reasoning ability, and general intelligence across different domains. OpenAI has evaluated Deep Research using a variety of benchmark tests to understand its performance relative to earlier models and competing systems.
These benchmarks also give users insight into where the model excels and where it might need human supervision. For an agent like Deep Research, which is designed to perform complex, multi-step research, these evaluations are especially important because they reflect the tool’s ability to think critically, process unfamiliar subjects, and adapt its research strategies.
Humanity’s Last Exam: Measuring Expert-Level Reasoning
One of the most significant evaluations OpenAI used is Humanity’s Last Exam. This benchmark tests AI models on over one hundred expert-level subjects, including engineering, linguistics, medicine, and environmental science. The questions range from multiple-choice to short-answer formats and are designed to challenge even trained professionals.
Deep Research scored 26.6 percent on this benchmark, which represents a major leap over previous models. For comparison, OpenAI’s o1 model scored 9.1 percent, and other strong models like DeepSeek-R1 and Claude 3.5 Sonnet scored under 10 percent. This means Deep Research is capable of navigating much more complex and nuanced questions than earlier versions of GPT or its competitors.
The biggest gains were observed in subjects like chemistry, social sciences, and mathematics. These are areas where success often depends on breaking down dense questions and finding reliable, specialized information—exactly the kind of tasks Deep Research was built to handle.
Performance Gaps and Their Implications
Even though 26.6 percent may sound modest, the context matters. Humanity’s Last Exam is not a trivia quiz or a basic knowledge test. It is built to simulate the types of questions a human researcher might face during graduate-level work or in high-stakes policy analysis. Performing well on this test suggests a high level of general reasoning and research skill.
However, it also highlights some gaps. The model still misses nearly three-quarters of the questions, which indicates that its research strategy, while better than past models, is not yet fully dependable for expert-level tasks without oversight. Users should be especially cautious when using Deep Research for specialized or sensitive topics where precision is critical.
GAIA Benchmark: Real-World Task Performance
The GAIA benchmark evaluates how well an AI model performs on real-world, multi-step tasks that require reasoning, browsing, and tool use. It includes questions that cannot be answered with a simple search or one-paragraph summary. Instead, the model must explore, compare, and integrate diverse data points to reach a conclusion.
Deep Research set a new record on the GAIA leaderboard, with particularly strong performance in Level 3 tasks. These tasks are the most complex, often requiring extended reasoning across multiple stages. At the highest evaluation setting, Deep Research achieved a 72.57 percent average score. That includes an impressive 58.03 percent on Level 3 questions, compared to the previous state-of-the-art average of 42.31 percent.
The model’s high pass-at-one score also means it gets the correct answer more frequently on the first try, rather than needing multiple revisions. Its consistency improves further when allowed to use up to 64 attempts per query. This behavior mirrors how human researchers refine their conclusions after revisiting data or reconsidering assumptions.
Internal Evaluations by OpenAI
In addition to external benchmarks, OpenAI conducted internal tests using domain-specific tasks reviewed by subject-matter experts. These evaluations explored how Deep Research performs under controlled conditions, including access to its browsing and Python toolset.
One of the key insights from these internal evaluations is that Deep Research performs better the more it interacts with tools. As the model is allowed to make additional tool calls—such as performing calculations, parsing code, or revisiting sources—its accuracy increases significantly. This shows that Deep Research becomes smarter when it’s allowed to slow down and iterate.
Another interesting finding is how performance relates to economic value. OpenAI observed that Deep Research performs best on tasks with low to moderate economic value. As the financial stakes of a task increase, accuracy tends to drop. This could be because higher-value tasks are more complex or depend on proprietary data not easily found online.
This insight is valuable for businesses or institutions using the model in decision-making. While the model is strong in research and reporting, it may struggle with tasks that involve highly confidential, regulated, or non-public information.
Alignment With Human Research Timelines
OpenAI also compared how Deep Research performs relative to the amount of time a human would need to complete the same task. The model is most accurate on tasks that would take a person between one and three hours to finish. Interestingly, the model does not perform significantly worse on longer tasks, suggesting that task complexity, not just duration, determines the challenge level.
This reinforces the idea that Deep Research is most helpful for moderately complex topics—detailed enough to need investigation, but not so niche or proprietary that accurate public data is unavailable.
Why These Benchmarks Matter
These benchmarks offer more than just scores. They reveal the emerging capabilities of research-grade AI agents and help users calibrate their expectations. When Deep Research succeeds, it does so by simulating a form of intelligent behavior: asking follow-up questions, validating assumptions, and re-evaluating previous steps. This makes it vastly more capable than models that produce fast but shallow answers.
However, the benchmarks also make it clear that the model is not perfect. It requires guidance, validation, and sometimes correction. It is a powerful tool—not a replacement for subject-matter expertise.
Real-World Use Cases and Advanced Strategies
Deep Research in Professional Workflows
Deep Research is not just a more powerful chatbot—it is an early glimpse of how AI will be integrated into professional environments where structured, reliable research is critical. It can already handle complex, time-consuming tasks that typically require expert analysis, offering clear advantages in industries such as finance, healthcare, engineering, journalism, and public policy.
Professionals can use Deep Research to gather intelligence on market trends, perform competitive analysis, identify emerging technologies, and track regulatory developments. In finance, for example, a user could prompt the model to analyze quarterly performance across a group of companies, compare analyst sentiment, and highlight red flags. In journalism, reporters could use Deep Research to gather background on people, timelines of events, or policy changes across different jurisdictions.
Researchers in academia or think tanks can use Deep Research to explore niche topics across disciplines, helping them identify sources, summarize prior work, and generate hypotheses. For students, it becomes a guided research assistant capable of organizing information for theses or long-form reports.
The ability to work across domains, follow citations, and present findings in structured formats makes it far more than a search tool—it’s a scalable knowledge partner.
Prompting Deep Research for Maximum Value
To extract the full capabilities of Deep Research, users should learn to engineer prompts that guide it toward structured, multi-layered outputs. This involves thinking not just about what you want to know, but how you want the model to think about the problem.
For example, a prompt like “Summarize the pros and cons of nuclear fusion as an energy source” will produce a decent overview. But prompting it with “Perform a comparative analysis between current nuclear fusion and fission energy systems, including engineering challenges, global research efforts, cost forecasts, and environmental impact, with citations where available” will initiate a far deeper research process.
Advanced prompts can include contextual framing, temporal constraints, and formatting instructions. You might add requirements such as “Include only studies published after 2022” or “Organize results by region and technology type.” These prompts encourage Deep Research to work in stages, validate its steps, and synthesize results in a structured way.
Another tactic is iterative prompting. After receiving an initial report, you can ask follow-up questions like “Can you clarify the assumptions behind those cost forecasts?” or “What are the key technical bottlenecks mentioned in recent academic papers?” This method turns the research into a conversation, allowing the model to refine and deepen its outputs.
Use Case: Strategic Technology Forecasting
One compelling use case for Deep Research is in forecasting technological developments. For example, a tech executive or investor might want a forecast on the commercialization of quantum computing or the future of AI regulation in specific countries. These are inherently multi-dimensional topics that require scanning across policy documents, scientific publications, corporate press releases, and trend analyses.
A strong prompt could be “Produce a structured forecast for the commercial rollout of quantum computing technologies in North America, Asia, and Europe. Include recent research trends, national funding strategies, corporate investments, and anticipated bottlenecks in hardware or talent. Identify which organizations are leading the field and why.”
Deep Research can scan government statements, R&D announcements, academic journals, and industry news to piece together a grounded forecast. While it cannot predict the future, it can synthesize signals and trends in a way that approximates what a human analyst might produce over several days of research.
Use Case: Large-Scale Literature Reviews
In scientific fields where knowledge evolves rapidly, literature reviews can take weeks to complete. Deep Research shortens that process by scanning dozens or even hundreds of sources and presenting a categorized summary. For example, a medical researcher could prompt the model to “Summarize the latest findings on GLP-1 receptor agonists for diabetes treatment, focusing on randomized clinical trials published after 2022 and separating findings by drug type and patient population.”
The model can scan scholarly archives, research hubs, and preprint repositories to identify high-relevance studies. It will then summarize them, highlight conflicting evidence, and in many cases provide links to full-text sources or author affiliations. While human verification is always necessary, this kind of work can significantly accelerate the research phase of scientific projects.
Final thoughts
Deep Research signals a major shift in how humans interact with large volumes of information. Instead of passively consuming search results or short AI summaries, users can now direct AI agents to perform autonomous investigations with multi-step reasoning and sourcing.
This raises new questions about trust, transparency, and reliance. As users increasingly depend on AI-generated reports, there is a risk of over-trusting results without reviewing underlying sources. OpenAI has started addressing this by improving source transparency, but long-term solutions may require new standards for citation, error correction, and peer review in AI-generated content.
There are also legal and ethical challenges. If Deep Research inadvertently spreads inaccurate medical or legal information, who is responsible? How do organizations verify the integrity of AI-driven research before using it to make decisions? These are unresolved issues that will shape the way AI is adopted in regulated sectors.
Despite these concerns, the potential is enormous. As the tool becomes more accurate, better aligned, and more widely accessible, it may soon become a standard part of every knowledge worker’s toolkit. Whether you are writing a book, launching a product, or shaping policy, Deep Research represents a new way to access and reason about the world’s information.