Introduction: A New Era of AI-Driven Research

As AI innovation surges globally, OpenAI has unveiled its latest creation, Deep Research. This new AI-powered investigative tool, integrated within ChatGPT, is OpenAI’s response to the rising success of DeepSeek, China’s popular AI chatbot. Positioned as an advanced AI agent capable of conducting independent research, Deep Research aims to redefine how users access, analyze, and synthesize data. Operating on OpenAI’s upcoming o3 model, the system promises in-depth analysis, making it a pivotal resource for professionals and casual users alike.

What Is Deep Research?

Deep Research is an AI agent integrated into ChatGPT, designed to autonomously collect and analyze multi-dimensional data, creating expert-level research reports. Unlike traditional AI models that prioritize rapid responses, Deep Research embraces longer processing times—ranging from 5 to 30 minutes or more—allowing it to deliver comprehensive and high-quality results. By simulating the meticulous workflow of human researchers, it breaks free from latency constraints that typically limit AI applications.

Reports generated by Deep Research are not simple responses but fully structured documents. OpenAI plans to further enhance this experience by integrating visuals, graphs, and in-depth contextual analysis, ensuring clarity and precision for users across various fields.

Why OpenAI Developed Deep Research

The demand for in-depth, autonomous research tools is skyrocketing as AI becomes a central force across industries. While current large language models (LLMs) and traditional search engines offer fragmented or surface-level insights, Deep Research fills a crucial gap by providing context-rich, well-researched answers. Designed as a virtual research assistant, it is tailored for professionals in fields like finance, science, and technology, where deep exploration and detailed analysis are key to decision-making.

However, OpenAI’s ambitions go beyond serving experts. Deep Research’s personalized suggestions allow everyday users to access complex data quickly and make informed decisions based on their individual needs. By doing so, OpenAI not only caters to a professional audience but also positions itself as a competitive alternative to DeepSeek, China’s cost-effective AI solution that has garnered significant global attention.

According to OpenAI CEO Sam Altman, while DeepSeek may be a strong competitor, its capabilities are “not groundbreaking,” emphasizing that Deep Research offers a more sophisticated approach by conducting multi-source analysis and presenting results at an expert level.

How Deep Research Works: A Step-by-Step Process

OpenAI’s new o3 model powers Deep Research, making it available to users on the Pro plan at a cost of $200 per month for 100 research queries. Here’s how it works:

1. Activation: Users activate Deep Research through the ChatGPT interface, selecting it from the tool’s dashboard.
2. Query Submission: Users enter detailed queries describing their research goals. Deep Research’s reverse-querying mechanism clarifies ambiguous requests, ensuring the system fully understands user requirements.
3. Autonomous Research Process: Deep Research autonomously gathers data from multiple verified sources, analyzing information across various dimensions. It prioritizes reputable databases, scientific papers, and industry-specific resources while filtering out unreliable content.
4. Real-Time Feedback and Monitoring: During the research process, users can view ongoing tasks via the “Activity” and “Source” panels, tracking which sources and tools Deep Research is using.
5. Comprehensive Report Generation: Upon completion (typically within 5 to 30 minutes), the system delivers a full report containing key insights, analysis, and recommendations. For example, a query on “The Impact of AI on Future Education” would generate a report covering personalized learning, automated educational management, and emerging AI trends in teaching.

Benchmarking Performance: How Does Deep Research Compare?

OpenAI rigorously tested Deep Research across three benchmarks: Humanity’s Last Exam, GAIA (General AI Agent), and Internal Evaluations, highlighting its strengths compared to previous models like o1, DeepSeek-R1, and Claude 3.5 Sonnet.

1. Humanity’s Last Exam

This benchmark assesses AI models on their ability to exhibit expert-level knowledge across multiple domains, including science, linguistics, biology, and mathematics. Deep Research achieved an accuracy rate of 26.6%, outperforming previous models like OpenAI’s o1 (9.1%), DeepSeek-R1 (9.4%), and Claude 3.5 Sonnet (4.3%).

Key areas of improvement included:

• Chemistry: Advanced multi-dimensional analysis allowed it to handle complex chemical interactions.
• Social sciences and humanities: The system excelled in contextual reasoning across historical and societal topics.
• Mathematics: Deep Research demonstrated superior problem-solving abilities, especially in higher-level logic-based scenarios.

2. GAIA Benchmark

The GAIA benchmark evaluates AI systems on real-world problem-solving tasks, requiring logical reasoning, web navigation, and multi-method expression. Deep Research dominated in GAIA’s Level 3 tasks, involving complex research projects that require cross-referencing and synthesis.

Notable metrics included:

• High pass@1 scores: Deep Research consistently delivered accurate results on the first attempt.
• High cons@64 scores: The system demonstrated the ability to refine incorrect answers over multiple attempts by incorporating newly gathered data.

3. Internal Evaluation

OpenAI’s internal evaluations further assessed Deep Research’s performance using task-specific metrics like tool calls (the AI’s ability to initiate external processes to collect data) and the economic impact of tasks (evaluating the complexity and financial stakes of tasks).

The analysis revealed:

• Efficiency with tool calls: The more tools Deep Research accessed, the higher its success rate in solving complex problems.
• Strong performance in economically low-impact tasks: Deep Research excelled in tasks requiring 1-3 hours of human effort, particularly those involving information synthesis.

Challenges and Limitations

While Deep Research demonstrates impressive potential, it is not without challenges. “Hallucinations,” where the AI generates incorrect or fabricated information, remain a concern. Additionally, the system faces difficulties in distinguishing between authoritative sources and speculative information, especially when data from unreliable or non-peer-reviewed platforms infiltrate its research process.

OpenAI is aware of these issues and plans to refine the tool further, ensuring greater accuracy and contextual understanding.

Why Deep Research Matters in the AI Race

Deep Research’s launch comes as OpenAI competes with global AI leaders like DeepSeek. With its superior ability to navigate complex data landscapes and provide actionable insights, Deep Research could become a game-changer for industries that rely on intensive research, from finance and healthcare to scientific innovation. By addressing both professional and mainstream user needs, OpenAI aims to solidify its dominance in the growing market for AI-powered research agents.

Additional Updates

• DeepSeek Expands Global Reach: Following its success in Asia, DeepSeek plans to launch in European and North American markets by Q3 2025.
• AI Legislation Discussions Intensify: Lawmakers in the U.S. and Europe are accelerating discussions on regulating AI agents due to growing concerns over misinformation.
• Google’s Response: Google has hinted at developing a rival AI research agent called AlphaQuery, potentially debuting in mid-2026.
• Corporate Adoption of AI Agents: Companies like JPMorgan and Pfizer have expressed interest in integrating Deep Research into their research workflows, citing its ability to reduce human effort by up to 60% in data-driven projects.

A Strategic Step for OpenAI

As OpenAI pushes the boundaries of AI capabilities, Deep Research is more than just a tool—it’s a paradigm shift. By offering autonomous, expert-level research, it promises to reshape industries reliant on in-depth data analysis. However, as competition heats up, OpenAI must continue refining its systems, addressing issues of accuracy and source reliability to maintain its edge in the evolving AI landscape.

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