OpenAI has just launched its new AI research agent – Deep Research. As the name suggests, this new agent is capable of doing detailed research and creating analytical reports, comprehensive articles, technical papers, and more. Competing head-to-head with Google’s Gemini Deep Research, the agent boasts of in-depth analysis and advanced synthesis skills. In this article, we will see how OpenAI’s Deep Research compares with the cheaper yet formidable Google Gemini Deep Research.
Table of Contents
What is Deep Research?
Deep research refers to a thorough and systematic investigation of a topic. The process involves referring multiple sources, critically analyzing vast amounts of data, and following structured methodologies to generate well-founded insights. Unlike surface-level exploration, deep research involves:
- Extensive Data Collection: Gathering information from diverse sources such as academic papers, reports, books, and verified online databases.
- Critical Analysis: Evaluating the credibility, relevance, and biases of the collected data.
- Contextual Understanding: Connecting new findings with existing knowledge for a more comprehensive perspective.
- Synthesis & Reporting: Organizing insights into detailed reports, articles, whitepapers, or technical documents.
- Fact-Checking & Validation: Cross-referencing sources to ensure accuracy and reliability.
Why Use Deep Research Instead of Other AI Chatbots?
Generally, a research task of this level would take a human hours or days to complete. AI chatbots help in speeding up this process by giving specific and relevant responses to research-based questions, relieving us from having to search multiple web pages manually.
Generative AI models can further structure the data into reports or articles with images, graphs, and charts. And now that most GenAI chatbots come with a web search feature, they can even cite the sources in the response.
However, the research done by these AI tools are mostly just surface-level. Also, it takes many levels of iteration and multiple prompts to get a cohesive and comprehensive response. Moreover, there is always the worry of hallucinations, where sometimes even the sources mentioned do not exist. This is why we need more advanced research tools.
AI-powered deep research tools aim to automate and enhance this process. They do extensive research, analyze vast amounts of data, and generate well-structured reports with credible citations. The two most popular generative AI tools in this category are Google Gemini Deep Research and OpenAI’s new Deep Research agent.
Also Read: Build a Deep Research Agent: $1 Alternative to $200 OpenAI’s Tool
OpenAI Deep Research vs Google Gemini Deep Research
Now let’s get into the meat of the topic. In this section, we’ll be comparing OpenAI’s Deep Research to Google Gemini’s Deep Research based on their features, pricing, and their researching skills. The aim of this comparison is to find out if OpenAI Deep Research is really worth 10x the price of Google Gemini Deep Research.
We will be doing the OpenAI vs Google Deep Research comparison in 2 parts:
- Features & Pricing Comparison
- Performance Comparison
Features and Pricing Comparison
| Feature | OpenAI Deep Research | Google Gemini Deep Research |
| Release Date | February 2nd, 2025 | December 11th, 2024 |
| Reasoning Model | OpenAI o3 | Google Gemini 1.5 Flash |
| Cost | $200/month (Pro users, 100 queries/month) | $20/month (Gemini Advanced) |
| Availability | US-only (expanding to Plus/Enterprise users soon) | Global via Gemini web app (English-only) |
| Compute Limits | Longer queries consume more resources | Daily request caps |
OpenAI vs Google Gemini Deep Research: Performance Comparison
It’s time for the ultimate showdown. We will now try out both the tools for the same prompt and compare their deep research capabilities. We will be assessing both the process and depth of the research as well as the structure and quality of the generated reports.
OpenAI Deep Research
Here’s a prompt OpenAI has tried out to demonstrate the skills of its Deep Research agent. The tool is asked to generate a report based on a country-wise analysis of mobile penetration % of the top 10 developed and developing countries. It is also asked to find the iOS and android adoption rates and the % of people who wish to learn a new language, for the same demographic. Let’s see how OpenAI Deep Research does this.
Prompt: “Help me find iOS and android adoption rates, % who want to learn another language, and change in mobile penetration, over the past 5 years, for top 10 developed and top 10 developing countries by GDP. Lay this info out in a formatted report, a table on metrics, and include recommendations on markets to target for a new translation app from ChatGPT, focusing on markets ChatGPT could better expand into.”
Initial response by OpenAI Deep Research:
Follow-up prompt: “Penetration as a percentage, and look at overall usage. Make your best assumptions on the rest!”
Final Response by OpenAI Deep Research:
Review:
OpenAI’s Deep Research agent took 11 minutes to gather information from 29 relevant sources and generate the report.
The Deep Research agent follows a highly iterative process that is dynamic in real-time. The agent first asks a few questions based on the prompt to understand the user’s exact need. This sets the context and perspective, giving some pointers and direction for the agent to research in.
The agent then follows a step‑by‑step approach starting with data extraction, followed by data validation, then annotation, and finally ending with trend analysis. As a result of this process, the output is organized in a clear, layered sequence. It starts with an overview of the research question, and ends with synthesized insights and strategic recommendations.
The agent emphasizes transparency by making the entire research process visible to the user with real‑time updates. It even displays charts and graphs on‑screen, and annotates trends as it works through the data. This makes it easy for the user to understand the flow of the process and see exactly how conclusions are reached.
OpenAI’s Deep Research does a very deep analysis of the topic and generates a wholistic and comprehensive report. The sources of particular statements are mentioned alongside, within the report, for direct verification. The search interface also lists out the source websites separately on the side panel for easy and direct access.
Google Gemini Deep Research
Now let’s try out the same prompt on Google’s Gemini Deep Research and see what it manages to pull off.
Note: Since Gemini Deep Research does not ask follow up questions before generating the response, I’ve added the follow-up prompt along with the original prompt, for a fair comparison.
Initial response by Google Gemini Deep Research:
Final response by Google Gemini Deep Research:
Review
Google Gemini took about 6 minutes to source, cross check, and analyze data from 29 relevant sources and generate the report.
Google Gemini first shares its plan on how it intends to do the research, starting with searching the web, analyzing the results, and then creating the report. The user has the option of editing this plan and guiding the model in the right direction, before starting the research.
Although the research starts with the model listing out what it’s doing, the process soon becomes static. It just shows a list of the websites the model is reading through, adding more sites, as the research progresses.
Gemini’s Deep Research output is delivered as a well-written detailed report which can be opened in Google Docs. The report is divided into well‑defined sections including a ‘Research Methodology’ section that explains how the research was done and justifies the quality of the sources. The report also includes well‑structured tables summarizing the data with explicit figures for comparison. These tables can also be opened in Google Sheets.
The sources used in every paragraph are listed at the end of that paragraph, so users can directly refer to or explore them further. However it does not explicitly mark which sentence came from which source.
Comparative Analysis Summary
| Parameter | OpenAI Deep Research | Gemini Deep Research |
| Research Methodology | – Highly iterative and visibly time‑intensive. – Uses live data extraction, cross‑checking, and annotations. – Transparent, real‑time process. |
– Systematic and thorough. – Details its data-gathering process in a ‘Research Methodology’ section. – Extensive source review. |
| Research Time | 5 – 30 minutes | 5 – 15 minutes |
| Depth of Analysis | – Very deep analysis of comparatively fewer sources. – Provides on‑screen charts, trend lines, and historical context. – Shows detailed comparisons between regions. |
– In-depth analysis of a more extensive resource pool. – Provides a summary table with explicit figures and contextual narrative for each market. |
| Interface & Visual Features | – Dynamic interface with real‑time interactive visuals (charts, graphs, annotations). | – Static process interface. – Output delivered as a well‑formatted written report with structured headings and summary tables. |
| Tools and Supporting Features | – Integrated charting, annotation, and live data extraction tools. | – Leverages web search and document synthesis tools. |
| Output Structure & Clarity | – Multi‑layered structure with a better flow: overview → live data gathering → visual analysis → insights/recommendations. – Clearer and more direct citations. |
– Clearly organized written report with distinct sections. – Detailed narrative that encourages self‑paced review and further learning. |
| Recommended Use Case | Ideal for decision‑makers and analysts who value seeing the research process live and in full transparency. | Ideal for strategic planning where a thorough, static, well-cited document is needed. |
Conclusion
Both OpenAI Deep Research and Google Gemini Deep Research bring powerful AI-driven research capabilities to the table. OpenAI’s Deep Research focuses on real-time, interactive analysis with transparency. Meanwhile, Google Gemini Deep Research offers a more affordable yet structured research methodology, presenting a well-formatted, document-friendly report. While OpenAI Deep Research provides deeper insights with a more iterative approach, Gemini Deep Research remains a strong contender for users who prefer a straightforward research output at a lower price.
Choosing between the two really depends on your needs. If you require detailed, real-time insights with live annotations, OpenAI Deep Research is worth the investment. However, if you prefer an affordable, static yet well-structured research document, Google Gemini Deep Research is a solid choice.
Frequently Asked Questions
Q1. How is OpenAI Deep Research different from Google Gemini Deep Research?
A. Firstly, OpenAI’s Deep Research is an agent, while Google Gemini Deep Research is an AI chatbot. OpenAI Deep Research focuses on a real-time, iterative research process with live annotations and transparency. On the other hand, Google Gemini Deep Research offers structured, document-based reports with extensive source citations.
Q2. Which is faster – OpenAI Deep Research or Google Gemini Deep Research?
A. Google Gemini Deep Research is generally faster, completing research in 5-15 minutes, whereas OpenAI Deep Research can take between 5-30 minutes depending on the complexity of the query.
Q3. Is Google Gemini Deep Research more cost-effective than OpenAI Deep Research?
A. Yes, Google Gemini Deep Research costs only $20/month, while OpenAI Deep Research is significantly more expensive at $200/month for Pro users.
Q4. Which AI research tool is better for business decision-making?
A. OpenAI Deep Research is better suited for data-driven decision-makers who need live insights and a transparent research process. Google Gemini Deep Research is ideal for structured, document-based strategic planning.
Q5. Is OpenAI Deep Research available to all?
A. Not yet. Currently, OpenAI Deep Research is only available in the U.S. and that too, to only Pro subscribers. It is expected to roll out to more users across other countries soon. Meanwhile, Google Gemini Deep Research is available globally for Gemini Advanced users.
Q6. Which tool provides better visualization and data representation?
A. OpenAI Deep Research excels in interactive visuals, live annotations, and dynamic charting. Google Gemini Deep Research presents data in structured tables but lacks real-time visual updates.
Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.
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