The role of Deep Research models in AI advancements

Research has always been a race against time. Now, with artificial intelligence (AI), this race is becoming faster than ever before. AI isn’t just about automating tasks anymore. It’s changing how we gather, analyze, and interpret information. From sorting through massive datasets to uncovering insights in seconds, AI research tools are redefining the speed and depth of information discovery.

A key part of this shift is the rise of Deep Research models, which have quickly become a major trend. Companies across the AI industry are launching their own versions, signaling a fundamental change in how AI processes and delivers information. 

Unlike traditional AI tools that offer surface-level responses, these advanced models dive deeper, trying to provide highly contextualized and accurate insights. Leading tech companies like OpenAI, Google, and Perplexity are driving this movement, continuously improving AI’s research capabilities.

This progress is clear in benchmark tests like Humanity’s Last Exam, which evaluates an AI model on complex reasoning and problem-solving. OpenAI’s Deep Research model showed an impressive improvement over previous versions. This leap in performance means the model can tackle challenging research questions with precision and accuracy.

In this article, we’ll explore the unique features of Deep Research models from OpenAI, Google, and Perplexity. We'll look at how these models are enhancing research methods, boosting productivity, and shaping the future of AI-powered assistants.

An overview of the Deep Research models

Let's start by taking a closer look at how different Deep Research models are driving research innovation with advanced insights.

OpenAI’s Deep Research model

On February 2, 2025, OpenAI introduced Deep Research, an advanced AI agent like model that is designed for in-depth, multi-step research. Enabled by a variant of the upcoming OpenAI o3 model, it can scan hundreds of sources, including text, images, and PDFs. It then uses this data to generate detailed, cited reports in just 5 to 30 minutes, which is far faster than manual research.

Unlike basic AI chatbots, Deep Research is built for professionals in sectors like finance, science, and engineering who need tools with accuracy and depth, not just quick answers. Deep Research even asks users for clarification during the process to refine its results. 

OpenAI is continuing to improve it, and recently added embedded images with citations and better file handling. Overall, whether analyzing markets or breaking down technical studies, Deep Research aims to deliver structured and reliable insights.

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Google’s Gemini Deep Research model

Google’s Gemini Deep Research, which was launched on December 11, 2024, is an AI assistant designed to simplify the complexities of tasks related to deep research. It automates the entire process by conducting web searches, analyzing data, and generating structured reports. It also provides direct source links, all in about five minutes.

What makes Gemini unique is its dynamic and iterative approach. Instead of just pulling static results, it refines its queries as it uncovers new insights. It begins by searching for general information but changes its focus as it gathers more details. This process repeats until it creates a clear and well-structured summary to be exported as a neatly formatted document.

Gemini can also help users discover valuable but often overlooked resources that standard searches might otherwise miss. If you need more details on a certain topic, you can simply ask a follow-up question, and Gemini can refine the report in real-time. 

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Perplexity’s Deep Research model

Launched on February 14th, 2025, Perplexity’s Deep Research mode takes question-answering to the next level. It conducts multiple searches, analyzes hundreds of sources, and applies advanced reasoning to deliver expert-level insights, all in just a few minutes.

This tool saves time by handling complex topics that would otherwise require hours of manual research. Its approach is smart and adaptive: it searches the web, reads documents, and refines its strategy as it gathers more information. The result can be a clear, detailed report that you can export as a PDF or a document or share as a Perplexity Page.

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What sets apart each Deep Research AI model?

What truly sets these models apart is their intelligent research approach. Each uses advanced techniques to deliver high-quality answers efficiently. 

Here’s a quick glimpse at how they work:

• OpenAI’s Deep Research model: It was trained end-to-end with reinforcement learning on challenging browsing and reasoning tasks, enabling it to plan multi-step search trajectories to locate and verify data. It adapts in real time by backtracking and adjusting its strategy based on newly discovered information.

• Google’s Gemini Deep Research model: It creates a multi‐step research plan and iteratively browses and refines its web searches to gather, verify, and synthesize relevant data. It continuously adjusts its approach based on new information.

• Perplexity’s Deep Research model: It iteratively generates and refines a research plan, searching, reading, and reasoning over hundreds of sources to build a deep understanding of a topic. 

Despite having different processes running under the hood of these models, they share many features. They can all analyze data, identify key patterns, and generate structured reports, presenting insights in a clear and readable format. Similarly, they can use visual aids such as charts and graphs to make information easier to interpret. Also, they support built-in citation management that ensures transparency. 

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The impact of the Deep Research models 

Deep Research models have the potential to redefine how we work by handling complex research tasks with speed and accuracy. They can analyze massive amounts of information in minutes, delivering structured insights that save time across industries. 

By identifying hidden patterns and generating precise observations, these models can help organizations optimize operations, anticipate trends, and make smarter decisions. Beyond large businesses, they make expert-level research accessible to students, small companies, and individuals, enabling informed choices without specialized expertise. 

Real-world applications across industries

Here are some real-world applications of Deep Research models:

• Investment and financial analysis: They can be used to create an in-depth review of market data, financial reports, and news trends to help investors and analysts identify lucrative opportunities and risks.

• Scientific research acceleration: Researchers in fields like medicine can use these models to study data and explore new breakthroughs. For example, they can scan thousands of research papers to identify potential treatments.
• Product development insights: These models can help review customer feedback, market trends, and competitive data to inform product innovation and strategic planning.

• Supporting policy decisions: Governments and research organizations can use these models to analyze global issues and assist in creating more impactful policies and regulations. 

• Automated legal research: These models can quickly analyze vast databases of case law, statutes, and legal opinions to identify relevant precedents and insights. 

Comparing the Deep Research models

Each of the Deep Research models comes with its own strengths and limitations. For instance, OpenAI’s Deep Research model achieves 26.6% accuracy in the Humanity’s Last Exam benchmark, though it’s restricted to Pro users.

Meanwhile, Perplexity’s Deep Research model offers a user-friendly interface with free daily queries, reaching 21.1% accuracy. At the same time, Gemini’s Deep Research model is a faster AI assistant, but it achieves a lower accuracy of 6.2% and requires a paid Gemini Advanced subscription.

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Pros and cons of leveraging Deep Research models

Now that we’ve seen how these models can drive insights across industries, let’s take a quick look at their advantages:

• Scalability: These models can adapt to various research needs, from quick information retrieval to in-depth analysis. They handle both small-scale queries and large-scale projects across industries. 

• Cost savings: Automating complex research processes reduces the need for manual work, cutting labor costs significantly. Organizations can redirect these savings toward innovation, improving overall productivity.

• Trend anticipation:  These models can analyze vast amounts of data to identify emerging trends before they become mainstream. By detecting patterns and shifts early, they help users make informed decisions.

While these models offer many advantages, they also come with certain challenges to keep in mind:

• Context overload: These models can sometimes overanalyze, fixating on minor details and producing lengthy reports. Users may need to refine the output to extract the most relevant insights.

• Ethical dilemmas: The Deep Research AI models might pull information from copyrighted content. This can lead to potential legal issues. Businesses can carefully review outputs to ensure compliance.

• Skill dependency: Getting the best results requires AI literacy. Unclear prompts lead to vague answers. Users who don’t have experience crafting precise queries may struggle to maximize the model’s potential.

Key takeaways

Deep Research models are still in their early stages. While they offer quick access to well-researched answers, these answers aren’t always reliable. These models can sometimes misinterpret data, mix credible sources with rumors, or fail to highlight uncertainties. However, with continued advancements, they have the potential to become reliable research tools.

For quick answers, simpler models like GPT-4o work well and may be more cost-effective. However, as AI continues to improve, we can expect these Deep Research models to evolve and offer even more accurate daily insights. 

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