What is AI-powered research?

AI-powered research tools transform how scientists conduct research, making it faster and more efficient. Deep research tools, in particular, have the potential to impact the scientific community significantly. They can help speed up the process, but users should be careful about mistakes before publishing that information. Industry reports and studies have shown that AI tools can be highly effective in certain areas, such as data analysis and literature reviews. These tools use capable AI models to synthesize information from multiple sources, providing key findings and insights. These models use reasoning models and generative AI to synthesize information and provide insights. They can also respond to complex topics and provide detailed answers. Pro users can leverage AI tools to gain a competitive edge in their research. Like Deep Research, new models and technologies, such as AI Python tools and text-only subsets, are emerging, and the integration of all these tools will increase the scope and reliability of Deep Research.

Can AI tools make literature reviews?

AI tools can assist with various aspects of literature reviews, including identifying relevant papers, summarizing key findings, and organizing research themes. These tools can process large volumes of academic literature quickly and help researchers identify gaps or patterns across studies. However, AI cannot fully replace human judgment in evaluating source quality, synthesizing complex arguments, or providing critical analysis. Researchers must still review, verify, and interpret AI-generated content to ensure accuracy and maintain academic rigor in their literature reviews.

Can AI tools help with data analysis and statistical work?

AI tools can assist with data analysis and statistical work by cleaning datasets, performing statistical tests, creating visualizations, and identifying patterns in large datasets. These tools can suggest appropriate statistical methods based on data type and research questions. However, researchers must understand their data context and validate results, as AI may miss domain-specific nuances or make inappropriate assumptions.

Are technical skills required to use AI research tools effectively?

Most modern AI research tools use natural language interfaces that do not require programming skills. However, basic data literacy and understanding of fundamental research concepts help users formulate better queries and interpret results more effectively. Advanced applications may benefit from technical knowledge for custom analysis or specialized workflows.

How do I verify and fact-check AI research outputs?

Researchers should cross-reference AI outputs with original sources and peer-reviewed literature. Citations and references provided by AI require verification, as they may be inaccurate or fabricated. Key findings should be confirmed using multiple sources, with particular caution for recent developments or niche topics. Statistical analyses benefit from validation through multiple tools, and subject matter experts should review complex outputs when possible.

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AI Deep Research: Claude vs ChatGPT vs Grok

Cem Dilmegani

updated on Aug 23, 2025

See our ethical norms

AI deep research is a feature on some LLMs that offers users a wider range of searches than AI search engines.

We tested the following tools with two tasks and evaluated them across 5 dimensions:

Kimi Research
Claude Research
Bright Data Deep Lookup*
ChatGPT Deep Research with 03
Grok 3 Deep Search
ChatGPT Deep Research with o1
Perplexity Deep Research

Results

We evaluated them in terms of accuracy and the number of sources. Check out the methodology to see how we evaluated these solutions.

Gemini leads in the accuracy of the data provided:

Claude is the leader based on the number of indexed sources:

AI deep research benchmark results based on the number of indexed sources

Task 1:

We asked them to create tables about enterprise password management software per our prompt. The whole prompt can be found here.

Nearly all tools provided detailed tables containing the requested information, though their approaches to data presentation varied significantly.

For comprehensive report generation:

Gemini and Claude emerged as the leading solutions, delivering extensive analytical reports with synthesized insights and contextual analysis.
In contrast, Bright Data Deep Lookup* focused primarily on data extraction, providing structured tables with limited narrative content.

Researchers should select tools based on their specific research needs. Those requiring comprehensive analysis and report-focused solutions will find Gemini and Claude most suitable, as these tools are more focused on synthesizing information into detailed reports.

Conversely, researchers prioritizing raw data collection and requiring large-scale web searches will benefit more from Bright Data, which provides extensive web data coverage with confidence levels and detailed explanations of source relevance and reliability.

This data-centric approach makes Bright Data valuable for systematic reviews requiring high-volume source verification.

Kimi employs a distinctive methodology for report generation, producing an interactive report that incorporates executive summaries, targeted “best for” sections, and strategic recommendations.

The report features integrated data visualizations and source attribution, resulting in a complete deliverable suitable for immediate implementation without further modification.

Note: Perplexity provided a detailed report but failed to create a table with its gathered information. Since our prompt specifically requested table outputs, it received zero points for that task.

*We will update Bright Data Deep Lookup when the product leaves the beta stage.

Task 2:

The goal of this task is to evaluate their speed and coverage in research. We requested a detailed report on RPA adoption to determine the number of indexed pages and the time it takes to generate a report.

Of course, the number of sources does not have to correlate with the quality of the research. However, since these tools are designed to speed up research, we considered it an important metric.

We should also note that search times vary significantly across these tools. Grok Deep Search is approximately 10 times faster than ChatGPT Deep Research and searches approximately 3 times more webpages.

Claude Deep Search is also highly responsive, having researched 261 sources in over 6 minutes. However, Gemini may not be an ideal choice for those seeking a fast and responsive solution, as it researched 62 sources in over 15 minutes..

Methodology

Every data in the prompt scored as 1 point. If the output was not in table format, we rated it as 0.

Prompt of the Task 1

Research and evaluate the top 5 enterprise password management solutions based on the following criteria to identify the most effective solution for enterprise deployment.

Criteria

1. Security Features

Encryption standard used
Zero-knowledge architecture implementation
MFA options supported
Third-party security certifications
Password health monitoring features

2. Deployment & Integration

Deployment options
Directory integration capabilities
API availability and functionality
SSO integration

3. User Experience

Browser extension compatibility
Mobile app availability and rating
Offline access capabilities
Password sharing functionality

4. Administration

Password policy enforcement options
User provisioning/deprovisioning automation
Reporting and compliance features
Emergency access protocols

5. Cost & Scalability

Compare pricing using standardized enterprise scenarios (100 users, 500 users, 1000+ users)

Delivery Format

Detailed table for each criterion
Cost comparison table with standardized scenarios

Prompt for Task 2

In our second task, we aimed to discover the scope of the research conducted. To do this, we compared the number of references cited. Comparing articles is not an objective method in this case, as establishing a definitive ground truth is not feasible.

However, the number of references can give us an idea about their ability to provide information, since the strength of these tools is their ability to index hundreds of web pages in minutes.

Benefits of AI deep research tools

Enhanced efficiency and productivity

Literature reviews: AI research tools act as a research assistant, performing a deep literature search on vast databases of scientific papers. They identify relevant papers and can synthesize information to generate concise summaries, significantly reducing the time and effort needed for a manual literature review.
Data collection and analysis: An AI research assistant can automate data collection by mining large databases and web pages. These tools possess deep research capabilities that allow them to process and analyze massive datasets far faster than traditional methods. They can identify patterns and trends that might be missed by manual review, which is crucial for complex research tasks like market analysis or creating a deep research report.
Automation of repetitive tasks: AI can handle repetitive tasks like data entry and formatting source citations. By automating these time-consuming processes, researchers can focus on more complex topics and the creative aspects of their work.

Deeper insights and discovery

Identifying research gaps: By analyzing existing academic literature, AI tools can help researchers pinpoint gaps in current knowledge. This is a critical step for formulating a new research question or developing a multi-step research plan. These tools provide easy-to-read insights in a structured, neatly organized format.
Synthesizing information: AI research assistants can synthesize information from multiple sources, generating a comprehensive report and highlighting key findings. This gives researchers a broad overview without needing to read every single paper in full, which saves time while still providing comprehensive insights.
- For example, Claude’s deep research tool generated a detailed report. The report can be published as an Artifact, which is accessible online and can be visible on search engines.
Exploring connections: Tools that visualize citation networks can help researchers see how different scientific papers are interconnected. This can lead to discoveries and a more comprehensive understanding of a research field.

For example, Grok indexed more than 100 different pages in our second task. Normally, it takes hours for a human to read and gather information from all these pages, but it took ∼2 minutes for Grok.

Therefore, these tools can speed up the research process. However, users should always remember that these tools can hallucinate and generate wrong information, so be cautious when using information directly taken from an LLM.

Challenges and limitations of AI deep research tools

Accuracy and reliability

Most people are suspicious of the accuracy of LLM-generated information and double-check it themselves because they know that LLMs can hallucinate. The issue with deep research is that, because it conducts more comprehensive research than standard chat and provides sources, users may mistakenly assume it always provides accurate information. LLMs (even with deep research) still tend to hallucinate, and this may result in serious misunderstandings.

Lack of context and nuance: An AI research assistant may struggle to grasp the full context of a research task, potentially summarizing information without understanding its deeper significance. This can lead to incomplete or incorrect conclusions.
Outdated information: The training data for some AI models may not be current, causing them to miss recent developments in scientific papers or other academic literature.
Source credibility: AI tools often struggle to differentiate between authoritative and unreliable sources, treating all information from the open web as equally valid. Human judgment is essential to vet the credibility of sources for a deep research report.

Bias and ethical concerns

Algorithmic bias: If the datasets used to train AI models contain societal biases, the AI will learn and perpetuate them. This can result in outputs that are biased against specific demographics, impacting the integrity of deep research.
Data privacy: The use of AI tools involves processing large amounts of data, which raises significant privacy and security concerns. Proprietary or confidential data entered by a researcher could be used to train future models, leading to a risk of data leakage.
Ownership and copyright: When an AI tool synthesizes information from multiple sources, questions arise regarding intellectual property and proper attribution. It is often challenging to determine ownership of the final output and ensure all source citations are correct.

Human skill and over-reliance

The illusion of expertise: AI tools can produce a polished, structured report, creating the false impression of a comprehensive, expert analysis. The tool is a research assistant, not a replacement for the judgment, expertise, and scrutiny that a human researcher provides to complex research tasks. This is especially relevant for decision makers facing high-stakes decisions.
Erosion of critical thinking: An over-reliance on AI research tools may diminish a researcher’s critical thinking and analytical skills. Providing all the answers can reduce the user’s engagement in the complex research processes essential for high-quality academic papers.
Steep learning curve: Despite their user-friendly design, many research tools have a slight learning curve, particularly for their advanced features. Researchers may need to invest time to leverage the tool’s deep research capabilities fully.

Gary Marcus also warned that it can cause a decline in the quality of scientific papers.¹

FAQs

Reference Links

Deep Research, Deep Bullshit, and the potential (model) collapse of science

Marcus on AI

Principal Analyst

Cem Dilmegani

Principal Analyst

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Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 55% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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