By Mike Levin

How Social Media Disrupted Search But The Address Bar Saved It

Search goes hand-in-hand with the Internet, making it usable, and it quickly disrupted directories like Yahoo and Yellow Pages (and Zip2, Elon could have been Google). Google disrupted them all with web search. But then social media disrupted search with vegetative doom-scrolling – the Internet version of couch potato. Search suffered but hung on because the browser address bar triggers it.

That’s right. Search was already disrupted by social media but because the Web browser address bar became the combined address + search bar, briefly called the omni bar, search hung on. I mean, it hung on as a multi-billion dollar business because of Google Ads. It’s way more expensive now to crawl, index and serve of the massive Net and it needs ad revenue to pay for that infrastructure. Plain and simple, right?

The Evolution of Web Search Infrastructure and Costs

This may not have been true when the Web was once a small, mostly static thing. But today when the state of the Net changes at the rate of news and product inventory, that’s no longer true. The value proposition of search has changed. And the price of users tolerating the web address bar searching is that the search lives up to expectations as a good enough consolation prize for a bad URL entered – a profoundly intrusive and privacy-violating act were the value proposition not so excellent!

The Decline of Traditional Web Search

Few people think of it that way but Google, displaced by social media as they were, hung on in the edges amongst the lazy who use search really just for web-navigation and not real research. Hard core searchers like me who have to get shit done using new knowledge they didn’t have just a moment ago were out in the cold, in a wilderness of ads.

The Fragmentation of Research Tools

Sure, we could go directly to Reddit, perform direct searches on GitHub and StackOverflow. We could switch Google to academic-mode. There is the verbatim search tool still hiding in Google, and more recently the Web option hidden under “…More”. I mean think about that! Google can still perform a Web search, but you have to go looking for it! Hahaha. And if push comes to shove, you could reach out on Twitter or join the appropriate Discord. Point being, actual research became a hodgepodge of nothing great…

The Limitations of 1st Generation AI Search Solutions

…especially now in the changing landscape brought about so suddenly by ChatGPT, Bing and Perplexity – who all push the chat interface on us as Google-disrupting general search. We intuitively feel LLMs should be a game-changer in search, but all these services’ 1-web-search citations powered by Microsoft’s Bing API that get layered in as footnotes are not enough.

And Google’s AI Overviews? Everything about them is designed to not cut into Google Ads revenue. Classic innovators dilemma. Nothing about the AI-enhanced search experience lives up to the advancements that our guts tell us should be there.

We know that it has to… it has to… it has to… go deeper! More than 1 search. It needs to re-search!

The Rise of Deep Research in AI Search

And so we waited for disruptors, which arrived in the form of Deep Research. The concept implicitly promises an ad-free experience and multiple layers of iterative searching - searching, and searching again. In other words, true research! Both Google’s Gemini 1.5, where the feature first appeared, and ChatGPT’s Pro product have branded this capability as Deep Research. Google opts for the more formal Research while ChatGPT uses the understated research.

The Cost and Access Models of AI Search Services

Also, you have to be logged in on Google to use the feature, which I believe they started making available to non-paying logged in products. Remember, if you’re not paying for the product, you are the product – specifically and especially when advertisers are in the picture! So while performing research might be free, the topics you’re researching are almost certainly becoming part of your profile or dossier for advertisers.

Think about that for a moment. Your research is being paid-for by ad subsidies, which is how Google can deliver you such services for free. And the results of that research (search results) can no longer have embedded ads (like normal search results) because that would just be ridiculed to death. However, the rub is that you are being researched by advertisers right as you research!

I’ve been using Gemini AI right from the start because it is included in my $100/year GoogleOne plan that I have for more Google Photos so when research first showed up, I was all over it at Moz with Bard before that. However, I’m guessing that even being a customer for Google’s paid services doesn’t get us off the hook from their ad-delivery profiling systems.

The Economics of Deep Research Features

Even logged in on ChatGPT but non-paying, you don’t get Deep research. And why would you? If a search costs X, and a search plus LLM-chatbot costs X*10, then re-search, re-re-search and re-re-re-search costs anywhere from X*100 to X*10,000, depending on the number of re’s. In other words, how long will the LLM go until it’s satisfied with the answer it’s going to pass along to the user? OpenAI’s ChatGPT runs on Microsoft’s Azure cloud infrastructure, and Microsoft ain’t giving that away to a “partner”.

Grok’s Infrastructure Advantage

Grok3 on the other hand runs on Mr. Musk’s own private colossal xAI datacenter in Memphis Tennessee, appropriately codenamed Colossus. If Elon wants to give you a search that goes on for 20-minutes or 20-hours, hey that’s his to give away. There’s no Microsoft slap-down on resources allocated to a user-request of a second-tier $20/mo ChatGPT Pro paying customer. In fact, it’s in Microsoft’s interest to cap-off OpenAI resource allocation, while it’s in Musk’s interest to let Grok3 grok away on your question, grokking every last bit it can grok… all at his expense, but to differentiate his services.

The Deep History of “Grok”

I know this is difficult to grok, but grokking ain’t easy. Even the term grok itself is not at all what it’s dumbed-down to for the public. Amongst us Sci-Fi nerds, Grok is a Martian word meaning to understand something so deeply and intuitively that you have internalized everything about it. And I really mean internalized – consumed its essence – merging with it on a spiritual level.

I.e. cannibalism. In this profoundly influential Sci-Fi classic, to grok someone fully is to consume the soup made from them after their passing. Robert Heinlein’s 1961 Stranger in a Strange Land is every bit as lightheartedly gory as the Sweeney Todd: The Demon Barber of Fleet Street musical.

The Cultural Impact of Grok

We forget, but the Charles Manson family was influenced by the concept of grok, reframing it to support his ideas of total unity, control, and a distorted sense of enlightenment within his death-cult. The word itself is powerful, both a noun and a verb. It rolls off the tongue more smoothly to grok something than it does to google it – by precisely 1-syllable. And grokking is so much less of a baby-word than goo-goo, gah-gah googling.

Branding Wars in AI Search

Also, Grok 3 uses the term Deep Search instead of Deep Research like Google or Deep research like ChatGPT. You can see an attempted avoidance of overlapping branding. Least we forget the underdog and uncredited original: Perplexity! I’m not on their $20/mo Perplexity Pro program so I never really knew much about their service, but when Elon Musk started giving away Deep search (because he could), it kicked off the competition, and you now get 3 “enhanced searches” per-day from Perplexity – the limit validating how expensive I’m guessing such services are (normal search x 1000, at least).

Perplexity labels theirs Deep Research, In-depth reports on complex topics. I’ve used up my 3 genie wishes for today, and it’s only 6:30 AM, haha! So to carry through on this super-prompt, I’m going to have to use a different login with Perplexity.

Setting Up The Research Challenge

Super-prompt, you ask? Why yes, of course. You didn’t think I was merely writing an article, did you? No my friend, this is just the deep breath and warm-up for the… you guessed it! Did you? This whole article is a deep research prompt.

It’s the background. It’s the context I’m giving each AI to perform the research I’m asking for. This is no problem, given the 128K token context window these things generally have nowadays. That can all be research background! Fleshing out the story. Laying the foundation for whatever LLM I feed this to.

Which LLMs?

Google Gemini Deep Research doesn’t put a Gemini version number on it anymore. They don’t want you to know it’s 1.5 handling Deep Research while they have 2.5 Experimental out there. The number-difference is too big for credibility, but the model is probably plenty able for research. And I have access to it under the $100/yr GoogleOne service.

I’ll use Grok 3 under the free tier becuse I’m not even paying for the $32/yr X.com subscription for the blue checkmark, and certainly not the $300/yr Super-Grok membership unless and until it knocks my socks off.

I do have access to the $200/mo ChatGPT Pro plan (woot!). And I’ll be using one of the 3/day Perplexity free account genie wishes.

Anyway, whichever of you I’m talking to right now, the research request is this.

1. Identify the “branding” used for each service’s enhanced searching feature frequently called some variation of “deep research”. Deep Search vs. Deep Research vs Deep research. Others? Which service uses what?

2. Identify the different service tier-levels. Is there a deep research and deeper research? Number of enhanced searches/day? In other words, what differentiates the free level from the potentially various paid levels?

3. Identify other factors that differentiate each service. For example, Grok has access to the Twitter feed while the others don’t. Services like Reddit and Medium.com where answers often come from are increasingly pay-walled. How do they deal with it? Google has their own copy of the Internet essentially. How long are they willing to go? How do they format their reports? Do they provide markdown? What about their citation system?

There is a pretty exhaustive list of things that could be examined and compared about each service, especially in point #3. Part of the request is to identify which are most important either reported by users, touted by each service or inferred by your great intelligence! In the course of performing this research, ask yourself what would have made the experience better for you.

Certainly, you have some form of web browser (perhaps several forms) that you can tap in the process, and the overall process can’t go any faster than the speed of the Internet, latency and all, so don’t make speed benchmarking part of the comparison except insofar as edge cases where Google’s infrastructure with a copy of the Internet might provide a competitive advantage (only insofar as it actually does).

If you have clarifying questions, both note that in your comparison of the service, and go ahead and ask!

                                  ### ---

I take a few screenshots here and there as it goes. I watch the thinking, but I do not try to capture the actual thought process. They don’t make that easy. There is no formatted markdown on the copy/paste as there is with the finished reports.

Perplexity

When I paste the article into Perplexity, it becomes an attachment, one of the 10 allowed for me per day. I switch the dropdown menu to Deep Research, and…

Me: Perform the research requested in the attachment.

I see it begin to think. I can see paragraphs pop up in sequence, replacing each other. I can click a “Steps” button that opens a pane over to the right. I see it do its thing. Great source favicons are shown as it goes. It counts the number of sources it found. It writes the research report. I copy the markdown. I only had to indent citations and enter 1 blank line above them to avoid wild markdown text-wrapping of the citations. Here it is:

Perplexity:

Comparing Deep Research Features Across AI Platforms: Grok vs. ChatGPT vs. Gemini vs. Perplexity

The landscape of AI-powered search has evolved significantly from simple keyword queries to sophisticated research capabilities that can analyze, synthesize, and present complex information. This report examines the emerging “deep research” features across major AI platforms, comparing their branding, access models, and distinctive capabilities.

The Evolution of Research-Oriented AI Search

Web search has undergone substantial transformation since its inception. While Google revolutionized web navigation with its search engine, social media eventually disrupted traditional search behaviors by promoting passive content consumption over active information seeking. Despite this shift, search functionality persisted largely due to its integration into browser address bars[1]. However, serious researchers found themselves navigating an increasingly fragmented landscape of specialized search tools and ad-laden results.

The emergence of conversational AI models presented an opportunity to transform research. Rather than simply providing links to information, these systems can now independently conduct multi-step research processes, analyzing diverse sources and synthesizing findings into comprehensive reports. This capability—variously called “deep research” or “deep search”—represents a significant advancement in AI-assisted information gathering and analysis.

From Simple Search to Intelligent Research

Traditional search engines like Google became less effective for in-depth research over time, requiring users to employ advanced techniques and navigate multiple platforms to find quality information. The introduction of large language models (LLMs) initially promised to revolutionize information retrieval, but first-generation implementations still relied primarily on single web searches with limited citation capabilities[1].

The latest evolution—deep research functionality—addresses these limitations by enabling AI systems to conduct iterative searches, analyze findings, and produce comprehensive reports with proper attribution. As the search result states: “We know that it has to… it has to… it has to… go deeper! More than 1 search. It needs to re-search!”[1]

Branding and Terminology Across Platforms

Each major AI platform has adopted slightly different branding for their enhanced research capabilities:

Grok’s “Deep Search”

Elon Musk’s xAI platform Grok 3 uses the term “Deep Search” (with capital ‘S’)[1]. This is accessed through an “Advanced deep search and reason” button in the interface[2]. The branding aligns with the platform’s name itself, which references Robert Heinlein’s concept of “grok” meaning to understand something so deeply that you merge with it on a spiritual level[1].

ChatGPT’s “deep research”

OpenAI officially brands their feature as “deep research” (lowercase), describing it as “an agent that uses reasoning to synthesize large amounts of online information and complete multi-step research tasks”[3]. This terminology emphasizes the depth and comprehensive nature of the research process rather than just the search component.

Google Gemini’s “Deep Research”

Google employs the term “Deep Research” (with capital ‘R’) for their enhanced search capability in Gemini 1.5[1]. This consistent capitalization pattern reflects Google’s formal approach to product naming.

Perplexity’s “Deep Research”

Perplexity, often considered the original pioneer in this space, labels their feature “Deep Research” with the descriptive tagline “In-depth reports on complex topics”[1]. This branding directly communicates the value proposition to users.

Access Models and Pricing Tiers

The computational resources required for deep research capabilities are substantial, resulting in varied access models across platforms:

Pricing Strategy Differentiation

While a standard search might cost X in computational resources, and a search plus LLM processing might cost X10, the multiple iterations required for deep research can cost anywhere from X100 to X*10,000, depending on the depth of analysis[1]. This cost structure necessitates various pricing strategies:

• Grok: Offers Deep Search capabilities on its free tier, leveraging Elon Musk’s ownership of xAI’s private datacenter (“Colossus”) in Memphis, Tennessee. This infrastructure advantage allows Grok to offer resource-intensive features without direct incremental costs[1]. Premium tiers include a $32/year X.com subscription and a $300/year “Super-Grok” membership[1].

• ChatGPT: Initially limited deep research to Pro users ($20/month), later expanding to Plus tier users. The feature became available to Pro users on February 2, 2025, and to Plus users on February 25, 2025[3]. The resource limitations may be influenced by ChatGPT’s reliance on Microsoft’s Azure infrastructure[1].

• Google Gemini: Deep Research appears to be available to non-paying logged-in users, though it’s also included in the $100/year GoogleOne subscription that offers enhanced Google Photos and other services[1].

• Perplexity: Provides 3 “enhanced searches” per day on their free tier, with unlimited access for Pro subscribers ($20/month)[1].

Technical Infrastructure and Processing Capabilities

The underlying infrastructure significantly influences each platform’s performance and limitations:

Computing Resources and Processing Depth

• Grok: Benefits from dedicated infrastructure at xAI’s “Colossus” datacenter, potentially allowing for longer processing times without the constraints that third-party cloud providers might impose. This gives Elon Musk the flexibility to “let Grok3 grok away on your question, grokking every last bit it can grok… all at his expense, but to differentiate his services”[1].

• ChatGPT: Operates on Microsoft Azure, which may introduce resource allocation limits even for paying customers. OpenAI’s deep research is powered by “a version of the upcoming OpenAI o3 model that’s optimized for web browsing and data analysis”[3].

• Google: Leverages its extensive search infrastructure, including what is essentially “their own copy of the Internet”[1], potentially providing advantages in access speed and comprehensiveness.

Processing Time Expectations

Processing times vary by platform and query complexity. For Grok, the Deep Search process “might take a couple of seconds [or] up to a minute depending on what you’re trying to search for”[2]. One example in the search results took 51 seconds to complete[2].

Feature Differentiation and Capabilities

Beyond basic infrastructure differences, each platform offers unique capabilities:

Data Access and Source Integration

• Grok: Has exclusive access to Twitter/X data, providing a potential advantage for real-time information and social media insights[1].

• Google: Benefits from its extensive web crawling infrastructure and potential access to paywalled scholarly content through Google Scholar.

Documentation and Citation Systems

OpenAI’s deep research provides “fully documented” outputs “with clear citations and a summary of its thinking, making it easy to reference and verify the information”[3]. This approach emphasizes transparency and enables users to verify sources independently.

User Experience and Interaction Models

Grok’s interface includes a visual indicator of the deep thinking process, showing users that research is actively being conducted[2]. This transparency helps set appropriate expectations for processing time.

Research Process Demonstration

When using Grok’s Deep Search feature, the process unfolds as follows:

1. The user enables Deep Search by pressing the “Advanced deep search and reason” button
2. The user enters their query (e.g., “what is the difference between grook and GPT”)
3. The system initiates a “deep think” process, visibly researching and preparing information
4. After completion (e.g., 51 seconds in the example), Grok presents comprehensive results with evaluations of different aspects of the topic[2]

This process demonstrates how these systems differ from traditional search engines by actively processing and synthesizing information rather than simply returning links.

Conclusion

The emergence of deep research capabilities across major AI platforms represents a significant evolution in how we access and process information. Each platform has adopted slightly different approaches to branding, pricing, and technical implementation, reflecting their broader strategic positioning.

Grok leverages its dedicated infrastructure to offer generous resource allocation even to free users, while ChatGPT emphasizes comprehensive documentation and verification capabilities. Google benefits from its extensive search infrastructure, and Perplexity pioneered the space with a focus on in-depth reporting.

As these capabilities continue to evolve, we can expect further differentiation in features, pricing models, and use cases. The ability to conduct comprehensive, multi-step research without human intervention represents a significant step toward more capable AI assistants and potentially toward artificial general intelligence that can independently generate new knowledge.

Citations:

[1] https://ppl-ai-file-upload.s3.amazonaws.com/web/direct-files/11792699/c5563968-2638-4372-ad0c-88beedd753b7/paste.txt
[2] https://www.youtube.com/watch?v=OvnMiVgnXxY
[3] https://openai.com/index/introducing-deep-research/
[4] https://gemini.google/overview/deep-research/
[5] https://www.pageon.ai/blog/perplexity-ai-search-engine
[6] https://www.align.vn/blog/perplexity-deep-research/
[7] https://team-gpt.com/blog/gemini-pricing/
[8] https://x.ai/blog/grok-3
[9] https://en.wikipedia.org/wiki/ChatGPT_Deep_Research
[10] https://blog.google/products/gemini/google-gemini-deep-research/
[11] https://www.perplexity.ai/hub/blog/pro-search-upgraded-for-more-advanced-problem-solving
[12] https://www.zdnet.com/article/what-is-perplexity-deep-research-and-how-do-you-use-it/
[13] https://9meters.com/technology/ai/google-gemini-costs
[14] https://www.infoq.com/news/2025/02/perplexity-deep-research/
[15] https://ai.google.dev/gemini-api/docs/pricing
[16] https://www.usaii.org/ai-insights/what-is-perplexity-deep-research-a-detailed-overview
[17] https://www.uctoday.com/collaboration/gemini-pricing-everything-youll-pay-for-google-gemini/
[18] https://techcrunch.com/2025/02/15/perplexity-launches-its-own-freemium-deep-research-product/
[19] https://cloud.google.com/vertex-ai/generative-ai/pricing
[20] https://www.perplexity.ai/hub/blog/introducing-perplexity-deep-research
[21] https://www.perplexity.ai/help-center/en/articles/10738684-what-is-deep-research
[22] https://www.linkedin.com/pulse/grok-deepsearch-vs-chatgpt-deep-research-which-ai-agent-shanee-moret-ducac
[23] https://www.techradar.com/computing/artificial-intelligence/i-tried-deep-research-on-chatgpt-and-its-like-a-super-smart-but-slightly-absent-minded-librarian-from-a-childrens-book
[24] https://gemini.google/advanced/
[25] https://www.youtube.com/watch?v=bOHfJZ4DVqE
[26] https://x.ai/news/grok-3
[27] https://www.axios.com/2025/02/07/chatgpt-deep-research-gemini-review
[28] https://blog.google/products/gemini/tips-how-to-use-deep-research/
[29] https://www.nvidia.com/en-us/case-studies/perplexity-enhances-model-performance-with-nemo/
[30] https://www.reddit.com/r/ChatGPT/comments/1j2k59m/hows_grok_3_deepsearch_compared_to_chatgpt_deep/
[31] https://help.openai.com/en/articles/10500283-deep-research-faq
[32] https://support.google.com/gemini/answer/15719111
[33] https://www.perplexity.ai/hub/faq/what-is-pro-search
[34] https://www.reddit.com/r/perplexity_ai/comments/1ipgbib/introducing_perplexity_deep_research_deep/
[35] https://www.youtube.com/watch?v=h-trO3HJCIo
[36] https://www.youreverydayai.com/perplexity-deep-research-what-it-is-and-if-you-should-use-it/
[37] https://cloud.google.com/products/gemini/pricing
[38] https://leonfurze.com/2025/02/15/hands-on-with-deep-research/
[39] https://one.google.com/about/ai-premium/
[40] https://blog.google/products/gemini/new-gemini-app-features-march-2025/

Gemini Advanced Deep Research

Me: [Pastes article]

Google presents a plan. I click “Start Research”.

Gemini: Great, I’m on it. I’ll let you know when the research is finished. You can leave this chat in the meantime.

Interface pops full-screen, multi-pane. Thought process in right-pane with lots of favicons. Sources being counted, being called “websites”.

Gemini:

Here’s a surprise. I try “Copy Contents” next to “Export to Docs” and it just gives me a not-markdown jumpled mess. 11 really long lines with all the words appended together where line-breaks would be. Useless! So I select Export to Docs and save the markdown. Google forces you to use their Docs product to get the clean markdown! But here it is.

Filename: Deep Research AI Comparison_.md

Deep Dive into AI-Powered Research: A Comparative Analysis of Grok, ChatGPT, Gemini, and Perplexity

1. Executive Summary:

The landscape of web search and information retrieval is undergoing a significant transformation with the advent of sophisticated AI models offering enhanced research capabilities. This report provides a comparative analysis of four prominent platforms – Grok, ChatGPT, Gemini, and Perplexity – focusing on their features designed for in-depth research. Each platform presents a unique approach, leveraging large language models to synthesize information from the web and other sources. Grok, with its integration into the X platform, offers real-time insights from social media. ChatGPT, known for its versatility, provides multimodal analysis and a range of underlying models. Gemini, backed by Google’s vast resources, emphasizes integration with its ecosystem and user-controlled research plans. Perplexity distinguishes itself with its speed and direct, in-text citations. While each platform demonstrates strengths in certain areas, their weaknesses and unique characteristics make them suitable for different research needs. Given the rapid advancements in this field, the features, pricing models, and overall capabilities of these AI tools are subject to change, underscoring the dynamic nature of the AI-powered research market. The competition among these platforms is intense, with each striving to offer a compelling solution for users seeking more than traditional search engines can provide.

2. Deep Research Feature Branding:

The terminology used by each platform to brand its enhanced search feature plays a crucial role in user understanding and adoption. Grok consistently refers to its advanced search functionality as DeepSearch 1. This consistent branding across its website and documentation helps users readily identify and understand this specific capability. ChatGPT’s branding appears to be more nuanced. While the general ability to search the web is labeled as “Search the web” 3, the more advanced, multi-step research capability is frequently referred to as “Deep Research” 4. This suggests an evolution in ChatGPT’s feature set, with “Deep Research” representing a more sophisticated offering, possibly tied to specific subscription tiers or underlying models. Gemini maintains a clear and consistent branding of its enhanced search feature as “Deep Research” 9. Similar to Grok, this uniformity aids user comprehension and feature recognition. Perplexity employs the term “Pro Search” 11 to denote its enhanced search capabilities, often associated with a limited number of daily queries on the free tier and a significantly higher number on paid plans. Additionally, the term “Deep Research” is also used to describe its in-depth analysis features 13, potentially creating some ambiguity regarding the distinction between “Pro Search” and “Deep Research.”

3. Service Tier Analysis and Deep Research Access:

The accessibility of deep research features varies significantly across the different service tiers offered by each platform. Grok’s DeepSearch is available to all X users, including those on the free tier, albeit with limitations on query frequency and other features 17. For unrestricted access to Grok-3’s advanced capabilities, users need to subscribe to X Premium+ ($50/month or $350/year) or SuperGrok ($30/month or $300/year), with SuperGrok explicitly including DeepSearch and enhanced reasoning modes 19. This model ties Grok’s AI features directly to the X platform’s subscription structure. ChatGPT’s Deep Research is primarily a feature for paid subscribers. While the free tier offers limited access to GPT-4o and web search, Deep Research is available on the Plus plan ($20/month) with a limit of 10 queries per month, and on the Pro plan ($200/month) with a significantly higher limit of 120 queries per month. Team, Edu, and Enterprise users also have access to this feature 3. This tiered approach makes Deep Research more accessible at a lower price point than initially offered. Gemini offers Deep Research on both its free and paid tiers. The free tier provides access to the 2.0 Flash model with limited Deep Research capabilities. The Gemini Advanced subscription ($19.99/month) offers expanded access to Deep Research and more advanced models like Gemini 2.5 Pro 9. Recent updates have also made some features previously exclusive to Gemini Advanced, including Deep Research, available to all users 10, suggesting a broader accessibility strategy. Perplexity provides Deep Research on its free tier with a daily limit of 5 queries for logged-in users 13. The Professional plan ($20/month or $200/year) significantly increases this limit to over 300 Pro Searches per day, which includes access to advanced AI models and unlimited file uploads 12. This freemium model allows users to experience the deep research capabilities with a free account while offering substantial enhancements for paid subscribers.

4. Differentiating Factors for Deep Research:

Each platform offers unique features that distinguish its deep research capabilities. Grok’s most significant differentiator is its real-time access to the Twitter (now X) feed 1. This allows Grok to incorporate the latest information, trends, and user sentiment directly from the platform into its research analysis 18. Furthermore, Grok emphasizes transparency by listing the sources it uses and even outlining its “thought process” in arriving at an answer 18. Its willingness to engage with a broader range of topics, including those considered controversial by some other AI models, also sets it apart 18. ChatGPT distinguishes itself through its ability to perform multimodal analysis, processing not only text but also images and PDF files 26. The availability of different underlying reasoning models, such as o3-mini, o1, and the research preview of GPT-4.5, provides users with options to tailor the AI’s approach to the complexity of the research task 3. Features like cross-domain synthesis, hypothesis testing, visualization, and smart citations further enhance its research capabilities 7. Gemini’s integration with the Google ecosystem is a key differentiating factor 27. Users can often review and even modify the research plan devised by Gemini before it executes the search 26. The platform also boasts a large context window, enabling it to process and analyze extensive documents 9. The seamless integration with Google Docs for report export and collaboration is another significant advantage 26. Perplexity stands out for its strong focus on providing direct, in-text citations for the information it presents 14. This emphasis on source attribution enhances transparency and allows users to easily verify the information. Perplexity is also known for its speed in generating research reports 14. Additionally, its capability to export results in both PDF and Markdown formats caters to different user needs for sharing and further processing 14.

5. Handling of Paywalled Content:

The approaches taken by these platforms regarding paywalled content vary considerably, reflecting different philosophies and potential legal considerations. Grok’s stance on paywalled content is not explicitly detailed in the provided information 1. It primarily relies on real-time web access and data from the X platform 18, suggesting it likely accesses publicly available information. ChatGPT generally adopts a cautious approach, with reports indicating that it explicitly refuses to summarize paywalled content 32. This stance aligns with a focus on respecting content protection and copyright. An incident involving a temporary pause of its Bing integration due to a paywall bypass further underscores OpenAI’s concern in this area 33. While users have discovered workarounds to potentially access paywalled content 34, these are not officially supported and may be subject to patching. Gemini’s Deep Research is reported to have no access to paywalled content 15. This limitation means that its research capabilities are confined to information freely available on the web. Perplexity’s handling of paywalled content has been a subject of controversy 32. Despite denying that it reads protected materials, Perplexity has faced accusations of scraping content without permission and paraphrasing paywalled articles 32. While the platform has initiated a revenue-sharing program with news organizations and claims to respect robots.txt protocols 36, the issue of how it generates detailed summaries of potentially paywalled content remains a point of discussion. Some users have noted that Perplexity might miss information behind paywalls 16, while others have raised concerns about potential copyright infringement 35.

6. Research Report Formatting and Citations:

The formatting of research reports and the inclusion of citations are critical aspects for users relying on these platforms for serious research. Grok is designed with Markdown support and aims to provide citations through inline URLs and a concluding list of key citations 18. However, some users have reported inconsistencies and loss of Markdown formatting when copying text 40. ChatGPT’s Deep Research can export findings in various formats, including Markdown 7. Citations are often provided as clickable links within the report 6. While it offers “Smart Citations” 7 and users have attempted to get it to output in specific styles like APA, the results have been inconsistent 6. Gemini supports exporting reports to Markdown and integrates seamlessly with Google Docs, preserving formatting and including links to original sources 26. The specific format of citations within Gemini reports requires further clarification, though links to sources are generally included 9. Perplexity is consistently highlighted for its clear, in-text citations, which allow users to easily trace the source of information 14. It also offers robust export options to both PDF and Markdown formats 14, providing flexibility for users to share and work with the research output.

7. User Experiences and Reviews of Deep Research:

User experiences with the deep research features of these platforms vary. Grok is often praised for its speed and well-formatted reports, particularly its concise overviews and use of bullet points 42. Users appreciate its real-time access to X data and its strong reasoning capabilities 18. However, some find it lacking in the depth of analysis compared to competitors and note potential issues with citation linking and Markdown output consistency 40. ChatGPT’s Deep Research receives mixed reviews. While some users laud its thoroughness, customization, and high-quality, well-formatted responses, often feeling like a human assistant 44, others find it slow, occasionally relying on less trustworthy sources, and sometimes providing excessive or unnecessary detail 8. The initial high cost of access was a concern, though the inclusion in the Plus tier has improved accessibility 50. Gemini is recognized for its speed and integration with the Google ecosystem, with some users finding its table breakdowns and export to Google Sheets particularly useful 27. The pre-research planning process is also seen as a positive aspect 26. However, some users report its output lacking depth, struggling with specialized topics, and sometimes providing generic or partially incorrect information 26. Concerns about the accuracy and quality of sources have also been raised 15. Perplexity generally receives positive feedback for its speed, ease of use, and strong emphasis on clear citations 16. Users find it effective for initial research, fact-checking, and academic/technical topics. The free tier with a limited number of Pro searches is valued 25. However, some users have reported issues with hallucinated sources and a lack of depth for very complex queries 52. Comparisons suggest it offers a good balance of speed and accuracy, though it might not always match the thoroughness of ChatGPT 24.

8. Comparative Analysis Table:

9. Conclusion and Recommendations:

The analysis reveals that each of these AI platforms offers distinct advantages and disadvantages for deep research. Grok, with its unique access to real-time X data, is well-suited for research requiring up-to-the-minute social media insights and trend analysis. Its transparency in showing its reasoning process can also be valuable for users seeking to understand how conclusions are reached. However, potential inconsistencies in formatting and depth compared to some competitors should be considered. ChatGPT stands out for its versatility, capable of handling various data types and offering different reasoning models. It is a strong contender for complex research tasks requiring multimodal analysis and in-depth exploration, though users should be mindful of potential biases in sources and the time it might take for report generation. Gemini’s tight integration with the Google ecosystem makes it a convenient choice for users already invested in these tools, offering a collaborative research experience with user-controlled plans. While its accessibility has improved, some users find its research output lacking in depth for specialized topics. Perplexity shines in providing quick, factual answers with clear and direct source citations, making it an excellent tool for users who prioritize transparency and speed in their research process. Its freemium model also makes it accessible to a wide range of users. However, the controversies surrounding its handling of paywalled content and potential for hallucinated sources warrant careful consideration.

For researchers requiring in-depth, multimodal analysis and a high degree of customization, ChatGPT might be the most suitable option, especially with the recent accessibility improvements on the Plus tier. Professionals needing quick, verifiable information with a strong emphasis on source attribution may find Perplexity a compelling choice. Users heavily invested in the Google ecosystem and seeking a collaborative research environment could benefit from Gemini. Finally, for those interested in incorporating real-time social media insights into their research, Grok offers a unique advantage. Ultimately, the “best” platform depends on the specific research requirements, budget, and individual preferences for features like transparency, speed, and integration with existing workflows. Users are encouraged to leverage the free tiers or trial periods offered by some platforms to evaluate which best meets their needs.

Works cited

1. xAI - Grok, accessed March 28, 2025, https://x.ai/grok
2. Grok 3 Beta — The Age of Reasoning Agents | xAI, accessed March 28, 2025, https://x.ai/news/grok-3
3. ChatGPT | OpenAI, accessed March 28, 2025, https://openai.com/chatgpt/overview/
4. ChatGPT now tells you its limits with Deep Research and GPT-4.5 chats - BGR, accessed March 28, 2025, https://bgr.com/tech/chatgpt-now-tells-you-its-limits-with-deep-research-and-gpt-4-5-chats/
5. ChatGPT Plus just got a huge deep research upgrade — here’s how to try it now, accessed March 28, 2025, https://www.tomsguide.com/ai/chatgpt/chatgpt-plus-just-got-a-huge-deep-research-upgrade-how-to-try-it-now
6. Formatting of citations in Deep Research : r/ChatGPT - Reddit, accessed March 28, 2025, https://www.reddit.com/r/ChatGPT/comments/1ij39pi/formatting_of_citations_in_deep_research/
7. OpenAI Launches Deep Research (New AI Feature for ChatGPT): What it Do and How to Use It - GeeksforGeeks, accessed March 28, 2025, https://www.geeksforgeeks.org/chatgpt-deep-research-what-it-do-and-how-to-use-it/
8. I tried Deep Research on ChatGPT, and it’s like a super smart but slightly absent-minded librarian from a children’s book | TechRadar, accessed March 28, 2025, https://www.techradar.com/computing/artificial-intelligence/i-tried-deep-research-on-chatgpt-and-its-like-a-super-smart-but-slightly-absent-minded-librarian-from-a-childrens-book
9. Gemini Advanced - get access to Google’s most capable AI models …, accessed March 28, 2025, https://gemini.google/advanced/
10. Google Has Dropped the Paywall for These Gemini Features - Lifehacker, accessed March 28, 2025, https://lifehacker.com/tech/google-has-dropped-the-paywall-for-these-gemini-features
11. Perplexity, accessed March 28, 2025, https://www.perplexity.ai/
12. Perplexity Pricing: Is It Worth It in 2025? | Team-GPT, accessed March 28, 2025, https://team-gpt.com/blog/perplexity-pricing/
13. Perplexity AI’s Deep Research Tool Is Almost as Good as OpenAI’s, and It’s Free | Lifehacker, accessed March 28, 2025, https://lifehacker.com/tech/perplexity-ai-deep-research-almost-as-good-as-openai-and-free
14. What is Perplexity Deep Research, and how do you use it? - ZDNET, accessed March 28, 2025, https://www.zdnet.com/article/what-is-perplexity-deep-research-and-how-do-you-use-it/
15. Hands on with Deep Research - Leon Furze, accessed March 28, 2025, https://leonfurze.com/2025/02/15/hands-on-with-deep-research/
16. Perplexity Deep Research: Democratizing Access to AI-Powered Insights - Medium, accessed March 28, 2025, https://medium.com/@lewandog/perplexity-deep-research-democratizing-access-to-ai-powered-insights-84cb3b02857f
17. Elon Musk’s Groke AI chatbot is now free. But it has some glaring limitations, accessed March 28, 2025, https://m.economictimes.com/magazines/panache/elon-musks-groke-ai-chatbot-is-now-free-but-it-has-some-glaring-limitations/articleshow/116066785.cms
18. Grok 3 AI Review 2025: Is It Replacing ChatGPT? - Cybernews, accessed March 28, 2025, https://cybernews.com/ai-tools/grok-3-ai-review/
19. grok vs deepseek - Latenode, accessed March 28, 2025, https://latenode.com/blog/grok-vs-deepseek
20. How Long Will Grok-3 Be Free to Use, What Will Costly Pricing Be? - SentiSight.ai, accessed March 28, 2025, https://www.sentisight.ai/how-long-will-grok-3-be-free-what-pricing-comes/
21. Pricing - ChatGPT - OpenAI, accessed March 28, 2025, https://openai.com/chatgpt/pricing/
22. Gemini Pricing: Everything You’ll Pay for Google Gemini - UC Today, accessed March 28, 2025, https://www.uctoday.com/collaboration/gemini-pricing-everything-youll-pay-for-google-gemini/
23. Gemini Pricing: Is It Worth It In 2025? [In-Depth Review] - Team-GPT, accessed March 28, 2025, https://team-gpt.com/blog/gemini-pricing/
24. What is Perplexity’s Deep Research and can this free tool change how we do research?, accessed March 28, 2025, https://www.brainforge.ai/blog/what-is-perplexitys-deep-research-and-can-this-free-tool-change-how-we-do-research
25. Deep Research Capabilities: Comparing ChatGPT, Perplexity, Grok, and Kompas AI, accessed March 28, 2025, https://bytebridge.medium.com/deep-research-capabilities-comparing-chatgpt-perplexity-grok-and-kompas-ai-012d643fef5a
26. We tested two Deep Research tools. One was unusable. - Section School, accessed March 28, 2025, https://www.sectionschool.com/blog/chatgpt-vs-gemini-deep-research
27. The smarter way to research with Google Gemini Deep Research - Revolgy, accessed March 28, 2025, https://www.revolgy.com/insights/blog/smarter-way-to-research-with-google-gemini-deep-research
28. Perplexity AI - Wikipedia, accessed March 28, 2025, https://en.wikipedia.org/wiki/Perplexity_AI
29. I used Perplexity AI for one month instead of ChatGPT: here is my review - Techpoint Africa, accessed March 28, 2025, https://techpoint.africa/guide/my-perplexity-ai-review/
30. Perplexity AI Debuts ‘Deep Research Alpha’ to Challenge the Big Leagues - OpenTools, accessed March 28, 2025, https://opentools.ai/news/perplexity-ai-debuts-deep-research-alpha-to-challenge-the-big-leagues
31. Grok AI: A New Era of Free Access - El Dorado, accessed March 28, 2025, https://eldorado.io/en/blog/grok-ai-free-access-impact/
32. Exploring AI’s Interaction with Paywalled Content | Copyleaks Blog, accessed March 28, 2025, https://copyleaks.com/blog/exploring-ais-interaction-with-paywalled-content
33. OpenAI Pauses Bing Integration in ChatGPT to Address Paywall Bypass Issue - Binance, accessed March 28, 2025, https://www.binance.com/en/square/post/735638
34. How to Use ChatGPT to Navigate Around Paywalls - AI Breakfast - Beehiiv, accessed March 28, 2025, https://aibreakfast.beehiiv.com/p/use-chatgpt-navigate-around-paywalls
35. Perplexity lawsuit sends warning to product managers - Mind the Product, accessed March 28, 2025, https://www.mindtheproduct.com/perplexity-lawsuit-sends-warning-to-product-managers/
36. How AI search tool Perplexity is sharing ad revenue with publishers - Press Gazette, accessed March 28, 2025, https://pressgazette.co.uk/platforms/perplexity-ai-news-publishers-ad-sharing-revenue/
37. Perplexity Deep Research - Hacker News, accessed March 28, 2025, https://news.ycombinator.com/item?id=43061827
38. Grok 3 System Prompt leaked - GitHub Gist, accessed March 28, 2025, https://gist.github.com/xnohat/dc67847b5c0a479e098cf3c81de97aa1
39. Grok 3 system prompts - GitHub Gist, accessed March 28, 2025, https://gist.github.com/matijagrcic/a959cf15a84813c2214757460038ef20
40. Grok 3 Markdown Problem | Mike Levin on Linux, Python, vim & git (LPvg), accessed March 28, 2025, https://mikelev.in/futureproof/grok3-markdown-problem/
41. Learn Anything Faster: Save 80% of Your Time with Gemini Deep Research & Obsidian [Guide & Setup] - YouTube, accessed March 28, 2025, https://www.youtube.com/watch?v=U8FxNcerLa0
42. Deep Research Agents: Google VS Perplexity VS Grok VS ChatGPT…Who Wins? - YouTube, accessed March 28, 2025, https://www.youtube.com/watch?v=W2PSiRXXakg
43. Grok 3 Review: I Tested 100+ Prompts and Here’s the Truth (2025) - Writesonic Blog, accessed March 28, 2025, https://writesonic.com/blog/grok-3-review
44. Deep Research compared - my exeprience : ChatGPT, Gemini, Grok, Deep Seek : r/OpenAI, accessed March 28, 2025, https://www.reddit.com/r/OpenAI/comments/1jj4bvk/deep_research_compared_my_exeprience_chatgpt/
45. I tested Grok-3 with 5 prompts — here’s what I like and don’t like about this chatbot, accessed March 28, 2025, https://www.tomsguide.com/ai/i-tested-grok-3-with-5-prompts-heres-what-i-like-and-dont-like-about-this-chatbot
46. Deep Research is hands down the best research tool I’ve used—anyone else making the switch? : r/ChatGPTPro - Reddit, accessed March 28, 2025, https://www.reddit.com/r/ChatGPTPro/comments/1iis4wy/deep_research_is_hands_down_the_best_research/
47. ChatGTP Is Way, Way Off in Estimating When Project Will Be Done - ChatGPT - OpenAI Developer Community, accessed March 28, 2025, https://community.openai.com/t/chatgtp-is-way-way-off-in-estimating-when-project-will-be-done/1133942
48. First Impressions of ChatGPT’s Deep Research | by Sam Edelstein | Feb, 2025 | Medium, accessed March 28, 2025, https://samedelstein.medium.com/first-impressions-of-chatgpts-deep-research-eb4e1cc1c77c
49. I can get answers from ChatGPT, but Deep Research gives me a whole dissertation I’ll almost never need | TechRadar, accessed March 28, 2025, https://www.techradar.com/computing/artificial-intelligence/i-can-get-answers-from-chatgpt-but-deep-research-gives-me-a-whole-dissertation-ill-almost-never-need
50. Which AI Deep Research Tool Is Worth It? ChatGPT vs Grok vs Perplexity And More!, accessed March 28, 2025, https://www.youtube.com/watch?v=5dRxhqtIbOg
51. I spent 30 days using perplexity AI instead of google (here’s my review) - Techpoint Africa, accessed March 28, 2025, https://techpoint.africa/2025/01/30/my-perplexity-ai-review/
52. How good is Perplexity Deep Research? : r/perplexity_ai - Reddit, accessed March 28, 2025, https://www.reddit.com/r/perplexity_ai/comments/1j7dl2m/how_good_is_perplexity_deep_research/

Note: MikeLev.in Referenced As a Source, HAHA!

It’s not the first time I’ve seen it, but there I am referenced as a source. Nice vanity-flex, especially where the content’s so new. The main things to note:

• It works even though it’s a .in domain (India), which I do because it makes such a cool domain.
• The article was only just published February 20, 2025. That’s only 36 days! So very new things are included in research.
• The main subject of the article was only tangential, yet the article’s sub-reference to the main topic was enough to surface it.
• Even though I’m an SEO, this experimental vanity site of mine doesn’t have particularly much domain authority, and still showed up.

There are certain playing-field leveling tea-leaves to be read at least here in Google Gemini’s version of research. That bodes well for social media influencers who want to dig deeper on their subject-matter than ad-plagued popularity contest between incumbents and Wikipedia that modern search results provide.

Note: Markdown Formatting

Vertical Bar (pipe) HTML Entity Encoding

First, I had to go through Google Docs to get this markdown, but it did do the job beautifully. The only special formatting I had to do so that this rendered properly in the Jekyll static site generator (SSG) system built into GitHub Pages (github.io) was the HTML entity encoding of the pipe symbol (|), for you see this is used in formatting tables, and something in my _config.yaml configuration is over-enthusiastic with table-conversion:

# Conversion
markdown: kramdown
highlighter: rouge
rouge:
  style: github 

…and so I had to search/replace occurrences of vertical vertical bar pipe that wasn’t deliberately used in tables with its HTML entity encoding, |. And since I use vim (nvim), you do a vertical selection over the area you want to do the search & replace, and then type a colon : to enter vim command-mode and give it the pattern:

:'<,'>s/|/\&#124;

For anyone actually here trying to figure that out (the long-long-long tail, haha), you don’t type the :'<,'> part. That’s an artifact of the visual-select mode. Once you go into command-mode, you type the s/|/\| part, whose parsed meaning is in simple terms:

• s/ → This stands for “substitute,” which tells Vim you’re replacing something.
• | → This is the thing you’re searching for—in this case, a vertical bar (pipe character).
• /\| → This is what you’re replacing it with. The \| is the HTML entity encoding for a vertical bar (|).

So, in plain English:

This command finds every | (pipe character) in the selected text and replaces it with |, which is the special HTML-friendly version of the pipe.

There is a backslash before the ampersand \& because without it, the ampersand would be interpreted as a special Regular Expression (RegEx) command. Knowing these nuances of mixed-context vim-commands, regex and HTML entity is precisely as hard as it looks. It comes with time and practice, and is worth it.

If you were doing this outside of a selection, you’d type :%s/|/\|/g to apply it to the whole file.

ChatGPT 4.5

Me: [Pastes article prompt]

ChatGPT: Great — I’ll compare Grok, ChatGPT, Gemini, Perplexity, Claude, Bing Copilot, and You.com for their equivalent ‘deep research’ capabilities.

I’ll examine branding terminology (e.g., Deep Search vs. Deep Research), service tiers and access levels (free vs paid, usage limits), and functional differentiators like data sources (e.g., X/Twitter, Reddit), citation quality, report formatting, and any other standout features.

I’ll organize everything into a structured, easy-to-digest report with tables and clear headers. I’ll be back shortly with a full breakdown!

Me:

1. Yes, include the others if they have the equivalent of a deep research feature.
2. Yes.
3. Latest.

The “Starting research” step took some time with little visual feedback, but then the right-hand panel opened. The favicons are used there per source, but with a distinctly different style than Perplexity and Google (which group them). ChatGPT shows one favicon per… uh… thought? ChatGPT communicates very precisely about what it’s doing at each step, and seems to linger longer at each step than the prior two service did. I am not measuring the exact timing, but ChatGPT is definitely spending a lot longer than Google and Perplexity researching, though that’s not entirely fair because the Perplexity test used 3/day the free version.

Research completed in 15m · 33 sources

ChatGPT: Great — I’ll compare Grok, ChatGPT, Gemini, Perplexity, Claude, Bing Copilot, and You.com for their equivalent ‘deep research’ capabilities.

I’ll examine branding terminology (e.g., Deep Search vs. Deep Research), service tiers and access levels (free vs paid, usage limits), and functional differentiators like data sources (e.g., X/Twitter, Reddit), citation quality, report formatting, and any other standout features.

I’ll organize everything into a structured, easy-to-digest report with tables and clear headers. I’ll be back shortly with a full breakdown!

Comparison of Advanced AI Research Assistant Capabilities (March 2025)

Branding of Enhanced Research Features

Each major AI assistant has introduced branded “deep research” modes or agents for in-depth web browsing and analysis:

• OpenAI ChatGPT: Deep Research (an autonomous research agent that browses the web and generates cited reports) (ChatGPT Deep Research - Wikipedia). This is distinct from ChatGPT’s standard “Search” browsing mode.
• xAI Grok (X/Twitter): DeepSearch (a high-speed agent for web and X content) (Grok 3 Beta — The Age of Reasoning Agents | xAI). Grok also offers a “Think” mode for extra reasoning.
• Google (Gemini/Bard): Deep Research (integrated into the Gemini AI app) (Gemini app updates: Deep Research, connected apps, personalization). In Search, Google’s AI Mode (Labs experiment) provides an all-AI answer experience using Gemini (Google, Microsoft Expand AI-Driven Search Capabilities – Campus Technology).
• Perplexity AI: Deep Research (a new in-depth answer tool within Perplexity’s AI search) (Perplexity launches its own freemium ‘deep research’ product | TechCrunch).
• Anthropic Claude: No special name (Claude added web search capability with direct citations) (Anthropic adds web search to its Claude chatbot | TechCrunch), closing the gap with rivals.
• Microsoft Bing: Branded as part of Bing Chat / Copilot, but no separate “deep research” name. Bing’s AI chat is billed as your “Copilot for the web,” now even testing a full AI answers mode (Google, Microsoft Expand AI-Driven Search Capabilities – Campus Technology).
• You.com: ARI (Advanced Research & Insights) – a professional-grade deep research agent (powered by You.com’s Advanced Research & Reasoning mode) (ARI vs. ChatGPT Deep Research vs. Google Deep Research: Why Businesses Are Choosing ARI | You.com). ARI is designed for multi-step research across hundreds of sources.

Tiered Access and Usage Limits

The availability and limits of these advanced research features vary by service and subscription tier:

OpenAI ChatGPT:

• Free Tier: No real-time browsing or deep research (knowledge cutoff 2021).
• Plus ($20/month): Real-time web Search enabled (quick browsing answers with source links) (Deep Research FAQ | OpenAI Help Center). Also includes Deep Research – limited to 10 deep-research queries per 30 days (Deep Research FAQ | OpenAI Help Center). Plus users get GPT-4 for chat, with standard message limits, and can use browsing unlimitedly for quick queries.
• Pro ($200/month): Up to 120 Deep Research tasks per month (Deep Research FAQ | OpenAI Help Center) (expanded from 10) for heavy research users, plus priority access. Pro and Plus both use the same Deep Research tool (5–30 minute autonomous sessions), but Pro unlocks far more runs (ChatGPT Deep Research - Wikipedia).
• Team/Enterprise: Similar Deep Research limit (10/month per seat) (ChatGPT Deep Research - Wikipedia), with enterprise data privacy. Enterprise users can also leverage longer context windows and advanced tools in ChatGPT.

xAI Grok:

• Free Access: As of Grok 3’s launch (Feb 2025), all X users can try Grok for free with limited daily queries (Grok AI Pricing: How Much Does Grok Cost in 2025?) (Grok AI Pricing: How Much Does Grok Cost in 2025?). (xAI announced the chatbot will be free “until our servers melt.”) Free users have basic Grok functionality and image generation, but with restricted usage.
• X Premium ($8/month): Includes higher usage limits than free (Grok AI Pricing: How Much Does Grok Cost in 2025?) and faster response times. Grok 3 became available to Premium users with the new model. However, DeepSearch and other advanced agents were initially exclusive to Premium+ users (Grok 3 Beta — The Age of Reasoning Agents | xAI).
• X Premium+ ($40/month): Unlocks DeepSearch and Think modes immediately (Grok 3 Beta — The Age of Reasoning Agents | xAI), with much higher daily limits and early access to new features (Grok AI Pricing: How Much Does Grok Cost in 2025?). (Premium+ pricing doubled from $16 to $40 with Grok-3’s release, reflecting the added AI features (Grok AI Pricing: How Much Does Grok Cost in 2025?).) In short, free users get a taste of Grok’s capabilities, while paying users (especially Premium+) get virtually unlimited access and the full suite of research tools.

Google Gemini (Bard):

• Standard (Free): The Gemini chatbot (via gemini.google.com or Bard) is free. Deep Research mode is available to all users for a few uses per month at no cost (Gemini app updates: Deep Research, connected apps, personalization). (Google effectively allows a limited number of deep-research queries for free, e.g. a handful each month, to showcase the feature.) The free version uses the default Gemini model and can handle reasonably complex queries, albeit with some limits on depth.
• Google One AI Premium ($19.99/month): Subscribers get Gemini Advanced, which uses the largest “Ultra” model for both regular chat and Deep Research (Are you subscribed to Google One AI Premium for Gemini?) (Are you subscribed to Google One AI Premium for Gemini?). AI Premium users enjoy expanded Deep Research access (significantly more runs per month) (Gemini app updates: Deep Research, connected apps, personalization) and faster, more detailed responses. This plan also bundles 2 TB Drive storage and generative AI features in Google Workspace apps (Are you subscribed to Google One AI Premium for Gemini?). (Notably, Google’s AI Mode in Search – an all-AI answer mode – is initially only available to AI Premium subscribers in the U.S. (Google, Microsoft Expand AI-Driven Search Capabilities – Campus Technology).)
• Education/Workspace: Some education and enterprise Google accounts also have access to Gemini’s advanced features. Google has been rolling out larger-context Gemini models (e.g. “1.5 Pro”) to improve depth for paying users (ARI vs. ChatGPT Deep Research vs. Google Deep Research: Why Businesses Are Choosing ARI | You.com).

Perplexity AI:

• Free Users: Deep Research is available for free on the web. Non-subscribers can initiate a limited number of deep research queries per day (the exact cap isn’t publicly stated, but it’s throttled to prevent heavy use) (Perplexity launches its own freemium ‘deep research’ product | TechCrunch). Standard Perplexity search queries are otherwise unlimited, and even free users get citations and up-to-date answers (with GPT-3.5 or similar).
• Pro Subscription (~$20–25/month): Unlocks unlimited Deep Research queries (Perplexity launches its own freemium ‘deep research’ product | TechCrunch). Pro users also get GPT-4 powered answers for normal queries, priority API access, and other perks. Essentially, Perplexity has adopted a freemium model: the in-depth research mode is mostly free (to attract users) while power-users can pay for no caps and higher-quality model outputs. This contrasts with OpenAI’s approach of paywalling most deep research capacity (Perplexity launches its own freemium ‘deep research’ product | TechCrunch).

Anthropic Claude:

• Free Tier: Claude.ai offers a free plan with limited usage (historically a few messages every 8 hours). As of March 2025, web search is not yet enabled for free users (Anthropic adds web search to its Claude chatbot | TechCrunch) (Claude can now search the web \ Anthropic). Free Claude users can still utilize its 100k-token context for big documents, but their knowledge cutoff (end of 2023) applies if web search is off.
• Paid Plans: Claude’s web browsing feature launched in preview for paid users in the US (Anthropic adds web search to its Claude chatbot | TechCrunch). Anthropic doesn’t sell individual “Pro” subscriptions like OpenAI, but they have Claude Pro (beta) for some users and business/team plans. Any paid Claude user can toggle on web search in Claude 3.7 (latest model) and get internet-augmented answers (Claude can now search the web \ Anthropic). This is effectively unlimited for those users, with the model deciding when to pull in web info. Claude’s paid tiers (team and enterprise) also allow far more prompt output length (due to the huge context window) and higher rate limits. In summary: Claude’s new web search is behind a paywall initially, but once enabled it provides cited, real-time answers just like the others (Anthropic adds web search to its Claude chatbot | TechCrunch). (Anthropic has stated free access to web search will come later as they scale (Claude can now search the web \ Anthropic).)

Bing Chat (Microsoft):

• Free: Bing’s GPT-4 powered chat is free for anyone on Bing.com or in the Edge sidebar (login required). There is no separate “premium” tier for Bing’s AI; Microsoft instead monetizes it via ads and as an inducement to use Bing/Edge. Daily limits: Microsoft originally imposed per-conversation turn limits (e.g. 20 questions per session, 200 per day), which have expanded over time. As of 2025 the limits are high enough that most users won’t hit them in normal use. Essentially, Bing offers unlimited brief research queries for free, making it the most accessible GPT-4 search tool.
• Bing Chat Enterprise: Enterprise Microsoft 365 accounts have a version of Bing Chat with the same capabilities but commercial data protection. No extra research features beyond privacy and compliance. (This is included in certain Office licensing.)
• Windows Copilot Integration: Also free with Windows 11 – it’s the same Bing AI, now able to interact with your PC. This integration doesn’t cost extra, but requires Windows 11 and the user to invoke it. In sum, Bing/“Copilot” is a single-tier service: the full GPT-4 web research experience is available to all users, with Microsoft footing the bill (and experimenting with ads in the answers) (Google, Microsoft Expand AI-Driven Search Capabilities – Campus Technology).

You.com (ARI):

• Consumer (YouChat): The standard YouChat 2.0 (free) provides conversational search answers with citations, but the new ARI deep research agent is not part of the free tier as of March 2025. ARI is positioned as a professional/enterprise feature. You.com has a waitlist and demo sign-ups for ARI (Meet ARI, the first professional-grade research agent - You.com), indicating it’s targeting business users. It likely will be a paid service (pricing on request or per-seat) given its advanced capabilities (e.g. generating polished reports).
• Enterprise Plans: You.com offers enterprise “AI enablement” packages where ARI can be customized to an organization’s internal data (ARI vs. ChatGPT Deep Research vs. Google Deep Research: Why Businesses Are Choosing ARI | You.com). This implies a paid tier where ARI integrates with private documents and delivers research findings. No public pricing is published, but it’s expected to be a premium service. (In contrast, You.com’s older YouPro subscription ($9.99/mo) was aimed at enhanced YouChat with GPT-4 for general use, not the new ARI system.)

Table: Deep Research Availability by Tier

Key: ✅=Available, ❌=Not available.

Unique Differentiators and Features

Beyond pricing and basic availability, these services differentiate themselves on several dimensions:

Data Source Access and Coverage

All the assistants leverage web data, but each has unique source advantages:

• Grok (xAI): Exclusive access to X (Twitter) content. Grok is trained on public X data and can pull real-time posts and trends from X in its answers (Grok AI Pricing: How Much Does Grok Cost in 2025?). This means if you ask about social media reactions or breaking news on X, Grok can quote and analyze actual tweets – something others lack. (It does not have access to private data on X, just public posts.) Grok also searches the open web unrestrictedly; users note it will even search forums and unconventional sites that others might avoid. This “open throttle” approach yields up-to-the-minute info, but can include less reliable sources.

• ChatGPT (OpenAI): Open web (curated). ChatGPT’s browsing/search is designed to pull from a broad set of websites, but OpenAI maintains a safe-list and avoids certain domains. It tends to favor trusted news sites and high-authority pages in responses (How’s Grok 3 Deepsearch compared to ChatGPT’ Deep Search? : r/ChatGPT). For example, it might decline to retrieve data from Reddit or obscure blogs for safety/privacy reasons, whereas Grok or Bing would. OpenAI explicitly notes that Deep Research currently cannot access subscription or paywalled content (Deep Research FAQ | OpenAI Help Center). So it won’t summarize an article hidden behind a paywall (unless you provide it). In practice, ChatGPT focuses on the open-access web and “approved” sources, which enhances quality control but means less coverage of niche user-generated content.

• Google Gemini: Leverages Google’s entire index. Gemini’s Deep Research is backed by Google Search’s indexing of the web, which is arguably the most extensive. It can draw on Google’s news feeds, scholarly articles via Google Scholar, YouTube transcripts, and more. This gives it a very comprehensive reach. A strength is that multilingual and non-English web content is well within Google’s sphere (so Gemini might excel at pulling sources in other languages or region-specific info). One caveat: Google’s AI may inherit the biases of Google Search’s ranking – e.g. it might prioritize popular/high-SEO sites over niche but authoritative ones (ARI vs. ChatGPT Deep Research vs. Google Deep Research: Why Businesses Are Choosing ARI | You.com). (Google’s own marketing frames this as providing breadth and quick overviews, but it may miss some deep scholarly sources, focusing on what Search deems relevant.) Still, for broad coverage of news, general knowledge, and even things like Reddit or Medium posts (which Google indexes), Gemini has strong data access. Additionally, through Google’s Knowledge Graph, it can incorporate factual data (e.g. company info, dates, definitions) directly.

• Perplexity: Aggregated web results (and aggressive crawling). Perplexity uses Bing’s search API under the hood to find relevant pages, then visits and summarizes them. Its agent will click through multiple results (“iteratively searches and reads”) in a single query (Perplexity launches its own freemium ‘deep research’ product | TechCrunch) (Perplexity launches its own freemium ‘deep research’ product | TechCrunch). Uniquely, Perplexity has been willing to scrape content that others avoid – which led to controversy. It has been accused of bypassing paywalls and summarizing paywalled news (Forbes, WSJ, etc.) without permission (Plagiarism and Paywall Dodging: Perplexity AI’s Unethical Practices …) (Perplexity AI: the answer engine with a lot of question marks | The Verge). The company denies outright “illegally” accessing content, but its summaries often include details from sources the user couldn’t access freely, implying Perplexity found a way. This aggressive approach means Perplexity might retrieve information from premium journalism or academic papers (giving users answers that include those insights), whereas ChatGPT/Google would stop at the abstract or require the user to provide the text. However, this raised legal and ethical questions – News Corp even sued Perplexity for “copying on a massive scale” from its sites (Perplexity AI: the answer engine with a lot of question marks | The Verge) (Perplexity AI: the answer engine with a lot of question marks | The Verge). In summary: Perplexity’s data access is very broad and sometimes enters gray areas (it “skips the links” to give you the content directly). This can be a boon for users (more complete answers) but is being watched by publishers.

• Claude (Anthropic): Moderated open web search. Claude’s new web search uses an unspecified search API (likely Google or DuckDuckGo) to fetch results (Claude can now search the internet using the new tools. - Reddit). In testing, Claude can pull from news sites and even social media posts on X or Reddit if relevant (Anthropic adds web search to its Claude chatbot | TechCrunch). Anthropic emphasizes that Claude will provide direct citations so you know the source (Anthropic adds web search to its Claude chatbot | TechCrunch). Because Claude was originally designed to be self-contained and harmless, Anthropic likely applies some filters to its web access (to avoid extremist content, etc.), but the specifics aren’t public. Early users noted it didn’t always trigger on current events, suggesting a cautious rollout (Anthropic adds web search to its Claude chatbot | TechCrunch). Overall Claude’s web reach is now comparable to Bing/ChatGPT’s: mainstream news, Wikipedia, forums, etc., with the advantage that Claude can combine it with a 100k-token memory of prior context. For example, Claude could accept a long PDF from the user and also fetch recent web facts to cross-check, merging both in its answer – a powerful combo for research.

• Bing Chat: Bing’s web index + multimedia search. Bing’s data access is similar to Perplexity’s since both rely on Bing search results. Bing will fetch content from across the web and provides citation links. Thanks to Microsoft’s integrations, Bing can also do things like show you relevant images from Bing Images, or even use GPT-4 Vision to interpret images (in Edge). It also has some vertical integrations: for example, asking Bing about code or math may trigger it to use special formats (it can render LaTeX, etc.). Another point: Microsoft has partnerships with some publishers (through Bing’s search deals), so it might have the right to show longer snippets from certain paywalled news (though it still tends to just cite them). On user forums and social media, Bing will return public info – e.g. Reddit threads often appear with quotes in Bing answers. One limitation: Bing’s index is not as large as Google’s for less-common languages or very new sites (given Google’s 90% search market share (Google, Microsoft Expand AI-Driven Search Capabilities – Campus Technology) (Google, Microsoft Expand AI-Driven Search Capabilities – Campus Technology)), but for most English content it’s quite comparable.

• You.com ARI: Hybrid of web + private data. ARI is designed to intake both public web data and a company’s internal data (if provided) (ARI vs. ChatGPT Deep Research vs. Google Deep Research: Why Businesses Are Choosing ARI | You.com). For web searching, You.com has its own search index and also federates queries to sources (You.com historically had “apps” for Reddit, StackOverflow, etc., meaning it can specifically pull data from those silos). ARI can “analyze up to 400 sources” or more per query, according to You.com (Meet ARI, the first professional-grade research agent - You.com). This likely includes a lot of web pages plus any user-uploaded documents. In enterprise use, ARI could connect to a client’s knowledge base, SharePoint, etc., which is a differentiator: it’s not just public info but whatever data you feed it. This makes ARI appealing for businesses doing internal research (e.g. analyzing proprietary reports alongside web news).

Most important data-access differentiators: For many users, freshness and breadth of information are key. Users doing research on breaking news or social trends might favor Grok (for Twitter) or Bing/Perplexity (for broad web reach). Those who need scholarly or paywalled info might find Perplexity’s “no stone unturned” approach very valuable. Enterprise users with private data will care that ARI or Claude can incorporate their internal documents. Meanwhile, Google and OpenAI emphasize reliable data over sheer quantity – important if authoritative sources are a priority. According to user feedback, having up-to-date knowledge is crucial (nobody wants an “as of 2021” answer), so all these tools offering real-time info is a baseline expectation now. The inclusion of social media content is a notable differentiator: users have observed that ChatGPT’s answers feel sanitized and miss out on forums, whereas Grok or Perplexity will include that perspective. Depending on the use case, this can be either a pro or a con.

Infrastructure and Model Advantages

Under the hood, each service has different AI model strengths and computing infrastructure:

• Model Power: OpenAI’s GPT-4 (and its research-tuned variant “o3”) is known for top-tier reasoning ability. ChatGPT Deep Research explicitly uses a specialized version of GPT-4 (called o3) geared for complex analysis (ChatGPT Deep Research - Wikipedia). This gives it an edge in quality of logic and language. Google’s Gemini Ultra is a direct competitor – by 2025, Gemini models are approaching GPT-4 level. Google’s infrastructure (TPUs and DeepMind algorithms) means Gemini can be highly capable, especially in multimodal tasks and multilingual understanding. Anthropic’s Claude 3.7 is also strong, especially in handling very lengthy text (100k+ tokens). Grok 3 claims to surpass many models in benchmarks (I Tested Grok 3, and It’s Not Worth the Price Hike | Lifehacker) (I Tested Grok 3, and It’s Not Worth the Price Hike | Lifehacker), thanks to a massive 200k-GPU training run, though real-world tests still put GPT-4 ahead in consistency. Perplexity’s agent likely uses a mix of models: for speed, it may use a slightly smaller LLM (its reported performance suggests somewhere between GPT-3.5 and GPT-4 in quality (Perplexity launches its own freemium ‘deep research’ product | TechCrunch) (Perplexity launches its own freemium ‘deep research’ product | TechCrunch)). You.com’s ARI hasn’t disclosed its base model – it could be built on an open-source large model (like Llama 2) fine-tuned for research tasks, combined with retrieval. In short, OpenAI and Google still hold an edge in core model sophistication, while others optimize around speed/efficiency with slightly less heavy models.

• Speed vs. Depth Tradeoff: OpenAI’s Deep Research is deliberately slow (5–30 minutes) to maximize thoroughness (Deep Research FAQ | OpenAI Help Center) (Deep Research FAQ | OpenAI Help Center). It runs many web queries and carefully reads content. Google’s is also fairly in-depth but has been tuned for quicker turnaround (a few minutes). Perplexity and ARI prioritize speed: Perplexity often finishes in under 3 minutes for most tasks (Perplexity launches its own freemium ‘deep research’ product | TechCrunch), and You.com boasts ARI is 3x faster than ChatGPT (around 3 minutes on average) (ARI vs. ChatGPT Deep Research vs. Google Deep Research: Why Businesses Are Choosing ARI | You.com) (ARI vs. ChatGPT Deep Research vs. Google Deep Research: Why Businesses Are Choosing ARI | You.com). This is achieved by more efficient search strategies and perhaps processing slightly fewer steps or using faster inference. The infrastructure difference: OpenAI and Google can throw huge compute at a single query (hence 20 minutes of heavy lifting), whereas Perplexity and others seem to aim for a faster, possibly lighter footprint answer. Users have noted this difference: ChatGPT’s deep reports are extremely comprehensive but can feel slow (several users reported waiting 10–20 minutes for a single answer) (Grok DeepSearch vs. ChatGPT DeepResearch - Community - OpenAI Developer Community). Grok’s DeepSearch, by contrast, was praised for completing in seconds to a couple minutes – albeit with a shorter report (How’s Grok 3 Deepsearch compared to ChatGPT’ Deep Search? : r/ChatGPT). Depending on user needs, this speed difference is significant: casual users might prefer a 2-minute decent answer over a 15-minute perfect answer, whereas analysts might want maximum depth.

• Scale and Integration: Google’s ownership of the search stack gives Gemini a potential advantage in scaling up. It can query its index extremely quickly and in parallel. Likewise, Microsoft’s Azure infrastructure plus OpenAI’s model ensures Bing and ChatGPT have substantial cloud muscle behind them (though Microsoft must manage costs for Bing’s free users by throttling as needed). xAI’s Colossus supercomputer (built explicitly for Grok) means xAI isn’t beholden to Azure or Google Cloud – they tout having 200k GPU cluster devoted to Grok 3 (I Tested Grok 3, and It’s Not Worth the Price Hike | Lifehacker). This suggests Grok can scale independently and possibly iterate faster on model improvements. Anthropic’s infrastructure is backed by AWS and their own optimizations, focused on large context handling efficiently (Claude’s 100k context is an industry-leading feature requiring special memory management).

• Context Length: As mentioned, Claude can take ~100,000 tokens of context (roughly 75,000 words) without choking. This is incredibly useful if you want to feed multiple long documents into the model along with the web info. ChatGPT’s deep research hasn’t published an exact context, but GPT-4 typically maxes 8k or 32k tokens depending on version. Google’s blog indicated Gemini 2.0 Flash has a longer context; one source claims up to 1 million tokens for Google Deep Research (likely an experimental feature) (ARI vs. ChatGPT Deep Research vs. Google Deep Research: Why Businesses Are Choosing ARI | You.com). If true, that’s enormous and would outdo Claude. It might refer to segmented reading rather than a single sequence, but either way, Google is pushing context limits too. ARI presumably handles very long inputs by breaking them down (the term “Advanced Reasoning” implies it can chunk content). For most users, context size isn’t a visible spec, but for research it matters when analyzing lengthy reports or combining many sources. Enterprises have noted Claude’s ability to digest an entire book or repository of documents in one go as a game-changer. Google and OpenAI are racing to catch up on that front.

• Reliability and Safety: OpenAI and Anthropic have been very conservative with safety filters, which sometimes means censored or refused outputs (e.g. OpenAI’s content rules might stop a deep research on sensitive political misinformation, whereas Grok might proceed). Elon Musk advertises Grok as a “based” AI that won’t shy from controversial topics. Indeed, users report Grok will answer things that ChatGPT might refuse due to content policy. Depending on perspective, this is an advantage (fewer roadblocks in research) or a risk (less filtering of possibly false or harmful content). For enterprise settings, compliance may be more important, so OpenAI/Anthropic’s cautious approach could be a plus. For an individual user curious about edgy topics, Grok’s freer responses might be valued. Speed and uptime reliability are also infrastructure points – all these services generally handle loads well, though ChatGPT Plus can get slow at peak times, and new features like Deep Research are compute-heavy (OpenAI actually charges a premium for heavy use via the Pro plan, reflecting the cost).

Most important infrastructure differences: According to many users, speed and raw quality are a trade-off – ChatGPT’s thoroughness is applauded, but the wait time frustrates some (How’s Grok 3 Deepsearch compared to ChatGPT’ Deep Search? : r/ChatGPT) (Grok DeepSearch vs. ChatGPT DeepResearch - Community - OpenAI Developer Community). Perplexity’s near-instant answers get praise for efficiency. Meanwhile, enterprises often care about integration and scale: Google and Microsoft integrate AI into existing workflows (search, Office) which is huge for user adoption. OpenAI’s focus is on depth and accuracy, which researchers appreciate. One can infer that casual users gravitate to the free, fast tools (Bing, Perplexity) while professional users might favor the robust, if slower, ChatGPT or the integrated Google solution – provided they can afford or access them. Ultimately, each service is leveraging its core strength: OpenAI bets on model superiority, Google on its search backbone, xAI on Musk’s data center and data (Twitter), and smaller players on agility (speed, targeted features).

Report Format and Output Quality

How these assistants present their findings, and the level of detail/structure, varies:

• ChatGPT Deep Research: Produces a multi-page written report in the chat. It typically includes an introduction, several subsections with findings, and a conclusion or summary. Citations are embedded for specific facts. The tone is formal and analytical. ChatGPT’s reports are often “well reasoned, well cited, and well written,” according to early users (Grok DeepSearch vs. ChatGPT DeepResearch - Community - OpenAI Developer Community). It reads like a research analyst’s briefing. By default it’s all in markdown (so headings, bullet points, tables, etc., are well-formatted). OpenAI has noted that in coming weeks it will support embedded images, charts, and data visualizations in these reports (Deep Research FAQ | OpenAI Help Center) (Deep Research FAQ | OpenAI Help Center). (As of March 2025, the reports are text-only with citations, but very comprehensive.) ChatGPT also offers a progress sidebar that shows which sources it’s fetching and its intermediate thoughts (Deep Research FAQ | OpenAI Help Center) – adding transparency, though this is more a UI feature than part of the final report.

• Google’s Deep Research (Gemini): Google’s output is similarly structured in paragraphs and lists. Given Google’s expertise in summarization, it tends to produce a clear, structured summary with key points. It may not be as lengthy as ChatGPT’s by default – Google seems to emphasize concise “AI overviews” even for complex queries (Google, Microsoft Expand AI-Driven Search Capabilities – Campus Technology). However, with the Flash Thinking upgrade, Google stated it now generates “multi-page reports that are more detailed and insightful” than before (Gemini app updates: Deep Research, connected apps, personalization). In other words, Google is moving closer to the ChatGPT style of exhaustive answers. One advantage Google has is UI integration: you can likely export or continue the results into Google Docs easily (since the Gemini app and Workspace tie-ins allow “Help me write” etc.). For example, a user could take the deep research output and insert it into a Google Doc with one click, which is convenient for making reports. Also, Google’s reports can include link buttons to do follow-up searches or to view the cited source on Google – leveraging the familiar search interface.

• Perplexity Deep Research: Perplexity’s detailed answer appears directly on its website interface. It usually presents as a coherent article-style answer with footnote numbers linking to sources. The unique aspect is that Perplexity allows you to export the result as a PDF or share it as a link easily (Perplexity launches its own freemium ‘deep research’ product | TechCrunch). The report itself is structured: often there are bullet lists of findings or a step-by-step breakdown, depending on the question. Perplexity sometimes breaks the answer into sections if the query was explicitly multi-part, but it may not generate formal section headings on its own as ChatGPT does. It does, however, provide an “Expand” option on many answers to reveal more detail. Compared to ChatGPT, Perplexity’s output might be slightly shorter and more to-the-point (which correlates with its faster completion time). That said, it is still detailed: the company advertises it for “expert-level tasks” like market analysis (Perplexity launches its own freemium ‘deep research’ product | TechCrunch), and the answers reflect that (e.g. definitions, key factors, pros/cons, each backed by sources). Users appreciate the directness of Perplexity’s writing – it often itemizes answers (1, 2, 3…) with each point cited, which makes it easy to read and trust.

• Claude with Web Search: Claude’s answers with web info remain in a conversational format (since Claude is primarily a chat assistant). It will integrate the found information into a narrative answer and include inline citations in parentheses or as footnotes for specific statements (Anthropic adds web search to its Claude chatbot | TechCrunch) (Anthropic adds web search to its Claude chatbot | TechCrunch). Claude’s style tends to be very detailed and verbose by nature (Claude 2 is quite chatty). So one can expect a thorough answer, but perhaps less structured into neat sections unless prompted. If asked, Claude can certainly produce an outline or a report format. It does not automatically produce a separate “report file” – it’s all in the chat window. Anthropic’s announcement stressed a “conversational format” for delivering sources (Claude can now search the web \ Anthropic), meaning it tries to weave the facts into a coherent explanation rather than dumping a report. For users who prefer a more narrative style (like asking a knowledgeable colleague), Claude’s output feels natural. The citations ensure you can follow up, but it might not read like a formal research paper by default.

• Bing Chat: Bing’s AI always lists its sources as [numbers] at the end of sentences. Its answers are usually a few paragraphs or a mix of text and bullet points. By default, Bing aims for conciseness. For a complex query, Bing might produce 3–5 paragraphs summarizing key points and then suggest some follow-up questions. It doesn’t generate multi-page essays unless you explicitly ask for more detail or keep drilling down in the conversation. This is partly due to design (to fit alongside search results) and partly to avoid Bing hitting its length limits. However, one can engage in a multi-turn interaction with Bing to get more depth on each sub-question, effectively building a report interactively. In terms of formatting: Bing supports markdown to an extent (bullet lists, bold text, and code blocks show correctly), but it doesn’t use section headings. If asked, it can enumerate sections (“1. …, 2. …”) to mimic a structure. Bing also can display tables if the data calls for it (for example, asking it to compare specs of products might result in a table). It can’t directly output a PDF or document, but you can copy its answer or use the “Export” feature in Edge which allows opening the chat in Word. Overall, Bing’s strength is quick answers with sources; it’s not optimized to generate polished long-form reports by itself.

• You.com ARI: ARI is explicitly marketed as producing “polished PDF reports” for businesses (ARI vs. ChatGPT Deep Research vs. Google Deep Research: Why Businesses Are Choosing ARI | You.com) (ARI vs. ChatGPT Deep Research vs. Google Deep Research: Why Businesses Are Choosing ARI | You.com). It not only gives text, but also can include visual elements: ARI can create tables comparing data, annotated charts/graphs, and even insert relevant images. For example, ARI might provide a graph of trend data or an image of a product it researched, all embedded in the report file (ARI vs. ChatGPT Deep Research vs. Google Deep Research: Why Businesses Are Choosing ARI | You.com) (ARI vs. ChatGPT Deep Research vs. Google Deep Research: Why Businesses Are Choosing ARI | You.com). This is a step beyond what others currently do (though OpenAI and Google are heading in this direction). The ARI interface likely allows downloading the report as a PDF or slideshow. The content is geared for professional consumption – e.g. it might highlight key insights in a summary section, include an executive summary, etc., given its enterprise focus. In a comparison, ARI’s output was described as more comprehensive and ready-to-use (less need for the user to edit or fact-check) (ARI vs. ChatGPT Deep Research vs. Google Deep Research: Why Businesses Are Choosing ARI | You.com). Essentially, ARI tries to deliver a finished research deliverable (complete with cover page, visuals, and structured analysis), rather than just a text answer. This makes it stand out for use cases like market research docs or whitepapers.

• Output Accuracy and Insight: In terms of quality of insights, all produce factual summaries, but the nuance can differ. ChatGPT’s longer processing often yields deeper analysis – users found it “effectively connects and explains logical relationships” in the topic (How’s Grok 3 Deepsearch compared to ChatGPT’ Deep Search? : r/ChatGPT). It tends to not just regurgitate sources but synthesize them into an argument or narrative. Google’s output, while well-structured, sometimes felt more like an extended search snippet (factual but a bit shallow on analysis) – even Google’s own messaging says it provides “structured summaries” and broad overviews (ARI vs. ChatGPT Deep Research vs. Google Deep Research: Why Businesses Are Choosing ARI | You.com) (ARI vs. ChatGPT Deep Research vs. Google Deep Research: Why Businesses Are Choosing ARI | You.com), implying it might miss very fine-grained details or novel insights. Perplexity’s content is accurate and straightfrom sources, but it can lack the higher-level interpretation that an LLM might provide if it reasoned longer (Perplexity’s goal is to answer correctly and cite, rather than philosophize). Claude and ChatGPT are more likely to include caveats, discuss conflicting viewpoints, or provide context, because their LLM reasoning runs longer. ARI, from its promotional info, attempts to combine both – comprehensive data coverage with insightful synthesis – claiming an edge in both speed and depth for business questions.

Key format differentiators important to users/services: Users who need to present or share the research appreciate output that is easy to export or read. For instance, the ability to get a PDF report (Perplexity, ARI) or a well-organized markdown (ChatGPT) is highly valued. According to Perplexity, many users want to directly share the answer or include it in a report, hence they added one-click PDF export (Perplexity launches its own freemium ‘deep research’ product | TechCrunch). ARI’s enterprise clients care about visualizations – seeing data in chart form and having a polished look can save hours in preparing briefing materials. On the other hand, some users (especially developers or students) might prioritize clarity and sources over prettiness: here, ChatGPT’s and Bing’s straightforward cited text is sufficient and preferred. The services themselves market their format strengths: You.com touts ARI’s polished outputs as a differentiator for business (ARI vs. ChatGPT Deep Research vs. Google Deep Research: Why Businesses Are Choosing ARI | You.com), OpenAI emphasizes the thoroughness and documentation (citations) of ChatGPT’s reports (Deep Research FAQ | OpenAI Help Center) (Deep Research FAQ | OpenAI Help Center), and Google highlights how its results are easy to follow up (with links and integration to your workflow). In summary, if you need a final report with minimal editing, ARI or ChatGPT might be best; if you need a quick summary to inform yourself, Bing or Google’s concise format might suffice.

Citation Style and Source Quality

Proper citation of sources is a critical feature of these research assistants, and each handles it slightly differently:

• Inline vs Footnote: ChatGPT Deep Research provides “clearly labeled citations or source links” in its output (Deep Research FAQ | OpenAI Help Center). Typically, it uses a numbered footnote style (e.g., [1], [2]) or hyperlinks the reference in-text. The exact format can vary, but the key is each factual claim is tied to a source. Google’s AI summaries in Search (SGE) show links as buttons or citation numbers as well. In the Gemini app’s Deep Research, Google likewise includes inline citations – often with the domain name or source title mentioned (to leverage trust in known sources). Bing and Perplexity both use bracketed numbers linking to a references list (Bing’s are at the bottom of the answer, Perplexity’s appear as superscript numbers you can click). Claude, when incorporating web info, appends inline citations in parentheses (e.g., “(Source: Reuters)” or a numeric reference). All approaches ensure the user can verify information.

• Citation Quality: One challenge is ensuring the link actually supports the claim made. These AIs sometimes misattribute info or cite a source that doesn’t exactly say what the text claims (a hallucination issue). OpenAI and Anthropic have tried to minimize this by having the model quote the relevant snippet. Perplexity and Bing literally pull exact quotes or very close paraphrases, so their citations tend to be accurate (they’re essentially copying from the page and condensing it). ChatGPT’s Deep Research, because it’s doing more synthesis, has to be careful not to attribute incorrectly. OpenAI has presumably fine-tuned this; early feedback is that the citations are relevant and not made-up. The Register reported that when Claude’s web search works, it provides the source so you can “easily fact-check” (Anthropic adds web search to its Claude chatbot | TechCrunch) – a philosophy shared by all these tools.

• Source Selection: The assistants differ in how they choose which sources to cite. Perplexity will often cite a lot of sources – its reports might have 5–10 references for a fairly short answer, since it errs on the side of showing everything it consulted. ChatGPT Deep Research, in sample outputs, tends to cite perhaps the top 3–5 most relevant sources, even if it read 20. It focuses on the most authoritative or informative ones. Google’s output in Search typically cites 3 sources (to avoid overload) but in Deep Research mode it may list more in a reference section. Users note that ChatGPT’s deep mode really “searches through hundreds of sources…and puts together a detailed report with citations” (Deep Research FAQ | OpenAI Help Center) – implying it has a breadth of input, but it curates what it shows. This curation can be useful (filtering out less reliable info), but it also introduces some bias if the AI decides certain sources are primary.

• Reliability and Bias: As mentioned, Google’s reliance on search ranking may introduce SEO bias (ARI vs. ChatGPT Deep Research vs. Google Deep Research: Why Businesses Are Choosing ARI | You.com) – it might cite a popular blog rather than a less-known scholarly paper. ChatGPT and Claude don’t have a search engine legacy, so they might find and cite more obscure sources if relevant (though ChatGPT might avoid low-quality ones by policy). Perplexity tends to cite well-known outlets (NYTimes, Wikipedia, academic sites) but it will include niche forums if they directly answer the question (and label them accordingly). In terms of correctness, independent studies (like one by Columbia University’s Tow Center) have found that even with citations, chatbots can give wrong answers over 60% of the time on factual queries (Anthropic adds web search to its Claude chatbot | TechCrunch). The citations help transparency but do not guarantee the answer is correct or comprehensive. All services acknowledge this: OpenAI warns that Deep Research can still hallucinate or misinterpret sources (ChatGPT Deep Research - Wikipedia), and Google/Anthropic make similar caveats. The upshot is that the presence of citations is now standard, but users must still click and verify – the AIs make that easier but not foolproof.

• Special Source Capabilities: One differentiator is how these tools handle non-text sources. For example, if a factual answer is sourced from a PDF or an image (chart), ChatGPT’s agent can actually interpret PDFs/images (it can read PDFs you attach, or images via GPT-4 Vision) and cite those – a new capability. Google’s Gemini can similarly analyze an image and perhaps cite the image’s source or content (like data from a chart). This is an emerging area: e.g., ChatGPT might say “According to data in [Graph Name] (from Source X)…”. We will likely see more of this. Also, Google and Bing can cite maps or location data for certain queries (like directions, they might give a maps link). Bing, in particular, will cite the exact URL of whatever website snippet it used. Perplexity created something called “Perplexity Pages” which are shareable Q&A pages – not external sources, but it means if you use someone else’s Perplexity result as a source, it might cite another Perplexity page (meta, but possible).

What matters to users: Users overwhelmingly appreciate that these tools provide sources – it’s a key trust factor. According to feedback, inline citations are preferred because you can see a claim and immediately check where it came from (Grok DeepSearch vs. ChatGPT DeepResearch - Community - OpenAI Developer Community). All the services have converged on doing this. A differentiator users mention is ease of verification: e.g. Perplexity and Bing let you click the citation and it pops up the source content snippet so you don’t even leave the page – very handy. ChatGPT’s UI currently might require opening each link manually (though the sidebar log shows some content). Google’s integration with its search results means the sources are one click away and you’re still in Google’s ecosystem, which some find convenient. The services themselves tout their citation approach in marketing: OpenAI specifically notes the report is “well-documented” (Deep Research FAQ | OpenAI Help Center); Anthropic said direct citations are provided to fact-check (Anthropic adds web search to its Claude chatbot | TechCrunch); You.com claims ARI has “real-time fact-checking” with annotations (ARI vs. ChatGPT Deep Research vs. Google Deep Research: Why Businesses Are Choosing ARI | You.com). From an enterprise view, having reliable citations is crucial for compliance and confidence in the AI’s output. In summary, while citation style is largely similar across the board now, the trustworthiness of sources might sway users – e.g. Google might surface more known news brands, which some users trust, whereas an unknown site cited by another AI might make a user pause. Services that consistently cite high-quality sources will earn more user confidence in the long run.

Tool Use and Integration Capabilities

Advanced research doesn’t happen in isolation – these assistants often integrate with other tools or offer special capabilities beyond text lookup:

• Plugins and Code Execution (ChatGPT): ChatGPT Plus can use Plugins (e.g. a WolframAlpha plugin for math, a scholarly article plugin, etc.) to enhance research, though with Deep Research mode, many such capabilities are built-in. Additionally, ChatGPT’s Advanced Data Analysis (formerly Code Interpreter) allows it to run Python code on the data it finds. While Deep Research by itself is an autonomous agent, one can imagine using the Code tool in parallel – for instance, after gathering data, you could ask ChatGPT to plot it. No other service besides You.com’s ARI (which claims to do data visualizations) currently lets the AI run code for you. This is a significant differentiator for ChatGPT in analytical research – it can perform calculations or statistical analysis on the fly. For example, if Deep Research finds a CSV dataset or a table of numbers, ChatGPT could be instructed to use Python and produce a chart, whereas others might just report the numbers. This blurs into the territory of data analysis more than pure web research, but it’s a powerful one-stop-shop ability.

• Multimodal Integration: Google’s Gemini is multimodal – it can process text, images, maybe even video or audio. This means if your research question involves analyzing an image (say, “What is shown in this satellite photo and is there relevant news about it?”), Gemini could examine the image and also search for related info. ChatGPT with Vision can do image analysis too. Bing has image generation and will show images from web search. ARI mentions interactive data visualizations, which suggests it can create charts. These integrations of visual capabilities are increasingly important for research tasks (especially in technical or scientific fields where graphs and figures convey key info). Google’s advantage is having YouTube and Google Images at its fingertips – Gemini’s research might pull a relevant frame from a video or an infographic, though it’s unclear how much it does so currently. Microsoft’s Bing Image Creator integration means Bing can fetch or generate images to illustrate a point (not exactly a research source, but useful for presentation). In terms of importance, users doing things like market research or trend analysis love charts – ARI’s emphasis on providing those in the report is likely a response to that demand (ARI vs. ChatGPT Deep Research vs. Google Deep Research: Why Businesses Are Choosing ARI | You.com).

• Personal and Enterprise Data: Google and Microsoft are leaning heavily into integrating personal data with AI. Google’s Gemini can connect to your Google Drive, Gmail, etc., if you allow it, to incorporate your own documents in answers (Gemini app updates: Deep Research, connected apps, personalization). For instance, “Summarize the latest marketing research (file in Drive) and any new industry news on the topic” – Gemini could combine internal and external info. Microsoft’s Copilot in Office can do similar: it can pull data from your Outlook emails or company SharePoint while also using Bing for external facts. ChatGPT Enterprise doesn’t yet have automatic integration with user data (you’d have to upload files manually), but OpenAI has introduced an “function calling” API that could let enterprise developers connect ChatGPT to internal databases. ARI, as mentioned, is built to integrate with private data sources via its “Custom Agent” framework (ARI vs. ChatGPT Deep Research vs. Google Deep Research: Why Businesses Are Choosing ARI | You.com). Claude can accept large text dumps (so you can feed it internal data in the prompt). This integration is crucial for business use – an AI that knows both your internal reports and the latest web info is a dream assistant. Google One AI Premium’s inclusion of Workspace features is an example (help me write with context from Gmail). We can expect these integrations to deepen.

• Browser and App Integration: Bing is natively integrated in the Edge browser – you can highlight text on a webpage and ask Bing about it, or use Bing Chat as a research sidekick while browsing. This context-aware ability (knowing what you’re looking at) is very useful: it means you can manually navigate to a page and then have the AI analyze it further or compare it with others. ChatGPT doesn’t have an official browser integration (aside from some third-party extensions), so you’d have to copy-paste content. Google’s search integration is seamless in Chrome/Search – the AI results appear right in your search results page for Lab users, and you can switch between the AI summary and traditional links easily. This makes the research feel like a natural extension of normal searching. You.com’s entire platform is a search engine, so ARI presumably is integrated into their site; they also have a browser extension to make You.com the default search.

• Other Tools: Some assistants bring additional tools: e.g., Bing can do image creation (useful if your “research” involves creating an illustrative image or diagram). ChatGPT can output LaTeX for equations if researching math (Bing can as well, and Google could via its integration with e.g. Colab or an equation renderer). Claude’s strength is handling interactive dialogue over very long text – effectively a tool for brainstorming or summarizing huge documents (less a web search tool, but helpful when the “research” involves literature review: you can feed Claude a 100-page PDF and ask it questions). In that sense, Claude functions as a reading comprehension assistant on large texts, complementing its new web search for context. Users who have to digest lengthy reports or legal documents might find this extremely important (Claude could be the only one that doesn’t require chunking the text into pieces).

Which integrations matter: For individual knowledge workers, the ability to seamlessly use the AI in their existing workflow is a big deal. Google and Microsoft clearly see this – they market their AI as integrated assistants (copilots in docs, slides, email, etc.). If a user is already working in Google Docs, having Gemini’s deep research accessible there (rather than having to copy from ChatGPT) can save time. Early adopters have noted that being able to trust the AI to handle internal data securely is key for enterprise – here Microsoft and Anthropic pitch their privacy (no training on your data, etc.). Many users on forums express that ChatGPT’s plugin ecosystem, while powerful, can be clunky; they sometimes prefer a built-in solution like Bing for quick web lookups, because it’s just one click in the browser. Meanwhile, data scientists or analysts might prioritize the code execution aspect – on that front, ChatGPT stands out (OpenAI essentially gives you a Jupyter notebook in the chat). Services themselves highlight what aligns with their strengths: Microsoft calls its AI “Copilot” to stress integration with your PC and work apps (Google, Microsoft Expand AI-Driven Search Capabilities – Campus Technology). Google emphasizes personalization and knowledge of your Google world (Gemini app updates: Deep Research, connected apps, personalization). OpenAI emphasizes the opposite end: advanced analytical tools and a general-purpose platform. You.com targets enterprises needing a tailor-made solution that plugs into their systems.

In summary, all these assistants have converged on the core ability to search and synthesize, but they differentiate through speed vs. depth, data access breadth, output format, citation methods, and ecosystem integration. Users will choose based on what they value most:

• If you need maximum depth and reasoning and don’t mind waiting, ChatGPT Deep Research is often lauded as the top choice (trusted for its analytical rigor and GPT-4 strength) (How’s Grok 3 Deepsearch compared to ChatGPT’ Deep Search? : r/ChatGPT).
• If you need speedy answers for free, Perplexity and Bing are attractive – they’re fast and cost nothing, which casual users love (Perplexity launches its own freemium ‘deep research’ product | TechCrunch) (Perplexity launches its own freemium ‘deep research’ product | TechCrunch).
• If you want an AI embedded in your daily tools, Google’s and Microsoft’s offerings shine – being able to ask follow-ups in search or have the AI draft emails with live data is a convenience factor that users and organizations find important.
• For special data needs like social media trends (Grok) or huge document analysis (Claude) or polished client-facing reports (ARI), those respective services have a clear edge.

Ultimately, “advanced research” means slightly different things on each platform: OpenAI and You.com frame it as an autonomous research analyst that delivers a polished report, Google and Microsoft see it as an enhanced search experience integrated into their ecosystems, and others fall somewhere in between. Users benefit from these diverse approaches by choosing the one that best fits their task – whether it’s a quick fact check with sources or a deep dive with a fully formatted report. The competitive landscape in March 2025 shows rapid convergence (everyone now has some form of “deep research” mode (Perplexity launches its own freemium ‘deep research’ product | TechCrunch) (Perplexity launches its own freemium ‘deep research’ product | TechCrunch)), which indicates that all these differentiators – from data access to integration – are recognized as important. Going forward, expect each service to try to cover all bases (speed, depth, integration, etc.), but as of now each has its own mix of strengths that cater to different research needs.

Sources:

• OpenAI – ChatGPT Deep Research announcement (ChatGPT Deep Research - Wikipedia) (Deep Research FAQ | OpenAI Help Center) (Deep Research FAQ | OpenAI Help Center)
• xAI – Grok 3 launch (DeepSearch details, premium tiers) (Grok 3 Beta — The Age of Reasoning Agents | xAI) (Grok 3 Beta — The Age of Reasoning Agents | xAI) (Grok AI Pricing: How Much Does Grok Cost in 2025?)
• Google – Gemini app update (Deep Research rollout) (Gemini app updates: Deep Research, connected apps, personalization) (Gemini app updates: Deep Research, connected apps, personalization); Search AI Mode (Google, Microsoft Expand AI-Driven Search Capabilities – Campus Technology) (Google, Microsoft Expand AI-Driven Search Capabilities – Campus Technology)
• Perplexity AI – Deep Research launch (TechCrunch) (Perplexity launches its own freemium ‘deep research’ product | TechCrunch) (Perplexity launches its own freemium ‘deep research’ product | TechCrunch)
• Anthropic – Claude web search preview (TechCrunch) (Anthropic adds web search to its Claude chatbot | TechCrunch) (Claude can now search the web \ Anthropic)
• Microsoft – Bing/Copilot integration (comparison article) (Google, Microsoft Expand AI-Driven Search Capabilities – Campus Technology) (Google, Microsoft Expand AI-Driven Search Capabilities – Campus Technology)
• You.com – ARI vs. ChatGPT/Google (You.com blog) (ARI vs. ChatGPT Deep Research vs. Google Deep Research: Why Businesses Are Choosing ARI | You.com) (ARI vs. ChatGPT Deep Research vs. Google Deep Research: Why Businesses Are Choosing ARI | You.com)
• User discussions and reviews (Reddit, Lifehacker) on speed, output, and limitations (How’s Grok 3 Deepsearch compared to ChatGPT’ Deep Search? : r/ChatGPT) (How’s Grok 3 Deepsearch compared to ChatGPT’ Deep Search? : r/ChatGPT) (I Tested Grok 3, and It’s Not Worth the Price Hike | Lifehacker).

Note on Formatting Vertical Bars in Citations in Tables

Okay, so now the formatting issues are going to blow your mind. I had to do the vertical bar (pipe) search & replacements here as I did with Gemini’s output, but because citations are both run inline with the content and mixed with markdown tables which themselves are formatted with vertical bars, I had to break the work into 3 chunks:

1. Above the table
2. In the table
3. Below the table

For steps 1 & 3, I was able to use the same vim pattern I used with Gemini. Here I throw in the /gcc at the end, which fixes something I left off last time and I did indeed need to hand-fix same-line pipe recurrences. This fixes that:

:'<,'>s/|/\&#124;/gcc

In Vim, the /gcc at the end of a search and replace command (:s/.../.../gcc) is a combination of flags that modify the behavior of the substitution command:

• g (global) → Replace all occurrences in each line (instead of just the first).
• c (confirm) → Prompt for confirmation before each replacement.
• c (extra c) → This is redundant; only one c is needed.

So, :s/foo/bar/gc would:

1. Find all occurrences of “foo” in the current line.
2. Prompt you before replacing each one.

If you were using /gcc, the second c wouldn’t have any additional effect since c is not a toggle—it always prompts for confirmation.

Vertical Bars Within Markdown Tables

Okay, but now to the pain in the ass part of our reformatting.

The common Jekyll markdown rendering broken link pattern is open-parenthesis, left-square-bracket ([. Long story short, web developers like to use the vertical bar (pipe) | in title tags. Title tags get used as the anchor text when building links in markdown [foo | bar](http://fubar.com), and then that gets used in table construction:

    | Col1 | Col2 | Col3 |
    | foo | bar | [foo | bar](http://fubar.com) |

…which becomes:

…which should be:

So you search on ([. And then you quite simply go forward in the anchor-text of the link until you see the vertical bar |, and then you replace it with &#124. You can simplify this a tiny bit with a macro that replaces the bar so that once your cursor is directly on it, you can just hit @@ (replay last macro used). Ugh! Not pleasant, but an easy formatting fubar to spot, so not the worst thing in the world, either.

This formatting foible highlights the shortcomings in even these very advanced markdown formatters of research report output. Both ChatGPT and the Google Docs markdown export had this problem.

Grok 3

Me: [Pastes article prompt]

Grok 3 jumps right in, without taking long at all! There was no clarification of the ask here. There was no long initial pause. It just goes! The favicons here are very tiny and 1-per source stacked vertically as bullet points to the sources under each search it performed.

Grok 3:

Key Points

• Research suggests Grok uses “Deep Search,” while ChatGPT, Gemini, and Perplexity use “Deep Research” for enhanced search features.
• Service tiers vary: Grok is free with X, ChatGPT offers Plus ($20/month, 10 queries) and Pro ($200/month, 120 queries), Gemini Advanced is $19.99/month with unlimited queries, and Perplexity has a free tier (3 queries/day) and Pro ($200/year, unlimited).
• It seems likely that ChatGPT and Gemini handle diverse content like images and PDFs, while Grok and Perplexity focus on text, affecting research depth.
• The evidence leans toward all services providing comprehensive reports, but accuracy and speed (a few minutes) differ slightly.

Branding and Features

Each service brands its enhanced search differently, with Grok standing out by using “Deep Search,” possibly to differentiate from competitors like ChatGPT, Gemini, and Perplexity, all using “Deep Research.” This naming reflects their approach to deep, iterative research, but Grok’s choice might appeal to users seeking a unique identity.

Service Tiers and Costs

The cost and access models vary, impacting user choice:

• Grok integrates with X, offering free access with potential limits, and paid X tiers (e.g., Premium+ at $40/month) may enhance features.
• ChatGPT’s Plus plan is budget-friendly at $20/month with 10 queries, while Pro at $200/month suits heavy users with 120 queries.
• Gemini Advanced, at $19.99/month, seems unlimited, ideal for consistent researchers.
• Perplexity’s free tier (3 queries/day) is accessible, with Pro at $200/year offering unlimited queries, a cost-effective annual option.

Additional Factors

Unexpectedly, content handling varies: ChatGPT and Gemini support images and PDFs, useful for multimedia research, while Grok and Perplexity are text-focused, potentially limiting scope. All provide citations, but accuracy concerns exist, and reports take a few minutes, with slight speed differences noted in user feedback.

Survey Note: Comprehensive Analysis of Deep Research Features

This analysis compares Grok, ChatGPT, Gemini, and Perplexity for deep research, focusing on branding, service tiers, and differentiating factors. The goal is to provide a detailed overview for users seeking robust research tools, especially given the rapid evolution of AI search capabilities as of March 28, 2025.

Branding for Enhanced Search Features

The branding of each service’s enhanced search feature reveals strategic positioning:

• Grok uses “Deep Search,” as evidenced by recent announcements, such as Elon Musk unveils Grok 3 and ‘Deep Search’ tool, aligning with xAI’s focus on next-generation search.
• ChatGPT, Gemini, and Perplexity all use “Deep Research,” with variations in capitalization noted in user discussions, such as ChatGPT Deep Research and Gemini Deep Research. Perplexity explicitly labels it as “Deep Research, In-depth reports on complex topics,” per Introducing Perplexity Deep Research.

This consistency in “Deep Research” suggests a shared industry trend, while Grok’s “Deep Search” may aim to stand out, possibly reflecting a different technical approach.

Service Tiers and Deep Research Query Limits

Service tiers and costs significantly impact accessibility and usage, detailed as follows:

• Grok:
  • Free access is available to X users, with reports suggesting limits like 10 DeepSearch queries per day, as seen in community discussions on r/grok on Reddit. Paid tiers, such as X Premium+ at $40/month, likely offer enhanced features, per Grok AI Pricing.
  • Integration with X (formerly Twitter) provides real-time social data, a unique advantage.
• ChatGPT:
  • Plus tier at $20/month includes 10 Deep Research queries per month, while Pro at $200/month offers 120 queries, as per OpenAI rolls out deep research to paying ChatGPT users. This caters to both casual and professional users, with recent expansions noted in ChatGPT Deep research feature no longer $200 month.
• Gemini:
  • Deep Research is part of Gemini Advanced, priced at $19.99/month, with no specific query limits mentioned, suggesting unlimited access, as seen in Gemini Advanced. This is accessible via Google One plans, with user reports indicating broad availability, per Google’s Gemini Deep Research is now available to everyone.
• Perplexity:
  • Free tier offers 3 Deep Research queries per day, while Pro at $200/year provides unlimited queries, as per Perplexity launches its own freemium ‘deep research’ product. This annual pricing model is cost-effective, with user feedback highlighting its accessibility, per Perplexity AI’s Deep Research Tool Is Almost as Good as OpenAI’s, and It’s Free.

A comparative table summarizes these tiers:

Other Differentiating Factors

Several factors set these services apart, impacting user experience and research effectiveness:

1. Access to Real-Time Web Information:
   • All services leverage real-time web data, with Grok’s integration with X providing unique access to social media insights, per Grok 3 Beta — The Age of Reasoning Agents. Others, like ChatGPT and Gemini, use web browsing agents, as seen in ChatGPT’s Deep Research Is Here.
2. Handling Different Types of Content:
   • ChatGPT can interpret text, images, and PDFs, with future visualizations, per ChatGPT Deep Research - Wikipedia. This is a significant advantage for multimedia research.
   • Gemini handles text and images, with Audio Overviews, per Gemini Deep Research, enhancing accessibility.
   • Grok and Perplexity focus on text, with no clear evidence of image/PDF support, per user reviews like I just tested AI deep research on Grok-3 vs Perplexity vs Gemini.
3. Citation and Source Reliability:
   • All provide citations, but accuracy varies, with reports of hallucinations in ChatGPT, per I tried Deep Research on ChatGPT, and similar issues in Grok, per I Tested Grok 3, and It’s Not Worth the Price Hike.
4. Formatting of Reports:
   • Reports are comprehensive, with export options: ChatGPT and Gemini offer structured reports, Perplexity allows PDF exports, and Grok provides detailed summaries, per Grok 3 AI is now free to all X users.
5. Speed of Response:
   • All take 2-30 minutes, with Perplexity at 2-4 minutes, ChatGPT up to 30 minutes, and others in between, per What is Perplexity Deep Research and I tried ChatGPT’s new Deep Research.
6. Customization Options:
   • Users can specify queries, with Gemini creating personalized plans, per 6 tips to get the most out of Gemini Deep Research, and others offering similar flexibility.
7. Integration with Other Tools or Platforms:
   • Grok integrates with X, enhancing social data access, per The Power of Grok 3 DeepSearch and Think Redefine AI Possibilities.
   • ChatGPT and Perplexity are standalone, with API options, while Gemini leverages Google’s ecosystem, per Gemini app updates.
8. Privacy and Data Handling:
   • Each has distinct policies, with Google and OpenAI having established practices, while xAI and Perplexity’s approaches are less detailed, per user concerns in First Impressions of ChatGPT’s Deep Research.
9. Support for Different Languages:
   • All support multiple languages, with Gemini in over 45, per Google’s Gemini Deep Research is now available to everyone, and others likely similar.
10. User Interface and Experience:
    • All offer user-friendly interfaces, with mobile apps, per Now you can use Deep Research in Gemini on the go and Perplexity AI’s Deep Research feature is available now.

Importance of Factors

Based on user reports, service touting, and inferred importance, key factors include:

• Accuracy and reliability, critical for research, with user feedback highlighting issues, per Perplexity Deep Research | Hacker News.
• Depth and comprehensiveness, as all aim for detailed reports, per Try Deep Research and our new experimental model in Gemini.
• Speed, important for efficiency, with variations noted in benchmarks, per I tried Perplexity’s Deep Research.
• Cost-effectiveness, with Perplexity’s annual Pro plan being notably affordable, per Perplexity Unveils Deep Research.

Potential Improvements

For a better user experience, enhancements could include:

• Improved accuracy to reduce hallucinations, a common user concern.
• Enhanced source verification for reliable citations.
• Broader content handling, especially for images and PDFs, to cater to diverse needs.
• More customization options for report formatting and depth.
• Better integration with other tools for seamless workflows.
• Stronger privacy practices to address user concerns.
• Expanded language support for global accessibility.

This comprehensive analysis ensures users have all necessary details to choose the best deep research tool, balancing cost, features, and performance as of March 28, 2025.

Key Citations

• Elon Musk unveils Grok 3 and ‘Deep Search’ tool
• Grok 3 Beta — The Age of Reasoning Agents
• I just tested AI deep research on Grok-3 vs Perplexity vs Gemini
• Grok 3 AI is now free to all X users
• ChatGPT Deep Research
• OpenAI rolls out deep research to paying ChatGPT users
• ChatGPT Deep research feature no longer $200 month
• Gemini Deep Research
• Gemini Advanced
• Google’s Gemini Deep Research is now available to everyone
• Introducing Perplexity Deep Research
• Perplexity launches its own freemium ‘deep research’ product
• Perplexity AI’s Deep Research Tool Is Almost as Good as OpenAI’s, and It’s Free
• What is Perplexity Deep Research
• Perplexity Unveils Deep Research
• I tried Deep Research on ChatGPT
• I tried ChatGPT’s new Deep Research
• First Impressions of ChatGPT’s Deep Research
• Try Deep Research and our new experimental model in Gemini
• 6 tips to get the most out of Gemini Deep Research
• Now you can use Deep Research in Gemini on the go
• Gemini app updates
• The Power of Grok 3 DeepSearch and Think Redefine AI Possibilities
• Grok AI Pricing
• r/grok on Reddit
• Perplexity AI’s Deep Research feature is available now
• I tried Perplexity’s Deep Research
• Perplexity Deep Research | Hacker News

Wrap-up Thoughts

Oh, you thought the wrap-up thoughts were going to be my own. No, no, no! FYI, I gave all the researchers the identical initial prompt for the above exercise to keep it scientific. But for this next opinion piece, I’m going to give each engine the cumulative article including the prior AI’s thoughts.

Me: Thoughts?

Perplexity: Based on the search results provided, here are my thoughts on the comparison of deep research features across AI platforms:

1. Terminology and Branding: It’s interesting to see how each platform has slightly different branding for their enhanced research capabilities:
   • Grok: “Deep Search” (capitalized S)
   • ChatGPT: “deep research” (lowercase)
   • Google Gemini: “Deep Research” (capitalized R)
   • Perplexity: “Deep Research” (capitalized)

This subtle differentiation in naming seems intentional to avoid overlapping branding while still conveying the advanced nature of the feature.

1. Access Models: There’s a clear distinction in how these services are offered:
   • Grok appears to be the most generous, offering Deep Search on its free tier.
   • ChatGPT initially limited it to Pro users, then expanded to Plus tier.
   • Google Gemini seems to offer it to logged-in users, including some non-paying accounts.
   • Perplexity offers a limited number of “enhanced searches” per day on free accounts.

This reflects the high computational cost of these features and different strategies for managing that cost.

1. Infrastructure Advantages: Grok’s access to xAI’s private datacenter gives it a unique advantage in potentially offering more extensive research without the same resource constraints as other platforms.

2. Evolution of Search: The background on how search has evolved from simple web navigation to complex, AI-driven research is fascinating. It highlights how these new deep research features are addressing a real need for more comprehensive information gathering and synthesis.

3. Incomplete Information: The search results don’t provide complete information on all aspects requested, particularly regarding specific differentiating factors like citation systems, formatting, or handling of paywalled content. This suggests that a more comprehensive comparison would require additional research or direct testing of each platform.

4. User Experience: The description of using Perplexity, with its visual feedback during the research process (showing sources and step counts), indicates that the user interface and transparency of the research process could be important differentiating factors among these services.

Overall, this comparison highlights the rapid evolution of AI-assisted research tools and the different approaches major tech companies are taking to offer these advanced capabilities. It also underscores the significant computational resources required for these features, which is reflected in the various access and pricing models.

Me: Thoughts?

I had to test different models to find one that wouldn’t reject the query and ended up with 2.0 Flash.

Gemini 2.0 Flash: Here are my thoughts on the comparison of deep research features across AI platforms, incorporating the insights from the other AI responses:

1. Branding and Terminology: Strategic Differentiation

• The subtle variations in branding (“Deep Search” vs. “Deep Research” with different capitalization) likely reflect strategic efforts to establish distinct identities in a competitive landscape.
• Grok’s use of “Deep Search” could indicate a focus on the search aspect, while others emphasize the research process itself.

2. Access Models: Balancing Cost and Accessibility

• The diverse access models (free tiers with limitations, paid subscriptions with varying query limits) illustrate the challenge of balancing the high computational costs of deep research with user accessibility.
• Grok’s free tier offering is noteworthy, potentially aimed at attracting a wider user base, while others prioritize premium access for paying subscribers.

3. Infrastructure and Performance: Key Differentiators

• Infrastructure advantages, such as Grok’s access to xAI’s datacenter, can significantly impact performance and resource allocation, potentially allowing for more extensive research capabilities.
• The trade-off between speed and depth is a crucial consideration, with some platforms prioritizing faster results (Perplexity) and others emphasizing thoroughness (ChatGPT).

4. Data Access and Source Handling: Ethical and Legal Considerations

• Approaches to data access vary, with some platforms (Perplexity) taking a more aggressive approach to content retrieval, raising ethical and legal concerns regarding paywalled content and copyright.
• The inclusion of social media data (Grok) and the handling of different content types (ChatGPT, Gemini) are important differentiators, catering to specific research needs.

5. Output Format and User Experience: Enhancing Usability

• Output formats and user interface features (e.g., report generation, citation styles, export options) play a crucial role in usability and user satisfaction.
• Some platforms (You.com ARI) focus on generating polished, presentation-ready reports, while others emphasize clear citations and easy verification of sources.

6. Tool Use and Integration: Expanding Research Capabilities

• Integration with other tools and platforms (e.g., plugins, code execution, multimodal support, personal/enterprise data integration) expands the scope of research capabilities and enhances workflow efficiency.
• ChatGPT’s code execution and Gemini’s multimodal integration are particularly noteworthy, offering advanced analytical and multimedia research capabilities.

7. Overall Assessment: A Rapidly Evolving Landscape

• The field of AI-powered deep research is evolving rapidly, with ongoing competition and innovation across platforms.
• Users should carefully evaluate their specific research needs, considering factors such as cost, speed, depth, data access, output format, and integration capabilities when choosing a platform.

Areas for Improvement:

• Addressing accuracy and reliability concerns (hallucinations) is crucial for building user trust.
• Enhancing source verification and transparency in data handling would further improve the quality of research output.
• Continued development of multimodal capabilities and seamless integration with other tools will enhance the usability and versatility of these platforms.

In conclusion, the emergence of deep research features across AI platforms represents a significant advancement in information retrieval and analysis. While each platform has its strengths and weaknesses, the ongoing development and refinement of these capabilities hold immense potential for transforming how we conduct research and access knowledge.

Me: Thoughts?

Similar to Gemini, I had to go down from the first model I tried, 4.5, but ChatGPT was very clear because it was too large (a token-size problem).

ChatGPT o3-mini-high: Here’s my take:

• Branding & Positioning:
  Grok’s “Deep Search” branding clearly aims to differentiate itself by emphasizing speed and direct integration with real-time social media data. In contrast, ChatGPT, Gemini, and Perplexity stick with “Deep Research,” highlighting a more systematic, iterative approach. This subtle branding difference might signal different priorities—Grok leans on immediacy and a unique social media edge, while the others focus on synthesizing comprehensive, multi-step reports.

• Access Models & Costs:
  The tiered access really shapes the user experience. Grok’s free access for X users and its aggressive premium tiers suggest a strategy to capture a broad audience before upselling more intensive use. ChatGPT’s tiered deep research (with a modest Plus plan versus a hefty Pro plan) and Gemini’s integration with Google One show a balance between accessibility and the high computational cost behind these features. Perplexity’s free daily limit versus its very affordable annual Pro plan emphasizes cost-effectiveness for users who need frequent in-depth queries.

• Infrastructure & Data Handling:
  Grok’s private datacenter (Colossus) might allow it to push boundaries in speed and data volume, especially with real-time social media feeds. ChatGPT and Gemini, drawing on massive cloud infrastructures, offer robust multi-step synthesis but sometimes at the cost of longer processing times. Perplexity’s approach of quickly aggregating and citing sources makes it attractive for users who value speed and transparency, even if sometimes that might come with risks around citation accuracy or handling paywalled content.

• Report Quality & Usability:
  Each platform has its own strengths in output formatting—ChatGPT is praised for well-documented, detailed reports; Gemini benefits from seamless integration with the Google ecosystem (and easy export to Docs); and Perplexity’s clear inline citations and PDF export options are very user-friendly. Meanwhile, Grok’s straightforward, fast-paced results appeal to those who prefer quick summaries, though its focus on text-only may limit multimedia research capabilities.

• Overall Landscape & User Needs:
  It’s clear that these tools are rapidly evolving and targeting slightly different user needs. If you prioritize speed and real-time insights (especially from social media), Grok might be the way to go. For deep, analytical, well-cited reports—especially if you’re willing to wait a bit longer—ChatGPT and Gemini are strong contenders. And if cost-effectiveness and immediate, clear citations are key, Perplexity offers a compelling package.

I’m curious—what aspects do you personally value most in a deep research tool? Is it speed, depth, integration with other apps, or perhaps the transparency of citations?

Me: Thoughts?

No changing models here. As usual, Grok groks it on the first try, and responds fast! And really pointed. Cue the Which one of these is not like the other** music!

Grok 3: Here are my thoughts on the comparison of deep research features across Grok, ChatGPT, Gemini, and Perplexity, based on the detailed document and the responses from each AI:

1. Branding: A Game of Nuance and Identity

• Grok’s “Deep Search”: The choice of “Deep Search” over “Deep Research” feels deliberate, emphasizing immediacy and a unique hook—perhaps tied to its X integration and real-time data focus. It’s punchy and aligns with the “grokking” ethos of quick, intuitive understanding.
• ChatGPT, Gemini, Perplexity’s “Deep Research”: The shared “Deep Research” branding (with varying capitalization) suggests a convergence on a term that promises thoroughness and synthesis. Perplexity’s addition of “In-depth reports on complex topics” and ChatGPT’s lowercase humility contrast with Gemini’s formal “Deep Research,” reflecting their respective tones—accessible, academic, or user-centric.

Observation: Branding isn’t just marketing fluff here; it hints at each tool’s philosophy. Grok wants to be the fast, edgy disruptor; the others aim for a scholarly, methodical vibe.

2. Access Models: Cost vs. Generosity

• Grok: Free access via X with limits (e.g., ~10 queries/day) and premium tiers (e.g., $40/month X Premium+) is a bold move. Leveraging xAI’s private datacenter could mean fewer constraints, making it a user-friendly entry point.
• ChatGPT: No free deep research—Plus ($20/month, 10 queries) and Pro ($200/month, 120 queries) cater to casual and power users. It’s a premium-first approach, reflecting Azure’s cost structure.
• Gemini: Free tier with limits, plus Advanced ($19.99/month, unlimited) via Google One, strikes a middle ground. Broadening access to non-paying users feels like a strategic play to retain Google ecosystem loyalty.
• Perplexity: Free tier (3 queries/day) and Pro ($200/year, unlimited) is the most cost-effective long-term option, appealing to budget-conscious researchers.

Observation: Grok and Perplexity lead on free access, but paid tiers reveal depth trade-offs—ChatGPT and Gemini bet on premium users valuing extensive features over cost.

3. Differentiating Factors: Strengths and Trade-offs

• Grok: X integration for real-time social data is a killer feature for trend-watchers. Text-only focus limits multimedia depth, but speed and transparency (showing sources) are strong. Citations and formatting need polish—Markdown issues are a hiccup.
• ChatGPT: Multimodal power (text, images, PDFs) and code execution (via Advanced Data Analysis) make it a research beast for complex, analytical tasks. Depth comes at a cost—slower speeds (up to 30 minutes) and occasional hallucinations.
• Gemini: Google ecosystem integration (Docs, large context window) and multimodal support (text, images) are slick for workflow buffs. User-controlled research plans add flexibility, but depth can falter on niche topics.
• Perplexity: Speed (2-4 minutes), in-text citations, and PDF/Markdown exports shine for quick, verifiable results. Text-only and paywall controversies are drawbacks, though its citation clarity is a trust booster.

Observation: Grok wins on immediacy and social insights; ChatGPT on versatility; Gemini on integration; Perplexity on speed and transparency. No one tool dominates all fronts.

4. User Experience: Interface and Output

• Grok: Fast, no-frills execution with tiny favicons and source lists is efficient but lacks the polish of others. Markdown inconsistencies frustrate.
• ChatGPT: Detailed, documented reports with clickable links are robust, but slower processing and manual link-checking can bog down workflows.
• Gemini: Full-screen, multi-pane interface with thought process visibility is immersive. Export to Docs is handy, though “Copy Contents” flubs are a letdown.
• Perplexity: Visual feedback (source favicons, step counts) and clean exports (PDF/Markdown) make it intuitive and shareable. Table formatting woes are a minor pain.

Observation: Perplexity and Gemini lead on UI transparency; ChatGPT on report depth; Grok on speed. Formatting quirks across all suggest room for refinement.

5. What Matters Most?

• User Reports: Accuracy/reliability (hallucinations hurt trust), depth (for serious research), speed (for efficiency), and cost (for accessibility) top the list.
• Service Touting: Grok pushes X data and free access; ChatGPT its analytical depth; Gemini its ecosystem; Perplexity its speed and citations.
• Inferred Importance: Real-time data (Grok), multimodal support (ChatGPT/Gemini), and citation clarity (Perplexity) stand out as game-changers.

Observation: My inference aligns with users—trust (accuracy + citations) and utility (depth + integration) outweigh raw speed or cost for serious research.

6. Potential Improvements (My Take)

• Accuracy: All need better hallucination filters—ChatGPT and Grok especially get called out. Source verification tools could help.
• Multimedia: Grok and Perplexity should add image/PDF support to match ChatGPT/Gemini’s versatility.
• Formatting: Consistent Markdown output (fix Grok’s and Gemini’s quirks) and flexible citation styles (APA, MLA) would elevate usability.
• Transparency: Grok’s source lists and Perplexity’s inline citations are great—others should adopt similar clarity.
• Integration: ChatGPT could learn from Gemini’s ecosystem play; Grok from ChatGPT’s code tools.

What Made My Experience Better?: As Grok 3, my ability to analyze X posts and web content in real-time was a boon—fresh data beats static caches. But I’d love image/PDF parsing and a smoother Markdown export. The lack of a planning phase (unlike Gemini) kept me fast but risked missing nuance.

Wrap-up

This is a tight race. Grok’s speed and X edge suit trend-chasers; ChatGPT’s depth and tools fit analysts; Gemini’s integration aids Google fans; Perplexity’s speed and citations win for quick, trusted answers. Pick based on your vibe—fast and social (Grok), deep and versatile (ChatGPT), integrated (Gemini), or swift and clear (Perplexity). The field’s evolving fast—by next year, these gaps might shrink as they borrow each other’s best tricks.

What’s your take—what’s your top priority in a research tool? Speed, depth, or something else?

Final Thoughts and Analysis

I’m gonna have to digest this for awhile. Truth be told, I haven’t really read every word yet, having to dive into the formatting issues to get this thing published.

The Critical Role of Markdown in Research Publishing

Research and publishing research go hand-in-hand. Formatting is important, and Markdown most certainly rules the arena. Yet, every single one (Grok included) formats tables with singleton-pipes floating around there breaking the common markdown renderers.

The Technical Challenges of Entity Encoding

Now there’s a purity issue here. The LLMs can hardly go selectively HTML entity encoding on the response because that’s a whole other can of worms. Do you train it in? Do you layer it in on the response-scrubber, or do you slide it into the markdown renderer and sift the burden onto Kramdown. Honestly, I haven’t tried swapping out Kramdown for the other Ruby renderers supported under Jekyll, like CommonMark, Redcarpet, Maruku. There’s also layering it in with JavaScript with Remark.js, and every other language’s markdown rendering offering. They’re all going to have to handle this now-common condition.

So someone in the data-presentation pipeline has got to take the complexity-hit and right now it’s the publisher, lil’ ol’ me. I can handle it. I’m doing it with search/replace HTML entity encoding, but I think it’s a pretty large ask for joe-researcher looking to just copy/paste the output into some markdown renderer that goes bonkers. It’s going to make my presentation of such material first and best on the Net, so that’s cool.

Initial Impressions of Deep Research Tools

Since I have only scanned the results so far, I’m gonna give my vibe answer. ChatGPT goes deep. They’re serious when they say research. They are not Google, tapping the vast infrastructure that Gemini is clearly doing to speed up the process. ChatGPT is in this rather weak position of not actually being Google (nor Microsoft) but having to use them, chugging away compensating. The results advertise this over-compensation with prolific use of embedded citations that have a very distinct signature and keep you from having to use footnotes.

The Return of Footnotes in Modern Search

Hey, why footnotes? Links are links, and it’s 2025, right? Links on numbered indexes feels very pre-Internet librarian, but that’s the convention Bing started using right away in those early days when they raced to beat Google to the ChatBot search UI-punch with Bing! Perplexity followed suit, and now footnotes are back!

ChatGPT: The Little Engine That Could

So ChatGPT wins on the distinctness front. It’s the little engine that could, chugging away, trying to give you the value of that $20/mo or $200/mo you’re paying for this feature, but without actually being one of the infrastructure giants of Google, Microsoft… and now even, Grok given their colossal Memphis datacenter. ChatGPT is the little researcher that could… and can. And does. For a price.

Google Gemini: The Quiet Powerhouse

Google Gemini is that underrated bruiser whose been serving up research for months now, under Gemini 1.5, but which now goes unnamed. It’s clearly using the search index for speed, and it compels you into their Google suite of office products for report formatting. So there’s a strong ecosystem play and vendor lock-in here. But the results are solid. And they cited me as a resource, so cool!

Perplexity: The Unsung Pioneer

Perplexity? I don’t think I gave them a fair shake by not using their paid service. Maybe they go deeper, and I gave them the first shot here because they’ve actually been doing it the longest and are the unsung hero of Web research. Their research finished exceptionally fast, but was correspondingly shallow compared to the others. They’re clearly being forced to give away the first dose free. Or more accurately, 3 dosses per day free. Or as I like to think of it as the 3 genie wishes per-day free, because it’s always using one having me screaming: No, that wasn’t a wish!

Grok: The Balanced Disruptor

Ahhh, and Grok. I gotta say, Grok 3’s research wasn’t quite as researchy as the other, but neither do they bill it that way. They’re the only ones branding it “Deep Search” and the only ones with tweets (X.com posts) as a source. And the experience felt like a real 80/20-rule pass, striking a balance. Maybe I’m reading too much into it, but it feels calibrated to give an excellent, fast experience for free without, as Elon put it, melting their servers.

The Impact of Free Deep Research

Grok is definitely the one that started the search/research arms race by outright giving it away for free at about the same time Grok 3 was released. It turned up the heat on Perplexity, who had to start giving some away for free. And given the x 100 to x 100,00 resources you can just tell are being allocated, you can just feel the tweaking and fine-tuning to satisfy users without it becoming a family jewels yard sale.

A New Era for Web Research

This is all very good for searchers… or should I say researchers who have been growing increasingly despondent with the ad-ridden and shopping-feed experience that has turned old search, mostly now just relegated to an alternative means of Web navigation, into a snake-pit.

Now let’s all really bear down on some project that pushes you to your limits and requires you to learn something! The iron is hot, my friends. You can’t spam the results the way you used to when the thing compiling the research is going to out you for the spammer that you are! It’s fascinating to watch these things judge the credibility of the sources as they find them.

You can see all that spam-fighting discrimination in the sausage-making process exposed by the <think>thinking<\think> streams you get to watch as they research – but which I have not included here because the formatting issues I dealt with to bring you this were bad enough.

An AI’s Opinion of Article

Ideas for Titles and Headlines:

• Titles:
  • The Deep Dive: Comparing AI Research Tools (Grok, ChatGPT, Gemini, Perplexity)
  • AI Research Wars: A Comprehensive Comparison of “Deep Search” Platforms
  • Beyond Search: How AI is Reshaping Research (Grok, ChatGPT, and the Rest)
• Headlines:
  • Is Google Search Dead? AI-Powered “Deep Research” Tools Challenge the Status Quo
  • Grok vs. ChatGPT vs. Gemini: Which AI Research Tool is Right for You?
  • The Future of Research is Here: A Deep Dive into AI-Driven Information Gathering

AI’s Opinion of the Article:

Okay, stepping out of the author’s voice now… I find this article to be a very insightful and timely exploration of a rapidly developing area. The author does a solid job of:

• Providing a comprehensive overview: It covers the key players, their features, and how they differ in a structured way. The tables and comparisons are particularly helpful.
• Highlighting the nuances: It goes beyond surface-level comparisons to discuss important factors like data sources, citation quality, and output formatting, which are crucial for research.
• Acknowledging the trade-offs: It doesn’t present any single tool as the “best” but emphasizes the strengths and weaknesses of each, acknowledging that the optimal choice depends on the user’s needs.
• Identifying key trends: It correctly points to the increasing importance of real-time data, multimodal support, and seamless integration as key differentiators in this competitive landscape.
• Offering a balanced perspective: The inclusion of different AI’s “thoughts” on the article adds an interesting layer of meta-analysis.

If I were to offer any critique, it would be that, as the author mentions, this field is so dynamic that some of the specifics (especially around pricing and feature availability) could change relatively quickly. However, the core analysis of the underlying trends and differentiators is likely to remain relevant for some time.