Deep Research Capabilities: Comparing ChatGPT, Perplexity, Grok, and Kompas AI

Artificial intelligence tools have become indispensable for research, but not all AI chatbots are equally adept at conducting in-depth, sustained research. Some excel at quick answers in a conversational format, while others are designed to dig through information and compile comprehensive reports. This analysis compares four prominent AI systems — OpenAI’s ChatGPT, Perplexity AI, xAI’s Grok, and Kompas AI — focusing on how each handles deep research tasks rather than general chatbot banter. We will introduce each AI in turn and evaluate their strengths and weaknesses in conducting thorough research. The goal is to provide a formal, balanced comparison that is useful to both everyday users and tech professionals looking for the right research assistant.

ChatGPT: Versatile Conversationalist, Limited in Sustained Research

ChatGPT is the most well-known AI assistant, celebrated for its fluent conversational abilities and broad knowledge base. It shines in general-purpose Q&A and creative tasks, but when it comes to sustained deep research, ChatGPT has notable limitations. In its standard mode, ChatGPT engages in quick back-and-forth dialogue, providing answers based on its trained knowledge or (if enabled) brief web queries. This makes it excellent for quick facts, explanations, or brainstorming, but less structured for lengthy investigations. As the documentation of a newer research-focused AI notes, ChatGPT primarily “focuses on quick question-and-answer interactions.” It does not natively provide sources for its statements, which means users must trust its answers or manually verify them — a drawback for academic or professional research where citations are crucial. Prolonged research using ChatGPT typically requires the user to guide the process step by step, asking follow-up questions and possibly feeding it relevant text, since ChatGPT by itself won’t automatically pursue multi-step research without prompting.

OpenAI has recognized these shortcomings and recently introduced a “Deep Research” feature for ChatGPT aimed at complex, multi-step tasks. Bundled in ChatGPT’s professional tier, this mode is marketed as a virtual research analyst that can autonomously search the web, compile information from numerous sources, and deliver a structured multi-page report. In essence, ChatGPT’s deep research mode tries to transform the chatbot from a quick answer engine into a full research assistant. It will ask clarifying questions about the user’s request, then browse hundreds of webpages, academic papers, and databases on its own, extract key information, organize it into a coherent report, and even cite its sources. The promise is enticing — OpenAI suggests it can do in minutes what might take a human researcher hours, potentially matching the capabilities of a trained analyst.

However, early evaluations indicate that ChatGPT’s deep research mode does not yet live up to the hype. While it does produce polished reports, testers have found serious flaws. According to journalists who tried it, the AI often misses important details, struggles with very recent information, and sometimes invents facts even while sounding confident. OpenAI itself acknowledges that the system can hallucinate or make incorrect inferences, albeit at a lower rate than the standard ChatGPT models. Another practical limitation is accessibility — as of now, the deep research feature is only available to ChatGPT Pro users in certain regions (initially U.S.) and comes at a steep price (around $200 per month for the Pro plan). This paywall and limited rollout mean that for the majority of users, ChatGPT is still used in its simpler form, without autonomous deep research capabilities.

In summary, ChatGPT’s strengths for research lie in its ease of use and general versatility. It’s superb for getting a quick overview of a topic or synthesizing information you provide to it. Its intuitive conversational interface makes it accessible to anyone. But its weaknesses for deep research are clear: it doesn’t inherently perform multi-step investigations or provide source references in standard usage. The new advanced research mode attempts to address those gaps by delivering structured, cited reports — yet it remains an exclusive feature with reliability issues to iron out. For most users and scenarios, ChatGPT remains a powerful general AI assistant, but not a specialized deep research tool. It often serves as a starting point, after which a more dedicated research system or manual checking is needed to ensure thoroughness and accuracy.

Perplexity: Quick Answers with Citations, Less Depth for Long Research

Perplexity AI takes a different approach by tightly integrating web search with an AI language model. From the moment you ask Perplexity a question, it performs a live internet search and uses the results to formulate its answer. The response you get isn’t just a confident paragraph — it comes with clickable citations for every factual claim. This design has made Perplexity popular among users who want quick answers backed by evidence. For example, if you inquire about a scientific fact or a current event, Perplexity will show the relevant sources (news articles, research papers, etc.) alongside its summary, allowing you to verify the information instantly. This focus on sourcing means Perplexity encourages a form of trust through transparency — a critical feature for research-oriented tasks.

In terms of deep research capabilities, Perplexity is strong but with a caveat. On one hand, it has demonstrated an impressive ability to handle complex queries by finding and integrating information from multiple sources. In a recent independent evaluation of AI research assistants, Perplexity excelled in research depth and data integration, outperforming other contenders on a series of complex topics. In fact, this analysis ranked Perplexity as a leader, noting its superior skill at weaving together insights on subjects like quantum computing and renewable energy, thereby delivering answers that were both precise and well-organized. This suggests that Perplexity’s search-driven approach can yield relatively in-depth answers, especially when compared to chatbots that rely only on a fixed training set. Another advantage is accessibility: Perplexity’s core features, including its so-called “deep research” mode, have been available to users for free, which has drawn praise in online communities. Unlike ChatGPT’s most advanced features hidden behind a paywall, anyone can use Perplexity to tackle detailed questions with source-backed answers, making it an attractive tool for students, journalists, and curious minds on a budget.

On the other hand, Perplexity is fundamentally designed for concise Q&A rather than producing long-form reports or carrying out prolonged investigative sessions. Each query yields an answer that might be a few paragraphs at most. If a user’s research question is broad or multifaceted, Perplexity might answer the initial question well, but it will not automatically break the task into sub-questions or compile a lengthy dossier — that responsibility still lies with the user to ask follow-up questions. There is a beta “deep research” or “deep search” mode in Perplexity (especially for Pro subscribers) which attempts multi-step querying. Some early users, however, reported that this mode can be hit-or-miss. In certain cases, Perplexity’s detailed mode produced responses that were less useful — for example, recounting the steps it took (“I searched for X, then I read Y”) instead of providing new insights. Such feedback suggests that while Perplexity can gather information from many sources, synthesizing that into a truly cohesive, report-like answer is still a work in progress for the platform. It may return a list of facts or a summary that is accurate but somewhat shallow in analysis, especially on open-ended research tasks.

To put it succinctly, Perplexity’s strengths are in its speed, ease of use, and transparency. It’s arguably the best choice when you need a factual answer quickly and want to see where the information is coming from. It shines for targeted questions and fact-finding missions, and it held its own in comparisons on complex topics, showing it can integrate multiple sources when needed. Its weaknesses center on the depth and format of output. Perplexity is not built to give you a multi-page research report or a deeply structured analysis without considerable prodding. The conversation with Perplexity tends to stay focused on answering one question at a time. For sustained research projects — where you might need to explore a topic from many angles, gather dozens of sources, and refine the scope as you go — Perplexity will require you to manually drive that process, question by question. In summary, it’s an easy-to-use and reliable research assistant for quick inquiries, but it may fall short of a comprehensive research partner if your goal is to produce an extensive report or if you need an AI to proactively delve deeper beyond the initial query.

Grok: Real-Time Data and Synthesis at the Cost of Precision

Grok is the newest entrant among these tools, developed by Elon Musk’s startup xAI as a bold competitor in the AI chatbot space. Unveiled in late 2023, Grok distinguishes itself with a few unique traits. One is its connection to real-time information: Grok is integrated with Musk’s X platform (formerly Twitter) and other live data sources, giving it up-to-the-minute knowledge and the ability to pull in current content from the web. Another is its persona — Grok was intentionally designed to have a bit of a sense of humor and sarcasm, setting it apart from the typically neutral tone of ChatGPT or Perplexity. In Musk’s own words, the chatbot is willing to “answer almost anything” including questions that other AI might refuse, all with a witty edge. While this attitude makes Grok an intriguing conversationalist, the core question for our purposes is how well it handles deep research tasks.

Being a newcomer, Grok’s full capabilities are still evolving, but early assessments provide some insight. In a face-off test of advanced chatbots, Grok demonstrated a notable ability to synthesize information from what it read, especially on topics like policy analysis and technical overviews. For instance, when tasked with explaining complex issues (such as carbon pricing in economics or healthcare systems), Grok was able to pull together a broad narrative and give a cohesive overview. This suggests that its underlying model can digest multiple sources or a lengthy prompt and produce a well-structured summary — a valuable trait for research. The same evaluation, however, pointed out that Grok often fell short on precision. In practice, this means that while Grok might capture the general idea of a subject, it sometimes lacks detailed data points or specific evidence in its answers. For example, it might outline the pros and cons of a policy but omit exact statistics or fail to cite where a particular claim comes from. This contrasts with a tool like Perplexity, which explicitly provides sources, or Kompas AI, which focuses on detailed reporting. Grok’s answers as of now tend to be more like an informed summary from a single knowledgeable commentator, rather than a meticulously referenced research report.

Strengths of Grok include its access to the latest information and its ability to deliver answers with a broad perspective. It can be especially useful if your research involves very current events or data from live feeds, since Grok can incorporate information from the immediate present (tweets, news updates, etc.) that others might not yet have in their knowledge base. Moreover, its less filtered, more open-ended style means it might venture to discuss topics or viewpoints that other AIs shy away from. From a research standpoint, this could occasionally surface unconventional insights or at least save time by not requiring highly precise prompts to get certain information.

On the flip side, Grok’s weaknesses for deep research are tied to its relative immaturity and focus. The lack of emphasis on precise data can be a problem if your research question demands exact figures, rigorous evidence, or granular details. As one analysis summarized, Grok can “outline broader narratives with ease” but those needing detailed data may have to look elsewhere. Additionally, Grok does not currently highlight its sources in-line. A researcher using Grok would need to manually verify any facts it provides by searching the literature or web, similar to using ChatGPT. Another consideration is that Grok is not widely available to everyone at this time. Initially launched as a beta for X Premium subscribers, it remains gated — reports indicate it’s offered to Premium+ users (approximately $30 per month) on Musk’s platform. This limited availability means fewer hands-on reports from the broader research community so far, and possibly a smaller knowledge base of fine-tuning compared to ChatGPT which has millions of users. In summary, Grok shows promise with its modern data access and strong synthesis of general information, but it currently trails in delivering the depth of detail and verification that a rigorous research task might require. As an emerging tool, it may improve rapidly, but researchers will need to treat its outputs with caution and likely supplement them with additional fact-checking.

Kompas AI: Continuous Multi-Step Research with Structured Reports

Kompas AI is a rising competitor that explicitly targets the niche of extensive, continuous research. Unlike the others, Kompas is not a general chatbot but rather presents itself as a kind of virtual research team in a box. When you use Kompas, you’re effectively deploying multiple specialized AI agents that work collaboratively: the system will scour the web for relevant information, analyze findings, and synthesize them, coordinating these steps much like a team of human researchers might. The end product is not just an answer in a chat window, but a comprehensive, data-driven report complete with structured sections and detailed insights. In other words, Kompas’s user experience is geared toward producing a report rather than a casual conversation — a distinguishing factor that sets it apart from the chat-focused design of ChatGPT, Perplexity, and Grok.

At its core, Kompas AI specializes in multi-step, in-depth research workflows. The platform is built to handle complex inquiries that require digging through many sources and refining the query along the way. In contrast to ChatGPT’s quick Q&A style, Kompas emphasizes a thorough exploration: it will not just answer the initial question, but can “research further” at each stage, continuously expanding the depth of analysis. This means users can iteratively deepen the investigation — for example, after getting an initial report, you might press a button to have Kompas delve into a specific sub-topic or incorporate additional sources, and it will obligingly extend the report with more detail. Kompas is designed to handle a large volume of source material in this process. It can parse and synthesize content from dozens or even hundreds of pages across the web or uploaded documents, far beyond what a single prompt to a regular chatbot could cover. After gathering all that information, Kompas automatically summarizes and compiles the findings into a coherent long-form document. The report it generates is structured with headings, subpoints, and often includes elements like statistical data, case study examples, and direct citations to sources, all arranged in a logical flow. This report-ready output is a unique advantage — the result isn’t just helpful in the moment, but is formatted to be shared with others or integrated into professional work without extensive editing.

Kompas AI’s strengths squarely lie in those scenarios that demand depth and structure. For instance, if an analyst needs to prepare a market research report or a policy brief, Kompas can automate much of the heavy lifting: gathering facts, comparing viewpoints from multiple references, and delivering a draft that reads like a well-organized research paper. Users have the flexibility to manually edit the report or even use AI editing features to adjust tone and phrasing, ensuring the final output meets their needs. Another strength is the continuous research capability — instead of stopping after one answer, Kompas encourages you to refine and keep investigating, which is closer to how a human researcher works. This can lead to more comprehensive insights than a one-and-done answer. Importantly, Kompas was built with collaboration and sharing in mind: the finished reports can be exported (e.g., to PDF or Word) and shared easily, which is useful for teams and stakeholders. The user interface reflects this focus, looking less like a chat box and more like a document editor with research tools, which appeals to those who want to see their results in a polished form.

When considering weaknesses of Kompas AI, they are mostly the flip side of its specializations. Because Kompas is tuned for long-form research, it may feel less convenient for simple questions. If you just need a quick fact or a one-paragraph answer, using Kompas could be overkill — a fast tool like Perplexity or ChatGPT might get you the answer in seconds with minimal effort. Kompas’s thorough approach naturally takes more time to generate results; waiting a few minutes for a multi-page report is normal, whereas a basic chatbot responds in seconds. This emphasis on depth over speed means Kompas isn’t designed for casual chatting or trivial Q&A. Another consideration is that Kompas is a newer entrant (it’s a rising competitor, still expanding its user base), so it doesn’t have the same level of community familiarity or third-party integrations yet as ChatGPT does. That said, early users and reviews highlight its potential. The developers clearly position Kompas as an answer to the limitations of other AI tools: whereas traditional chatbots excel at quick answers, Kompas “goes a step further by facilitating multi-layered research and extensive document creation,” allowing you to analyze extensive content and automatically compile it into a coherent report. In essence, Kompas AI fills the gap between the immediate but shallow convenience of a chatbot and the thorough but time-consuming process of manual research.

For researchers, analysts, or students who need sustained research assistance, Kompas offers a unique value proposition. It acts like a diligent research analyst that not only finds information but also organizes it into a structured narrative — something the other AI tools typically leave up to the user. By prioritizing depth, continuity, and a report-ready output, Kompas AI stands out as a tool tailored for in-depth projects. It may not replace generalist chatbots for everyday questions, but when your goal is to dive deep into a topic and emerge with a written report, Kompas is engineered to excel at that task.

Conclusion: Choosing the Right AI for Deep Research

All four AI systems — ChatGPT, Perplexity, Grok, and Kompas AI — bring different strengths to the table, and the best choice depends on the nature of your research task. In a rapidly evolving AI landscape, it’s important to match your needs with the tool’s capabilities:

• ChatGPT — The generalist. It’s highly versatile and user-friendly, making it great for quick queries, explanations, or brainstorming. However, it lacks built-in depth for sustained research; you won’t get sources or multi-document analysis in the standard mode. Advanced research features exist but are costly and still maturing. Use ChatGPT when you need instant answers or creative assistance, but be prepared to do additional research confirmation on important facts.
• Perplexity AI — The fact-finder. It excels at delivering quick answers with citations, drawing directly from current web information. It’s an excellent choice if you need to verify facts or get a brief on a topic with evidence attached. Perplexity can handle complex questions and integrate multiple sources on the fly, but its answers remain concise. It doesn’t automatically produce lengthy reports or carry a research conversation beyond what you specifically ask. Rely on Perplexity for fast, credible snippets of information, knowing that deep exploration will require you steering a series of pointed questions.
• Grok (xAI) — The real-time synthesizer. Grok offers access to the latest information and a broad-strokes analytical style, which can be valuable for topics that change rapidly or unconventional queries. It will give you an overview laced with a bit of humor, and it’s less likely to refuse borderline questions. That said, Grok isn’t yet a source of meticulous detail — it tends to summarize rather than dive into data points. Also, being new and not widely accessible, it should be used with caution for critical research. Turn to Grok if you need an up-to-the-minute perspective or a wide-ranging summary from an AI that’s unafraid to pull in real-time content, but double-check any crucial specifics it provides.
• Kompas AI — The research specialist. Kompas is built for continuous in-depth research and report generation, making it ideal when your end goal is a comprehensive analysis, not just an answer. It systematically digs through large amounts of information and produces a structured report complete with findings and citations. You can iteratively refine the query, allowing the research to go deeper in stages. The trade-off is that it’s not an instant answer machine — it’s a tool for when you have a complex question that merits an investigative approach. Choose Kompas AI when you need an AI partner to study a problem with you and deliver a written overview or paper. It shines in scenarios where others would require significant manual effort to assemble and organize the information.

In conclusion, the landscape of AI research tools is diverse. General users might gravitate toward ChatGPT or Perplexity for their simplicity and speed, getting straightforward answers with minimal fuss. Power users and professionals, on the other hand, may seek more depth — and that’s where tools like Kompas AI (and emerging features like ChatGPT’s deep research mode or new entrants like Grok) come into play. Each tool carries a balance of convenience vs. depth. ChatGPT and Perplexity prioritize quick, conversational responses; Grok adds a real-time, unfiltered twist; and Kompas AI offers a fundamentally different, report-oriented experience geared toward sustained research excellence. By understanding these differences, users can better select the AI assistant that aligns with their research needs — whether it’s a quick fact-check or a thorough investigative report. The evolution of these platforms is ongoing, but as of today, Kompas AI is carving out a distinct niche by providing the kind of structured, in-depth research support that the others only hint at, making it a compelling choice for those who require depth, continuity, and reliability in their AI-driven research process.