Deep Research Tools: A Comprehensive Guide and Comparison

48 min readMar 4, 2025

Introduction to Deep Research

What is Deep Research? In simple terms, deep research refers to an intensive, thorough investigation into a topic, often involving multi-step information gathering and analysis. Unlike a quick web search or a brief Q&A with an AI, deep research delves into numerous sources and synthesizes a comprehensive understanding. This approach is crucial for people doing intensive knowledge work — think of academics writing literature reviews, analysts compiling business intelligence, or journalists investigating complex stories. They often need more than surface-level answers, requiring a methodical dive into data and sources to produce reliable insights. Deep research typically involves reading across academic papers, news archives, databases, or the open web, then connecting the dots to form a cohesive narrative or report.

Significance and Use Cases: The value of deep research lies in the depth and reliability of its output. In academic research, deep research helps scholars survey all relevant literature, ensuring no important study or contradictory evidence is overlooked. In business intelligence, companies use deep research to analyze market trends, competitor strategies, or customer insights across reports and web data, yielding actionable intelligence. Investigative journalism relies on deep research to uncover facts from public records, historical news, and interviews, piecing together a factual story from fragments. Other fields like policy analysis, science and engineering R&D, or legal case reviews also demand deep research to achieve thoroughness. In all these scenarios, the researcher must gather information from multiple sources, verify facts, and compile findings into a structured format (like a report or briefing). It’s intensive work — and this is where AI-powered deep research tools have recently stepped in to help.

AI and Deep Research: Modern AI tools are increasingly capable of automating parts of the deep research process. Advanced AI agents (like OpenAI’s ChatGPT “Deep Research” mode or specialized platforms) can autonomously search the internet, read through large volumes of text, and generate a well-structured report with sources. These tools mimic how a human researcher might work — by iteratively querying, reading, adjusting the search, and writing up findings. The goal is to save time and provide a “first draft” of comprehensive research. This is a game-changer: academics can get a draft literature summary in minutes instead of days, and analysts can have an AI quickly pull together data from hundreds of webpages. However, it’s not magic — these tools still require oversight. They can sometimes “hallucinate” (produce incorrect information) or cite less credible sources. So, while AI deep research tools are powerful for scaling up our research capabilities, users must understand their strengths and limitations to choose the right tool for the task.

In this blog, we will explore five notable deep research tools — ChatGPT, Perplexity, Kompas AI, GenSpark, and Elicit. We’ll compare their performance on key factors such as cost, output quality, speed, editing ease, and overall user experience. Then, we’ll dive deeper into each tool’s unique strengths and weaknesses. Whether you’re a general user curious about AI research assistants or a tech professional seeking the best tool for rigorous analysis, this guide will provide a clear, detailed overview.

Comparing Deep Research Tools: Key Factors

Before examining each tool individually, let’s compare how ChatGPT, Perplexity, Kompas AI, GenSpark, and Elicit stack up on several important criteria for deep research: cost efficiency, report quality, speed, ease of editing, and overall user experience. This high-level comparison will help identify which tools excel in certain areas and where their trade-offs lie.

Cost Efficiency

Cost is often a deciding factor, especially if you plan to use a tool extensively. Here’s how our five tools compare in terms of pricing models and value:

ChatGPT (OpenAI): ChatGPT offers a free tier (accessible to anyone) but its advanced capabilities for research typically require a paid plan. The standard ChatGPT Plus subscription is $20/month, which gives access to GPT-4 and some beta features. However, the specific “Deep Research” agent mode that OpenAI introduced is only available on the ChatGPT Pro plan at around $200/month. In other words, to have ChatGPT autonomously scour the web and produce deep research reports, you need a hefty subscription. This cost is a concern for extensive use — it’s quite steep for individuals (roughly $200 extra on top of the base plan). API access to GPT-4 is another route, charged per token, which can also become expensive if an agent is reading and writing tens of thousands of words.
Summary: Powerful but pricey — ChatGPT can do deep research, but heavy usage may strain your budget.
Perplexity AI: Perplexity operates on a freemium model. The good news is that its Deep Research feature is available even on the free tier (just sign up with an email). Free users can run a limited number of deep research queries per day (often around five queries daily at no cost, based on current limits) — which is great for students or occasional users. Perplexity also offers a paid subscription (Perplexity Pro) that unlocks more queries, faster responses, and possibly larger context or GPT-4 answers. That subscription has been roughly $20-$30/month in the past. Overall, Perplexity is very cost-effective for deep research: you can get substantial use without paying, and the paid tier is moderate if you need more.
Summary: Budget-friendly, with a generous free usage and affordable upgrade, making advanced research accessible to anyone.
Kompas AI: Kompas AI is a dedicated research platform and is a paid service (after a free trial). Its pricing is subscription-based with tiers. As of the latest info, the Standard plan is around $20/month (for individual researchers with a cap on reports) and a Pro plan around $40-$50/month for higher usage. They typically offer a free trial (e.g., 30 free credits on sign-up) so you can test it out. While it’s not free for ongoing use, Kompas’s standard tier cost is in the same ballpark as ChatGPT’s $20 (but Kompas is specifically built for deep research tasks). There is no exorbitant premium requirement beyond that — you get the full multi-step research capabilities with a normal subscription. This makes Kompas a relatively cost-efficient choice for heavy research needs, especially compared to ChatGPT’s $200 pro tier.
Summary: Paid but reasonably priced — designed for professionals, it balances cost with specialized functionality (roughly the cost of other pro productivity tools).
GenSpark: GenSpark.ai currently offers its deep research via a free tier. Users can access its AI-driven research agent (often labeled “AI Pilot”) without a subscription or usage fees. This means you can run complex research queries on GenSpark at no monetary cost, which is a big plus for cost efficiency. (There may be premium offerings or plans for enterprise, but for individual use the core features appear free at the moment.) The trade-off for being free is often found in other areas like speed or capacity, which we’ll discuss. But purely in terms of dollars, GenSpark is very cost-efficient — essentially no direct cost for extensive research, making it attractive to users who can’t invest in subscriptions.
Summary: Completely free for now, which lowers the barrier to trying deep research (just be prepared for some non-monetary costs like time).
Elicit: Elicit is an AI research assistant provided by the nonprofit Ought, and it’s currently free to use. There is no subscription fee; you can go to Elicit’s website and start asking research questions without payment. Elicit’s focus is narrower (academic literature), but from a cost perspective, it’s one of the most accessible tools. It’s funded by grants and donations, so researchers and students benefit from its capabilities at no cost. There isn’t a paid tier for Elicit as of now, so everyone gets full functionality.
Summary: Completely free, ideal for students and academics on a budget who need AI help in their research workflow.

Report Quality (Depth, Structure, Accuracy, Relevance)

One of the biggest considerations when choosing a deep research tool is the quality of the report or output it generates. Does it produce in-depth content or just superficial summaries? Are its reports well-structured and coherent? Does it stay accurate and cite relevant sources? Here’s how each tool fares:

ChatGPT: When it comes to depth and structure, ChatGPT (especially with the GPT-4 model) is very capable. With the Deep Research mode, it can produce lengthy, detailed reports on a topic — sometimes running into tens of pages of analysis. For example, one user found it generated a 12,000-word report with numerous references on an education topic. The structure is usually logical: it tends to follow the prompt’s guidance, breaking the report into sections or an outline if asked. It can even format references or bibliography in styles like APA or MLA on request. In terms of accuracy and relevance, ChatGPT’s answers are generally high-quality thanks to GPT-4’s understanding, but there are cautionary points: it can sometimes include a source that isn’t the most authoritative, since it doesn’t always perfectly judge source credibility. Also, like all AI, if a topic has little published info, it might fill gaps by hallucinating. Overall, ChatGPT shines in depth and coherence — its narratives are well-written — and it usually stays on topic. If you need a polished, essay-like output, it excels. Just remember to fact-check key details and sources, as it might occasionally cite a blog when a scholarly source would be better. The relevance of content is typically high because it’s guided by the user’s prompt and clarifying questions (the Deep Research mode even asks the user to refine scope before diving in, ensuring it covers exactly what’s needed).
Perplexity: Perplexity’s output is characterized by being concise but well-cited. It focuses on answering the query directly and factually. The depth is good, though typically not as verbose as ChatGPT’s — instead of a 20-page essay, Perplexity might give you a few well-structured paragraphs summarizing the findings. One notable aspect of Perplexity’s Deep Research is that it pulls from a very wide range of sources in the process. In one comparison, Perplexity’s answer cited 57 sources, more than double the sources used by ChatGPT for the same query. This indicates strong coverage and evidence for its claims. However, it doesn’t necessarily quote or elaborate on each source in the text — many are provided as references or footnotes for thoroughness. The structure of Perplexity’s report is usually a straightforward coherent summary, possibly with bullet points for different aspects of the answer. It tends to produce a direct synthesis rather than a long narrative, which can be easier to digest quickly but perhaps less deep in explanation than ChatGPT’s essay style. Regarding accuracy, Perplexity places emphasis on factual correctness — it was reported to achieve 93.9% accuracy on a factual QA benchmark (SimpleQA), outperforming other AI on that test. It actively cites sources inline, so users can verify each claim. There have been instances noted by users where Perplexity hallucinated a citation (e.g. a source with a wrong date or a non-existent link), so one should still verify critical references. But overall, its reputation is to be trustworthy and relevant, sticking closely to found sources.
Summary of quality: Perplexity gives highly relevant, factual summaries with lots of sources, but the report may not be as deep or discursive as some others — it’s often to-the-point.
Kompas AI: Kompas AI is specifically built to deliver truly comprehensive reports, and it shows in the output quality. Its reports are long-form and structured, often automatically organized into sections with headings, subtopics, and a logical flow. Under the hood, Kompas performs multi-step research on dozens or even hundreds of pages, then compiles the findings into a coherent long-form document. This means the depth is usually excellent — it’s not uncommon for a Kompas report to include background, key findings, case examples, data points, and a conclusion or summary. It strives to filter out irrelevant info and only include what’s pertinent, so even though it’s thorough, it stays focused. In terms of accuracy and citations, Kompas does provide citations for information (much like an academic report). The platform evaluates source reliability before integrating content, aiming for evidence-backed points. You can expect it to include references to the articles or websites it used, enhancing trustworthiness. Another strong point is relevance: because Kompas asks you for an initial prompt and perhaps some keywords or outline preferences, it tailors the research closely to your needs. Users often describe the output as something akin to a research analyst’s work: well-rounded perspective, multiple viewpoints or examples, and readable prose. Overall, Kompas’s report quality is high — depth comparable to ChatGPT’s long outputs, structure even more explicitly organized (with an automatically generated outline), and accuracy reinforced by source-backed content. (Of course, with any AI, final verification is wise, but Kompas’s specialization in research makes it reliable for extensive projects.)
GenSpark: GenSpark’s AI research tool also aims to produce in-depth reports, but user experiences suggest a mix of strengths and weaknesses in quality. On the positive side, GenSpark uses a “mixture of experts” approach with multiple AI models (GPT-4, Claude, Gemini, etc. working together). This multi-agent system can provide diverse perspectives on a query and cross-verify information, theoretically leading to well-rounded answers. GenSpark’s outputs, often shown as Sparkpages, do contain detailed content and usually include links or citations to the sources it consulted (especially if you view the Sparkpage, you can see the references it used). In terms of structure, GenSpark tends to present information in sections or bullet points, depending on the query, rather than a single continuous essay — kind of like a research brief. The accuracy and relevance of GenSpark’s outputs are generally good for common topics, as it uses credible sources and large models. It’s marketed as providing “unbiased, comprehensive results” via its multi-agent system. However, a known issue has been that the quality is not consistent if the process doesn’t complete fully (more on that in Speed). If the agent times out or fails mid-way, the report might be incomplete or missing context. When it does finish, users have found the insights useful but sometimes not as deep on technical details as ChatGPT. In one comparison for a technical research task, a user noted OpenAI’s Deep Research gave a superior result, implying GenSpark’s analysis was a bit less nuanced for that case. Overall, GenSpark’s report quality is decent: it covers the question with a fair amount of detail and sources, but it may not always reach the same depth or polished narrative as some others, partly due to occasional execution issues. It’s a newer platform, so its quality may improve with time.
Elicit: Elicit’s outputs are quite different in style from the others. Rather than writing a free-form long report, Elicit is designed to surface relevant information from academic literature. When you ask a question, Elicit typically returns a list or table of top relevant papers with short summaries or extracted answers from each. The “report” you get might be, for example, a table where each row is a research paper and columns show the paper’s title, a summary of its findings relevant to your question, and maybe other details (like sample size if it’s a study, publication year, etc.). So the depth comes from the fact that you’re getting actual scientific findings and detailed points from multiple papers. However, Elicit itself doesn’t narrate a combined summary — it’s more of an aggregator and extractor. The structure is very straightforward: often a bulleted list of findings or a Q&A format pulling answers from sources. In some modes, Elicit will give a one-paragraph direct answer (synthesized) to your question and cite a couple of papers as references. This can be useful for a quick answer, but if you need a comprehensive report, you would likely use the paper findings it gives to write your own synthesis. In terms of accuracy, Elicit is strong because it uses published papers as the source of truth. It’s basically doing an AI-powered literature review. There’s little hallucination in the sense of making up facts — it usually quotes or summarizes what’s in the actual documents. The relevance of results is high for academic or factual queries; Elicit employs semantic search so even if your keywords aren’t exact, it can find related research (e.g., you ask about “effect of social media on teen mental health” and it finds papers phrased differently but relevant). One drawback is if your question isn’t something well-studied in literature, Elicit might not find much or might give somewhat tangential results. Also, because it’s focused on academic content, it might miss non-scholarly but relevant information (news, statistics from reports, etc.).
Summary of quality: Elicit provides trusted, source-based answers especially suited for research questions — it shines in accuracy and relevance for academic topics, but the user has to do more assembly of the final report since Elicit’s output is not a narrative report but rather building blocks for one.

Speed

How fast can each tool deliver insights or a full report? Speed can vary widely — some tools take mere seconds or a couple of minutes, while others might have you waiting much longer for complex queries. Here’s what to expect:

ChatGPT: In standard chat mode, ChatGPT generates responses in real-time within seconds for short answers, and perhaps a minute or two for multi-paragraph answers. However, in Deep Research mode (the autonomous web research agent), ChatGPT’s process is slower because it’s performing multiple searches and reading content before responding. Users report that ChatGPT’s deep research can take on the order of several minutes to produce a final report. For instance, after clarifying the query, it spent about 8 minutes searching and compiling results for a detailed report. This is still relatively fast given it might be reading dozens of pages in that time. The speed also depends on the complexity: some tasks might finish in 5 minutes, others in 10–15 minutes. It’s not instant gratification, but considering the depth of output (thousands of words, cited), a 5–10 minute wait is quite reasonable. One thing to note is that during the process, ChatGPT will show a “thinking” or progress indicator, which at least lets you know it’s working. Overall: ChatGPT (Deep Research) is moderately fast — faster than a human doing hours of research, but slower than simple Q&A. For most uses, waiting a few minutes for a thorough report is acceptable, but it’s not the fastest of all.
Perplexity: Perplexity is known for its quick responses. Its normal mode answers often come in just seconds. For Deep Research mode, it does take a bit longer since it’s iterative, but it’s optimized to be efficient. Typically, Perplexity completes a deep research query in under 3 minutes in many cases. It automates the search-read-summarize cycle very effectively. Users often find that by the time you’ve formulated your next question, Perplexity has the answer ready. In fact, among AI research tools, Perplexity tends to be one of the fastest while still maintaining quality. It’s not uncommon to get a well-structured summary with citations in 1–2 minutes. If the question is extremely broad it might take a bit longer, but generally speed is a selling point for Perplexity. Also, its interface shows a progress bar or some indication as it goes through steps (and you can watch which sources it’s pulling). Overall: Fast and responsive — Perplexity’s deep research feels snappy, making it great when you need information quickly without waiting around.
Kompas AI: Kompas AI, because it conducts multi-step research across many sources, typically takes a few minutes to generate a full report. From user feedback and the developers, it runs multiple AI agents in parallel to speed this up. Many reports will be ready in the range of 3 to 5 minutes. For example, if you ask for a comprehensive market trend analysis, Kompas might outline, fetch sources, analyze, and produce a structured report all within a handful of minutes. This is quite efficient given the volume of data it might be processing. If you use the “Research Further” feature to deepen the analysis with another round, that adds a couple more minutes. Compared to ChatGPT’s agent, Kompas is in a similar ballpark or slightly faster, since it parallelizes tasks. It’s certainly slower than a simple search engine, but fast relative to the depth of output. There is also a consideration for very large tasks — if you explicitly ask for something extremely broad that triggers hundreds of pages, it could take longer (and possibly the system might break it into chunks). In general use, users report the speed as acceptable and the tool provides intermediate feedback (it might show which stage it’s on). You’re likely to get a complete long-form report without significant delay. Overall: Kompas AI is reasonably fast for deep research, usually delivering results in a few minutes, striking a good balance between thoroughness and waiting time.
GenSpark: Here is where GenSpark shows a notable drawback. GenSpark’s multi-agent approach, while powerful, can be very slow in practice. It doesn’t just retrieve a few pages; it often goes through hundreds of sources and multiple AI processes, which can take a long time to complete. Users have noted that GenSpark’s agents usually take around 20 minutes to finish a deep research task, and in some cases it can run for nearly 2 hours for a single query. This is a significant waiting time and can be frustrating if you were expecting quick insights. The slow speed might be due to the overhead of coordinating multiple models and ensuring comprehensive coverage. GenSpark sometimes attempts extremely thorough searches, which is impressive but time-consuming. There have also been reports of tasks timing out or failing if they hit certain limits, meaning you could wait a long time only to get a partial result. The platform might be improving over time, but as of now, speed is a major limitation. If you have a quick question or need instant results, GenSpark is not the ideal choice — it’s more for when you can let it run in the background. Overall: Significantly slower than other tools for deep research. GenSpark prioritizes thoroughness (and uses complex processing), but you pay for that in wait time. It’s best used when you can afford to start a query and come back later for the results.
Elicit: Elicit’s speed is generally very fast. Since it’s mostly doing search over indexed academic papers (via Semantic Scholar’s database) and then using a language model to extract answers, it usually returns results in seconds. A typical Elicit query might take 5–15 seconds to populate a table of relevant papers and their summaries. Even a more complex question that involves scanning many papers rarely takes more than a minute or two. Elicit is optimized for quick interactions — you can refine your query or add filters (like specifying a date range or field of study) and it will update the results near-instantly. The model’s summarization for each paper is done on the fly but it’s quite fast. One reason it’s speedy is that it doesn’t generate a huge narrative, it’s pulling bite-sized info (which is easier and quicker for an AI to produce). Also, much of the heavy lifting (searching papers) is done through efficient information retrieval systems. In short, among our list, Elicit is one of the quickest to deliver useful content, making it convenient to use during research brainstorming or when you want to quickly scan what literature is out there on a question. Overall: Lightning-fast for what it does — Elicit provides near-immediate results, keeping your research flow moving without pauses.

Ease of Editing and Refinement

After an AI generates a report or answer, how easy is it for the user to tweak or refine the content? Deep research often requires adjusting the output — maybe correcting a detail, expanding a section, or changing the tone. This criterion looks at how flexible each tool is in allowing edits or iterative refinement.

ChatGPT: Editing with ChatGPT is typically done through conversation. While ChatGPT doesn’t give you a GUI to directly edit its text output, you can instruct it to make changes. For example, you can say “Expand the section on methodology” or “Rewrite the above in a more formal tone”, and it will attempt to comply. This interactive refinement is one of ChatGPT’s strengths — you have a back-and-forth dialog to zero in on what you want. If something is incorrect or not detailed enough, you can point it out and ChatGPT will correct itself (provided the information is within its knowledge or you guide it). In Deep Research mode, after the initial big report, you can still chat with the AI agent to ask follow-up questions or request adjustments. However, one limitation is if the output is extremely long (like the 12k word report scenario), editing via prompts can become unwieldy or may hit context length limits eventually. ChatGPT’s interface does let you edit your last prompt and regenerate, which is useful for refining the query itself (rather than the answer’s text). There’s no direct document editor in ChatGPT where you click and type to change the AI’s text — you’d have to copy it out to Word or similar for manual edits. But given its conversational nature, many users find it easy to iterate on the content until it’s right.
Summary: Editing with ChatGPT is flexible through prompts, though not a WYSIWYG editor. It’s good for refining content in context, but manual editing has to be done outside the tool.
Perplexity: Perplexity does not offer in-app editing of the generated answer. The answer is displayed with its citations, but you can’t click and revise the text directly. To refine the output, you usually would pose a follow-up question or rephrase your query and run it again. Perplexity’s interface encourages iterative queries: you might ask a question, get an answer, then ask a more specific follow-up (for example, “Can you give more details on [a subtopic]?”) to deepen that part of the report. It will then produce a refined answer or an extended one focusing on that subtopic. This question drill-down approach works, but it’s not as free-form as editing a document. Another aspect is exporting and sharing — Perplexity allows you to export the report it creates (you can convert it into a Perplexity Page or a PDF). Once exported, you could edit the content in a document editor manually. So, ease of editing within Perplexity’s environment is limited, but ease of getting the content out for editing is good. Also, because Perplexity’s answers are concise, many users find they don’t need heavy editing for small tasks — they might just use the info directly with citations. If more customization is needed, one might prefer a different tool or plan to do manual edits.
Summary: Limited built-in editing — you refine Perplexity outputs mainly by asking new questions. It’s straightforward to use, but not meant for heavy on-platform report editing.
Kompas AI: Kompas AI places a strong emphasis on easy editing of reports. After it generates a report, the platform provides robust editing tools. You can manually edit any part of the text within the Kompas interface — effectively, it doubles as a document editor. Kompas even includes an AI Edit feature that helps adjust the text: you can toggle an AI editor to change the tone, fix phrasing, or translate sections automatically. For instance, if the report’s tone is too formal, the AI Edit can make it more casual, or if you want a summary at the top, you could ask the AI to generate that. Importantly, Kompas also lets you reorganize the structure easily — since it often presents an outline, you can move sections around or delete/add as needed. The fact that it was built for creating long-form reports means it treats the output like a living document rather than a static answer. Users can thus refine the report to their liking before finalizing it. Additionally, Kompas’s “Research Further” feature is a form of iterative refinement: if you feel a section needs more detail, you can instruct the tool to delve deeper on that section, and it will append or insert additional findings. This is a unique form of editing where you are effectively editing the content by expanding it through additional AI research. Overall, Kompas offers the best editing experience among these tools — combining AI assistance with user control.
Summary: Highly editable — Kompas provides a built-in report editor and AI tools for refinement, giving you hands-on control to tweak the generated research just like you would edit a document.
GenSpark: GenSpark’s interface for its research outputs (Sparkpages) is more read-only. You get the compiled result page and you can share it or copy from it, but you cannot directly modify that page on the GenSpark site. If you want to make changes, you would need to copy the content to your own document. There isn’t an interactive way to tell the GenSpark agent “change this part” or “update that section” — the system doesn’t support iterative dialogue in the same way ChatGPT does. Essentially, each GenSpark query is a one-off generation; if you need something different, you might have to run a new query with adjusted parameters or prompt. That said, GenSpark’s website is designed like a search engine, so you can always perform another search query to refine results. But you’ll be starting fresh each time. The ease of editing, therefore, is relatively poor within the tool itself. You have to do manual edits externally. On the bright side, since GenSpark is free, you can run multiple attempts without cost if the first output isn’t satisfactory — but that is time consuming given the speed. They might improve this in the future with more interactive features, but currently it’s more of a “get your answer, take it or leave it” setup.
Summary: Minimal editing support — treat GenSpark’s output as a final answer that you can copy and edit on your own; the tool itself doesn’t facilitate direct refinement of that output.
Elicit: Elicit’s workflow is not about editing large generated texts, because it usually gives you data (like paper summaries). You can refine what you see by changing the query or by using filters. For example, you can filter results by year if you only want recent papers, or exclude certain keywords, etc., and Elicit will update the list dynamically. This is a sort of refinement of results rather than editing an answer. If Elicit gives you a short synthesized answer and you want more detail, one approach is to click on one of the source papers and read more, or ask a more specific question about that detail. Elicit is very interactive in that you can keep narrowing down or expanding the scope with new questions. However, it does not compose a single report that you then edit. So in terms of editing, the concept is a bit orthogonal — you’re curating results. Many users will use Elicit outputs as input to their own writing. For instance, you might get key points from 5 papers via Elicit, then you write a paragraph yourself combining those (or use ChatGPT to help write it, even). Elicit doesn’t need much editing because it’s typically factual snippets. If something seems off, you likely just ignore that paper or result.
Summary: Different paradigm — Elicit doesn’t generate a long text to edit; instead it lets you refine queries and results. The “editing” here is more about selecting and using the information in your own write-up.

Overall User Experience (UX)

Finally, let’s compare the general user experience of each tool — how easy and pleasant is it to use? This includes the interface design, navigation, and how well the tool integrates into a user’s workflow.

ChatGPT: ChatGPT’s interface is a familiar chat-style layout. The UX is very straightforward — you type a prompt and get a response, in a conversational thread. This simplicity is why ChatGPT gained mass adoption. For deep research, the new mode still operates within this chat interface, which some might find too minimal for large reports, but it does show the AI’s “thinking” process in a side panel (for example, the list of searches it’s doing), which is engaging for power users. Navigation is just scrolling through a conversation, which is easy for general users. However, managing very long outputs in a chat can be a bit clunky (you have to scroll a lot). Also, ChatGPT lacks specialized research workflow features — e.g. you can’t see a bibliography all at once unless it prints one, and you can’t easily jump to a specific section aside from using your browser find tool. So, usability is high for casual Q&A, but for deep research one might wish for more organizational tools. Nevertheless, the overall experience is polished and reliable. There are virtually no bugs, and it handles context well. ChatGPT also works across devices (the web interface is mobile-friendly and there are apps), which makes it convenient. For workflow efficiency: if you’re already brainstorming in ChatGPT, having it do research is seamless; but if you want to integrate it with, say, your reference manager or other tools, you have to copy-paste out.
In summary: user-friendly and minimalistic. Great for accessibility, maybe not specialized enough for heavy research projects without some manual effort alongside.
Perplexity AI: Perplexity offers a web interface that feels like a mix of a search engine and a Q&A assistant. The design is clean, with the question at top and the answer below, sources cited alongside. It also has a sidebar for follow-up questions in a thread, so you can have a conversational experience (each follow-up refines or asks something new). Users often praise Perplexity’s UI for showing sources clearly — each sentence of the answer is usually annotated with footnote numbers you can click to see the reference. This transparency boosts confidence in the answers. Navigation is easy: you can scroll through the answer and click on any source link to read it in detail (it opens in a panel). There’s also a history of your sessions if logged in. Workflow efficiency is good for quick research tasks — you ask, get answer, possibly copy the snippet with citation to your notes. For lengthier workflows, you can use the export feature to get a persistent page of the answer. One limitation is that if you want to do multi-part research (like first gather background, then statistics, then case studies), you handle that by sequential questions; Perplexity doesn’t automatically merge them (that’s where a tool like Kompas might do a multi-section report). But overall, using Perplexity is smooth and intuitive. The speed and straightforward presentation mean both general users and professionals can adapt to it easily. It feels like a supercharged search engine, so there’s not much of a learning curve. In terms of UI polish, it’s quite modern and there are no major pain points.
UX summary: Transparent and easy-to-use, Perplexity is designed for a hassle-free experience, with strengths in source navigation and quick Q&A style research.
Kompas AI: Since Kompas AI is purpose-built for deep research, its user experience includes more workflow-oriented features. When you start a research project in Kompas, the interface might ask for your topic and any specific sub-questions or preferences (this helps it create an outline automatically). Once it generates a report, the UI presents it in a document format with a table of contents or a clear separation of sections. Users have described it as feeling like a research workspace. You can easily switch into edit mode, access the AI editing tools (e.g., change tone), or hit a “Research Further” button on a section that you want to deepen. This makes navigation and refinement very intuitive — the tool guides you through a research process rather than just dumping an answer. For a general user, Kompas might initially seem a bit more complex than a simple chat, only because it offers multiple buttons and options (outline view, edit tools, etc.). However, the learning curve is not steep; the platform provides an onboarding or tooltips to explain features. The ability to export your final report as a document or PDF is built-in, which is great for sharing results. Also, Kompas being available on web and having cloud sync means you can start research on one device and continue on another, which is handy for professionals on the go. In terms of overall workflow efficiency, Kompas can actually save time during the project — because it automates the outline and allows continuous research, you spend less time managing the process and more time reviewing content. The navigation within a long report is also better thanks to section headers and the structured format (no endless scrolling without context).
UX summary: Research-centric and efficient, Kompas AI’s interface is tailored to longer projects. Once you get used to its features, it can significantly streamline the deep research workflow. It manages to pack powerful capabilities without making the UI overwhelming, which is a plus for both tech-savvy users and non-experts who just want thorough results with less hassle.
GenSpark: GenSpark’s user interface is similar to a search engine with an AI twist. You enter your query in a search bar. What happens next is a bit different: rather than immediately showing results, GenSpark might show a loading indicator for quite a while (given its speed issues). Eventually, it presents a Sparkpage with the compiled answer. The Sparkpage is basically a scrollable page that might have sections, and often it includes a list of references at the end or embedded hyperlinks. Navigation and usability: On the plus side, the interface is clean and the results, when they appear, are easy to read. However, the lack of interactivity (no follow-up questions in the same session, no editing) can make the UX feel static. One aspect that affects user experience greatly is the delay — watching a spinner for several minutes is not engaging. GenSpark sometimes tries to mitigate this by providing intermediate status messages like “Analyzing source X…”, which at least keeps the user informed that it’s working. For a general user, the patience required might be a deal-breaker. For a professional, the depth might be appreciated but the inability to easily refine without starting over is inconvenient. Also, because it’s newer, there might be occasional hiccups or less polished elements in the UI. It does allow sharing a Sparkpage via a link, which is nice for collaboration (similar to sharing a Google doc link).
In summary: GenSpark’s UX is a mixed bag — conceptually straightforward, but hampered by performance. It feels like using a beta product where the idea is solid but the execution isn’t as smooth as mature tools. If you don’t mind setting a query and coming back later, it’s fine; but for interactive research or quick iteration, the user experience can be frustrating.
Elicit: Elicit’s interface is tailored to researchers. When you ask a question, it shows a table of results with columns like “Paper Title”, “Snippet (answer to your question)”, “Journal/Year”, etc. It’s a unique layout compared to the other tools. For a general user, the tabular format might be a surprise (it looks more like a tool for literature review than a chat or essay). However, it’s quite functional: you can sort the table, click on titles to get more details about a study, or toggle what information you want to see. There’s also a box to refine your query or ask a new question. Elicit’s focus on academic papers means the user should ideally be somewhat comfortable reading scholarly snippets — it might quote an abstract sentence, for example. But even if not, the tool tries to pull out the answer in plain language if possible. One great UX feature is the “Find me papers about X” style simplicity — you don’t need to know boolean search or specific databases; just natural language works. Also, Elicit is evolving — they have an “Elicit Beta” interface that might give a more narrative summary for some queries. In terms of integration into workflow, Elicit is used often alongside other tools: for instance, a researcher might use Elicit to get papers, then use Zotero or a reference manager to download them, etc. Elicit doesn’t manage citations for you, but it gives you the references you might need. Because it’s free and web-based, it’s accessible. It may not be the friendliest for a completely non-academic user (since the value is mainly if you needed research literature), but for its target audience, the UX is quite efficient. No fluff, just results and the ability to dig deeper if you want by reading the actual papers.
UX summary: Functional and geared to research tasks — not a glossy consumer app, but it gets the job done with logical design. Once you understand what it’s showing you, it’s a very powerful assistant for literature search.

Now that we’ve compared these tools across key dimensions, let’s examine each one in detail to highlight their individual strengths and ideal use cases, as well as any notable weaknesses.

Detailed Breakdown of Each Tool

ChatGPT (with Deep Research)

Overview & Strengths: ChatGPT is a highly versatile AI assistant, and with the introduction of its “Deep Research” capability it can serve as a powerful research aide. The core strength of ChatGPT is its intelligence and flexibility — powered by GPT-4, it can understand complex queries and generate very detailed responses. When used for deep research, ChatGPT will autonomously search the web and databases, and compile a structured report. It excels at writing quality — the prose it produces is often human-like and well-organized, which is great for readability. It can also adjust style on the fly (e.g., you can ask for bullet-point summaries, or a narrative report, etc.). Another strength is that it can handle a wide range of topics: from scientific explanations to policy analysis, thanks to its broad training. For users who already use ChatGPT for other tasks (coding help, brainstorming, etc.), having deep research in the same interface is convenient. You can have a conversation that transitions from asking factual questions to generating a report outline, to finalizing the report. In short, ChatGPT’s deep research feature offers a “one-stop shop” — it plans the research, finds information, and writes it up in a coherent way.

Cost Concerns for Extensive Research: The biggest drawback of ChatGPT for heavy research use is cost. As discussed, the Deep Research mode is only available with a ChatGPT Pro $200/month plan. For many individuals, and even smaller organizations, this is a significant expense. While the value can be justified if you’re saving many hours of work (some professionals might offset the cost by higher productivity), it’s still a barrier. Some people solve this by subscribing only during months when they have a major project, but that adds friction. Even the standard $20/month Plus plan, if you try to approximate deep research manually, can run into limitations: GPT-4 on Plus has message caps and slow speeds for long outputs, and using the API for large-scale research could accumulate sizable token fees. In essence, ChatGPT is not the cheapest option for deep research, especially when there are free or cheaper specialized tools emerging. If budget is a concern and your research needs are constant, ChatGPT might be too expensive to be your primary tool for this purpose. Many users might use it sparingly — for example, do initial exploration with free tools, and perhaps use ChatGPT (if they have Plus) to refine or expand on certain points.

Other Weaknesses: Aside from cost, ChatGPT’s general weaknesses apply to its research usage as well. It can sometimes be too verbose, giving more information than needed, which then requires summarizing. It also may hallucinate references if it doesn’t actually retrieve sources (one must ensure to use the browsing/agent mode; if you just ask GPT-4 without browsing, it might invent citations). ChatGPT’s knowledge cutoff might be an issue for very recent topics — the browsing helps, but if the info is behind paywalls or not easily accessible, it might not find it. Another weakness is that ChatGPT doesn’t inherently show you its sources in the final answer unless you prompt it to, whereas tools like Perplexity do by default. You can ask ChatGPT to cite sources, and it will try, but verifying them is necessary. In summary, ChatGPT as a deep research tool is powerful but comes at a premium. It’s best for users who need top-notch language quality and are willing to invest money (or whose company has an enterprise subscription) to get a tailored, extensive report. Casual or budget-conscious researchers might turn to alternatives for most tasks and perhaps use ChatGPT selectively.

Perplexity

Overview & Strengths: Perplexity is an AI answer engine that stands out for combining search and AI seamlessly. It was built with research assistance in mind, meaning it always tries to ground its answers in real sources. One of Perplexity’s strengths is its research quality in terms of factual accuracy — as noted earlier, it has high accuracy on benchmarks and it actively references sources. For a user, this means you can trust but also verify its output, which is crucial in research. The tool’s interface and approach make it excellent for fact-finding missions, quick literature scans, and getting a succinct overview of a topic with pointers to more information. Perplexity’s speed is another big plus — it delivers insights faster than many competitors, so if you are on a deadline and need to gather info quickly, it’s a top choice. It’s also very easy to use: just ask in natural language, no complicated setup. Another strong point is that Perplexity’s answers often capture key points from multiple sources, which can reveal different angles on your question. For example, if you ask a question on a controversial topic, it might say “Source A reports X, while Source B suggests Y,” giving a balanced view. This is extremely useful for research where you want to see consensus or disagreement among sources. To sum up strengths: Perplexity is accurate, fast, and source-transparent. It’s like having a smart research assistant who also hands you the reference articles to read more.

Common Limitations: While Perplexity is powerful, it has some limitations to be aware of. One limitation is depth of output — Perplexity tends to provide answers that are relatively short (maybe a few paragraphs at most). Even its “deep research” mode won’t output a 10-page report; it will still be a concise synthesis, albeit drawn from deep searching. This means if you actually need a long-form report or a very detailed breakdown, Perplexity might not give it in one go. You might have to ask multiple specific questions and aggregate the answers yourself. Another limitation is that Perplexity’s knowledge is tied to what it can search; if the information isn’t easily searchable or is in long documents that require intensive reading, the summary might miss nuance. For instance, highly technical or mathematical content might not be fully captured in a quick summary. Users also report that context doesn’t carry over strongly between queries — it has a thread feature, but each question is largely answered on its own. So it might not remember all details from a previous answer when you ask a follow-up (beyond the immediate context shown). Additionally, while rare, the issue of hallucinated citations has been observed — sometimes the AI might cite a real article but attribute a wrong year or combine facts incorrectly. Perplexity tries to avoid this by pulling actual text from sources, but caution is still advised. Finally, Perplexity’s free version may have query limits per day (to manage load), so you can’t endlessly query without possibly hitting a cap unless you subscribe.
Who is it best for? Perplexity is ideal for users who need quick, accurate answers with references. It’s great for journalists needing fact checks, students verifying information for an essay, or anyone who wants a fast explanation with evidence. It may not be the best if you need a deeply narrative report or analysis — other tools (or manual effort) would complement it in those cases.

Kompas AI

Overview: Kompas AI is a tool purpose-built for deep, continuous research and report generation. It positions itself not just as an answer engine, but as a full research assistant that can go from initial inquiry all the way to a polished report. One way to think of Kompas is as a “virtual research team in a box” — it orchestrates multiple AI agents to handle different tasks (searching, reading, summarizing, writing) and delivers a cohesive result. The platform was specifically designed to address the needs of users who found general chatbots lacking in research workflow features.

Key Strengths: Kompas’s strengths align with the needs of deep research:

Continuous Multi-step Research: Unlike one-shot Q&A tools, Kompas works iteratively. It will perform an initial round of research, present findings, and crucially, it allows you to dig deeper. Its “Research Further” feature lets you expand the analysis continually. This means if the first pass found 50 sources and you want more detail, it can go find another batch of sources or information layers (for example, maybe first pass got general info, second pass gets case studies or specific data points). This continuous aspect ensures nothing important is left unexplored if you need more depth.
Long-Form Structured Reports: Kompas automatically generates a structured report, complete with sections and an organized flow. The output isn’t just a blob of text; it reads like a research paper or detailed report. This structure is incredibly useful for absorbing the information and for sharing it with others. It also saves you time in having to organize the content yourself. The reports include citations and evidence, as Kompas can insert references for claims it makes, and even compile a reference list if needed. So the quality of the report is on par with something a diligent human researcher might produce, both in depth and organization.
Easy Editing and Refinement: As mentioned earlier, Kompas shines in giving the user control over the report. You can manually edit any part, and use the built-in AI editor to tweak tone or phrasing. This addresses a common pain point: often AI-generated content needs a human touch; Kompas makes it easy to add that touch right in the platform. You don’t have to export to Word just to fix a paragraph — you can do it on the spot. Additionally, because the research can be iterative, you can refine not just the text but the content itself by telling Kompas to fetch more info or focus on a particular angle.
Designed for Researchers: The overall user experience with Kompas feels like it was designed alongside researchers or analysts. For example, it filters out a lot of fluff and focuses on reliable sources, aiming to include stats, case studies, and specifics for a “thorough and high-quality final report.” Also, it handles large volumes of data — it can analyze hundreds of pages of content across sources and distill them. This scalability means even very broad topics can be tackled. Essentially, Kompas is built to go further than ChatGPT’s deep research in terms of continuous refinement and user control.

Weaknesses or Considerations: No tool is perfect, and Kompas has a few considerations to keep in mind:

Learning Curve: Kompas has more features and buttons than a simple chat interface, so first-time users might need a few minutes to learn the workflow (outline generation, using the edit mode, etc.). It’s not overly complex, but someone expecting a one-click answer might be initially surprised that Kompas encourages a bit more interaction (which is ultimately beneficial for the result).
Cost & Access: Kompas is not free beyond the trial. This could be a barrier for some. However, as we noted, the pricing (~$20-$50/month) is moderate for what it offers, and arguably if you are the kind of user who needs Kompas (extensive research help), you likely can justify that cost. Still, for a very occasional user, they might stick to free tools and only use Kompas during the trial or as needed.
Speed vs Thoroughness: While Kompas is reasonably fast, it may not be instant. Users who are accustomed to split-second answers need to expect that Kompas might take a few minutes to produce a very detailed report. This is usually a worthwhile trade-off for depth. But if you ask a very narrow question, Kompas’s heavy-duty approach might be overkill (and slower than something like Perplexity). So Kompas shines when you have a broad or complex research question; for a quick fact, it’s not the tool you’d use (and it’s not meant to be a quick fact lookup tool).
Reliance on Source Availability: Kompas, like others, is limited by what information is accessible on the web. It can’t break through paywalls (it won’t read content that isn’t publicly available), so some niche or premium data might not be included unless you provide it. The flip side is you can actually upload documents or provide text for Kompas to include (depending on its features) — which is great — but that requires user action.

Best Use Cases: Kompas AI is best suited for situations where you need a thorough, written analysis on a topic without doing all the legwork yourself. For example:

A market researcher could use Kompas to generate a comprehensive market trend report, complete with data and examples, then edit it to add proprietary insights.
A consultant or business strategist might use it to quickly get up to speed on a new industry or to produce part of a client deliverable (saving hours of googling and compiling info).
Students or academics could use it for writing literature reviews or white papers — it won’t replace reading the actual papers, but it can summarize lots of background info and related work, which the student can then refine and cite properly.
Writers and content creators might use Kompas to gather research for a book or detailed article, especially if it involves factual reporting.

In all these cases, Kompas’s ability to handle scope and depth, and allow the user to iteratively refine the output, is a huge advantage. It’s like an AI that doesn’t stop at one answer, but collaborates with you until you’re satisfied.

In a naturally non-promotional tone, it’s fair to say Kompas AI has carved out a niche where it leverages AI to amplify human research capabilities. It doesn’t just answer a question — it helps you conduct an investigation. For users who frequently find themselves doing extensive research projects, Kompas can be a highly valuable tool in their toolkit, offering a blend of automation and control that few others do.

GenSpark

Overview: GenSpark (or Genspark.ai) is an AI-driven research tool that has gained attention as a free, multi-agent search assistant. It’s somewhat akin to having multiple AI search bots work on your query simultaneously and then aggregate the findings. The idea is innovative: by using different AI models (OpenAI, Anthropic, etc.) and specialized agents, GenSpark attempts to provide comprehensive answers and even generate things like reports or analyses on demand. It’s positioned as especially useful for business and market research use cases (its site mentions delivering expert business insights instantly).

Strengths: One strength of GenSpark is its multi-model approach. By not relying on a single AI model, it can capitalize on the strengths of each. For example, GPT-4 might be great at reasoning, Claude might be better at summarizing long texts, etc. GenSpark tries to orchestrate them, which could lead to a richer output. In practice, this means GenSpark often pulls in a diverse set of sources and perspectives, theoretically reducing single-model bias and potentially catching things one model might miss. Another strength is that it’s completely free (at least at the time of writing) for the standard features. This lowers the barrier to usage — anyone can try it for heavy research queries without worrying about paywalls or usage limits. The interface (Sparkpages) often presents detailed information, and it usually includes the references it used, which is good for transparency. GenSpark has also shown strength in certain niche tasks; for instance, it introduced features for financial reports and web data analysis, which could be useful for finance professionals wanting an AI summary of financial info (leveraging the Claude model as reported in news). Moreover, GenSpark’s creators highlight unbiased results thanks to the multi-agent system — this is hard to measure, but the intent is that it cross-verifies facts to give you reliable info. In summary, GenSpark’s strengths lie in its ambitious comprehensive approach and its accessibility (no cost). It aims to be a one-click research assistant that scours broadly.

Speed Limitations (Major Weakness): The most significant issue with GenSpark is speed. As noted, it can take a very long time to produce an output for complex queries — often on the order of tens of minutes. This is a serious limitation in practical use. If you ask GenSpark a question and it says “please wait 20 minutes,” most users will not find that acceptable on a regular basis, especially when other tools respond in seconds or minutes. The slow speed is likely due to the multi-agent orchestration and possibly limited computing resources (since it’s free, it might not have the massive backend that paid services do). Sometimes, the wait might not yield a clearly better result than faster tools, making it hard to justify unless the thoroughness is indeed superior. Users have reported extreme cases where it ran nearly 2 hours for a task — that’s an outlier but indicative of how performance can degrade on big tasks. Another related weakness: due to the slowness, if you spot something to change, running a second round is painful. It’s not interactive, so every refinement means another long wait.

Other Drawbacks: Aside from speed, GenSpark being a newer tool means it might have some stability issues. As the HN comments indicated, sometimes the agent would fail to execute the “Deep Research” tool at all (especially with certain model choices, like an OpenAI model that might time out). This can lead to frustration where you get no answer after waiting. It’s essentially the growing pains of a new platform. Additionally, the lack of conversational refinement (no chat to tweak results) and limited customization options (beyond choosing which AI model to try) can make it less user-friendly for iterative research. You kind of fire the query and hope for the best. GenSpark’s content quality, while generally good, may not consistently match something like ChatGPT’s polished output, especially in technical topics. It’s worth noting that the multi-agent idea is complex, and sometimes combining outputs can lead to a less coherent final report if not done perfectly. Some users might find the results a bit disjointed or lacking explanation connecting the pieces.

Best Use Cases: Given these pros and cons, where does GenSpark fit best?

Quick, Light Research (if speed improves or for simpler queries): If you have a straightforward query and you’re not in a hurry, GenSpark can retrieve answers for free. For example, a student might use it to get some points on a topic for homework if they don’t have access to paid tools.
Budget-Conscious Users: Any individual or organization that cannot afford subscriptions might tolerate the speed in exchange for not paying. They could run GenSpark, go do something else, and come back to a result. Over time, this might still save them manual research effort.
Comparison and Verification: Some tech-savvy users might use GenSpark as one of multiple tools to compare answers. Perhaps they get an answer from ChatGPT and one from GenSpark and see if GenSpark’s multi-agent approach found something different or extra. In this way, GenSpark can act as a “second pair of eyes” on a research problem.
Niche Search Tasks: If GenSpark has integrated some specialized data (like the finance report feature) it might fetch information that general tools like ChatGPT won’t immediately provide unless prompted specifically. For example, it might automatically present a financial summary or historical data for a company, which could be handy.

However, if one needs a rapid, interactive research session, GenSpark is currently not the go-to. Its main advantage is being free and thorough, but the speed bottleneck means it feels like using an older computer — it can do the job, just slowly. If the developers improve the speed or allow some partial results streaming, it could become much more useful. As of now, use GenSpark with the expectation that patience is required. It’s a promising tool, but for mission-critical fast projects, it might serve better as a backup rather than the primary tool.

Elicit

Overview: Elicit is a unique AI research assistant focused on helping with academic literature and evidence-based questions. It was developed by Ought, a research organization, with the aim of making scholarly knowledge more accessible through AI. Elicit doesn’t browse the entire web; instead, it primarily taps into a large database of research papers (such as Semantic Scholar’s corpus). It uses language models to understand your question and then finds relevant academic papers, extracts useful information from them, and presents that to you. Essentially, Elicit is like an AI-powered academic search engine plus summarizer.

Capabilities: Elicit has a set of capabilities tailored to research workflows:

Literature Review Assistance: This is where Elicit shines. If you ask a question that researchers might ask (e.g., “What are the effects of X on Y according to recent studies?”), Elicit will retrieve papers that studied that question and show you key results or conclusions from each. It can do this even if your question doesn’t match the paper titles exactly, because it understands the query’s intent (semantic search). This helps you find relevant studies without perfect keyword guessing.
Summarizing Papers: You can give Elicit a specific paper (by title or DOI) and ask it to summarize it, or ask a question about it. Elicit will read the paper (via its abstract or full text if available) and give you an answer. This is very useful for quickly understanding a paper without reading it end-to-end.
Extracting Specific Info: Elicit can pull out specific data from papers. For example, “What was the sample size in Smith 2021’s experiment?” or “According to Johnson 2020, what is the correlation between A and B?” It can navigate the text to find these details and present them.
Idea Generation: By seeing what research has been done, Elicit indirectly helps generate ideas. If you ask a broad question, it might show you various approaches researchers have taken, which can spur new questions or hypotheses.
Synthesis (in Beta): Elicit has been experimenting with features to write a summary that synthesizes multiple papers’ findings together. This is a bit more advanced and can sometimes produce a coherent narrative of “Overall, studies suggest X …” with references. It’s still improving in that area.

Best Use Cases: Elicit is best used in academic and professional research settings where credibility and evidence are paramount. For instance:

A student writing a research paper can use Elicit to quickly find relevant literature, saving hours that would otherwise be spent manually searching databases.
A scientist or analyst can use Elicit to check if a certain hypothesis has been studied before, or find the current state-of-the-art findings on a topic.
In evidence-based fields like medicine or public policy, Elicit can help gather what the published evidence says about a question (like “Does intervention X work for problem Y?”), which is incredibly valuable for informed decisions.
Even outside academia, a curious individual who wants a substantive answer (with sources) to a question like “What do studies show about productivity and remote work?” could use Elicit to get an answer that’s more grounded in research than, say, a general web search or a random blog.

Drawbacks: Elicit’s focused nature means it’s not the tool for every question. Its knowledge domain is largely academic research. If you ask “How do I fix my car engine?” Elicit won’t be useful, because there’s likely no academic paper on your specific car issue. Similarly, if the information you need is in the news, on forums, or other non-academic sources, Elicit won’t retrieve that. It prioritizes journals, conference papers, etc. Another drawback is that Elicit’s answers can be dry or technical, since they often quote or paraphrase academic phrasing. For a general user, that might be less digestible. Also, the tool sometimes gives answers that are basically a snippet from one paper, which may not capture the consensus if different papers have different results. So, a user might have to look at several of the provided snippets to see the bigger picture. Elicit also might miss very recent papers that aren’t in its database yet or not open-access. And like all AI, if a paper has a mistake or unsupported claim, Elicit isn’t currently “fact-checking” it — it’s just relaying what was written. Therefore, critical thinking is still needed.

Best Use vs. Other Tools: Compared to the other tools in this blog:

Elicit is not for writing long reports for you. It provides the ingredients (facts, findings) for you to write your own.
It’s complementary to something like ChatGPT. In fact, some people use Elicit to find relevant papers and then use ChatGPT to help summarize or explain those papers in simpler language.
It’s somewhat similar to the “References” or “Wikipedia” aspect of Perplexity, but far more focused on academic sources and with more ability to extract targeted info.
It doesn’t do web browsing or multi-step reasoning to compile a narrative like Kompas or ChatGPT Deep Research; it stays at the level of “here’s what this paper says”.

Drawbacks (continued) & Mitigations: One notable drawback is that Elicit currently requires internet access and can’t be used offline with your own PDFs (though they were exploring letting users upload PDFs to analyze — which would be a neat feature if fully implemented). Also, if your question is extremely broad or vague, Elicit might give broad results that aren’t too helpful; it sometimes works best when you have a fairly specific question that researchers might conceivably study. There is also the possibility of irrelevant results if your question has multiple interpretations — you might need to refine your query. The interface may feel a bit like a tool for experts, but they do provide tutorials and it’s free to try, so it’s worth exploring.

Conclusion on Elicit: Elicit is a specialized but powerful assistant for those who need evidence-backed answers. Its ability to cut down literature review time and point you directly to relevant findings is a huge boon in research work. The trade-off is that it won’t write the report for you — you, the user, are still in charge of interpreting and synthesizing the information. That said, for anyone in academia or doing any form of scholarly research, Elicit can feel like a secret superpower, accelerating the tedious parts of research so you can focus on analysis and writing. It’s a tool best used alongside one’s own expertise, making the research process more efficient and thorough.

Conclusion

Key Takeaways: In this exploration of deep research tools, we’ve seen that while all these platforms share the common goal of helping us gather and synthesize information, they each have distinct strengths. ChatGPT (with its advanced research mode) offers unparalleled language fluency and depth, essentially giving you a well-written report but at a significant cost and with some need for careful fact-checking due to its general AI nature. Perplexity emerges as an excellent everyday research companion — it’s fast, factual, and user-friendly, perfect for quickly learning the lay of the land on a topic with confidence from cited sources. Kompas AI stands out as a dedicated research workhorse, going the extra mile (and then some) by doing continuous multi-step digging and giving you a fully structured, editable report; it bridges the gap between AI output and user control, which makes it especially powerful for intensive projects. GenSpark shows the promise of combining multiple AI insights and doing it all for free, making deep research accessible, but its slow performance currently limits its practicality for time-sensitive tasks. Elicit carves a niche for itself by focusing on academic literature — it doesn’t write the story for you, but it hands you the evidence on a silver platter, which for many researchers is the hardest part.

Final Comparisons: If we compare them directly, we might say:

For sheer quality of output (when money is no object): ChatGPT’s top-tier output and Kompas’s comprehensive reports are neck and neck. ChatGPT writes more fluidly; Kompas ensures no stone is unturned and is easier to refine. ChatGPT might give a 10/10 essay, Kompas a 10/10 research brief.
For ease and speed: Perplexity is the clear winner — almost like an AI-powered Google, it’s there whenever you need quick info without fuss. Elicit is also quick but serves a specific purpose.
For cost efficiency: Elicit and GenSpark being free are fantastic, with GenSpark covering general topics (slowly) and Elicit covering academic ones (rapidly). Perplexity’s free tier also covers a lot of ground for no cost. Kompas and ChatGPT require subscriptions, but Kompas arguably gives more specialized value for a similar or slightly higher price than a generic ChatGPT Plus, while ChatGPT’s full capabilities demand a high fee.
For user experience and workflow: Kompas is built for the research workflow itself, which can save time if you’re routinely doing reports. ChatGPT and Perplexity are more Q&A-oriented, which might require you to do a bit more manual assembly for a large project. GenSpark’s UX suffers from slowness. Elicit integrates well if your workflow is academic research.

Recommendations for Different Users:

Businesses/Professionals: If you’re in a business setting (marketing research, consulting, competitive intelligence), Kompas AI is likely the best fit because it produces ready-to-use reports and lets you fine-tune them. It can save a team’s hours by automating the grunt work of research while still allowing the team to inject their insights. ChatGPT’s pro plan could also be used if budget permits, especially for companies that already use OpenAI’s ecosystem — it will deliver great results, but consider if the improved writing style is worth the cost over Kompas, which might actually gather more data. For quick checks or daily questions, employees can use Perplexity (perhaps the free version or a team plan if available) for instant answers with sources. GenSpark, being free, might appeal to startups or NGOs with no budget — it can be used to get insights without subscriptions, just with patience.
Researchers/Academics: Elicit should be in every researcher’s toolbox for literature reviews and scanning academic knowledge. It directly addresses their needs and is free. For writing up sections of a paper or getting a draft of an analysis, many academics might use ChatGPT (the standard GPT-4 on Plus) because it’s great at refining text — but they should verify content. Kompas AI could be very useful for, say, a professor or research assistant who needs to survey a broad topic (especially interdisciplinary or non-specialist areas) quickly; it will gather a lot of sources that they can then look at in detail. Perplexity can help students understand concepts or find references for assignments with less risk of misinformation than general web search. GenSpark might be used in academia experimentally to see if its multi-agent approach finds obscure info, but it’s less likely to be a go-to for time-pressed researchers.
Individual Professionals and Power Users: For independent analysts, writers, or just intellectually curious individuals, the choice can be mixed and based on specific needs. If you frequently need comprehensive reports and are willing to invest a bit, Kompas AI offers a strong value (essentially giving you research services that might be akin to hiring a part-time research assistant). If your use is more occasional and you prefer not to subscribe, Perplexity and Elicit together cover a lot of ground — Perplexity for general topics and web info, Elicit for deeper scientific queries. ChatGPT Plus at $20 is also a solid option for individuals because you get a general assistant that can do a bit of everything (just remember its deep research mode is limited unless you have Pro). A writer might use ChatGPT for drafting and Perplexity for fact-checking sources, for example. GenSpark could be tried by hobbyists or tech enthusiasts who are not on a deadline — it’s free, so it’s more about whether you’re okay with the wait and possibly a bit of trial-and-error.

Objectivity and the Right Tool for the Job: It’s important to note that these tools are not mutually exclusive. In fact, a savvy user might combine them. Each tool can fill a gap the other leaves. For instance, Kompas or ChatGPT can produce a nice long report, and then one might use Elicit to verify that the key claims in that report are backed by published studies, and use Perplexity to see if any recent news or data might have been missed. Using them together can compensate for individual weaknesses (like cross-checking ChatGPT’s output with Perplexity for factual accuracy).

In choosing the right tool, consider the scope of your research task, your need for speed vs depth, and your budget. If you need a quick, reliable answer with citations for a work meeting in 5 minutes, go to Perplexity. If you’re starting a week-long research project for a client report, invest in Kompas AI for that period (it will likely pay off in time saved). If you have access to ChatGPT’s advanced features and you trust it, it can produce excellent results especially with careful prompting — just budget the cost accordingly. If you specifically need to lean on academic evidence, Elicit is unmatched in that domain and it’s free. And if budgets are tight or you want to experiment with an AI research agent, GenSpark is there — it might surprise you with what it finds, as long as you’re not racing the clock.

Final Thoughts: We are in an exciting era where AI can shoulder a lot of the heavy lifting in research. Each of these tools represents a different approach to that promise: from conversational AI to search-augmented QA to fully autonomous research agents. As these tools evolve, we can expect them to become faster, more accurate, and even more user-friendly. For now, the choice of tool can greatly influence your research experience. By understanding what each one offers, you can leverage them smartly — perhaps the true “deep research” skill is knowing which AI assistant to ask and how to ask it. With the right tool (or combination of tools) in hand, even individual users can achieve a breadth and depth of research that used to require teams of people. The future of deep research is likely a collaborative dance between human curiosity and AI diligence, and these five tools are among the leading partners in that dance today.