Transform your AI-generated research papers, academic documents, and ChatGPT PDF exports into clean, structured Markdown format. Perfect for researchers, students, and content creators.
Support for ChatGPT research papers and academic PDFs
Max: 10 files, 4MB each, up to 50 pages each (recommended: ~10 pages)
1 token per file
Explore a few sample CHATGPT Deep research papers and their converted Markdown output.
# Converting ChatGPT Deep Research PDFs to Markdown ## Why Manual Conversion Is Difficult Converting a ChatGPT Deep Research report from PDF to Markdown is not straightforward, mainly due to the complex formatting and embedded elements involved. By default, OpenAI’s Deep Research feature does not offer a direct Markdown export, forcing users to rely on workarounds. Many users have expressed frustration that Deep Research lacks a simple PDF or Markdown export option—a missing feature seen as a significant oversight for such a premium service. As a result, people end up manually copying content from the PDF, which brings several challenges: - **Loss of Formatting**: Copy-pasting text from a PDF often strips away structure and styling. Headings, bullet lists, and footnotes (like ChatGPT’s source citations) can be lost or jumbled. In practice, manual conversion is “extremely difficult and time-consuming”—simply copying the text loses the original formatting, and trying to recreate it by hand can take hours. Maintaining the proper document structure (sections, subpoints, Q&A format) is nearly impossible to do perfectly by hand. - **Embedded Elements (Images and Layout)**: Deep Research PDFs often contain embedded images, graphs, or tables, as well as numbered citations and references. Standard PDF-to-text methods usually ignore or drop these elements. For example, one user found that their PDF-to-Markdown conversion resulted in a file with all the text but no images included. Extracting images and embedding them in Markdown requires extra steps (saving each image and adding the correct `![]()` links). Page layout artifacts (page numbers, headers/footers) can also clutter the output if not handled, and multi-column layouts or sidebars (if any) don’t translate cleanly into linear text. Overall, a Deep Research PDF isn’t a simple linear document, so converting it by hand can lead to messy results. - **Time and Effort**: Because of these formatting complexities, doing the conversion manually is very labor-intensive. Each section might need re-formatting in Markdown (adding `#` for headers, `-` for lists, backticks for code, etc.), and each reference link or footnote needs to be reconstructed. The Deep Research PDF output might be dozens of pages long with numerous citations and Q&A sections, so manual conversion could take a prohibitive amount of time. As the DeepResearch2Markdown site itself notes, manual copy-paste conversion can take hours and still fail to preserve the structure properly. In short, without an automated tool, reusing Deep Research content in a Markdown workflow is tedious and error-prone. ## Using DeepResearch2Markdown to Convert PDFs to Markdown The website DeepResearch2Markdown (at [deepresearch2markdown.com](https://deepresearch2markdown.com)) was created to solve this problem by automatically converting ChatGPT Deep Research PDFs into clean Markdown files. It provides a simple, no-code solution that preserves the original formatting. ## Step-by-Step Guide to Using DeepResearch2Markdown 1. **Export Your Deep Research Content as a PDF**: - In ChatGPT, first generate or open your Deep Research report. - Use your web browser’s “Print” function and choose 'Save as PDF' to download the conversation or report as a PDF file. Ensure the PDF is text-based (selectable text), not just an image or scan of text. 2. **Visit DeepResearch2Markdown and Sign In**: - Go to [deepresearch2markdown.com](https://deepresearch2markdown.com). - You may need to sign in with a Google account to use the converter. The service uses a token system for conversions – new users get 1 free token, which allows one PDF conversion at no cost. 3. **Upload the PDF File**: - On the DeepResearch2Markdown homepage, find the upload area that says “Drop PDF files here or click to browse.” - Drag and drop your ChatGPT-generated PDF onto this area, or click 'Choose Files' to select the PDF from your computer. You can upload multiple PDFs at once (the site supports batch processing for up to 10 files at a time). Each file will be queued for conversion. 4. **Wait for the Conversion**: - Once uploaded, the tool will automatically process the PDF. This usually only takes a few seconds per document. - DeepResearch2Markdown uses a combination of OCR and custom algorithms to extract the text and formatting from the PDF. The site will preserve the document’s structure, including headings, lists, and citation markers, as it converts the content to Markdown format. 5. **Download the Markdown Output**: - After processing, a download link or button will appear for your converted file. Click to download the Markdown (.md) file to your computer. - The output will be a clean Markdown document containing the text from your Deep Research report, formatted with proper Markdown syntax. All major elements from the original should be retained. Using DeepResearch2Markdown is straightforward and does not require any coding or software installation. In summary, you upload your ChatGPT PDF and instantly get a Markdown version of it. This process eliminates the need for tedious manual reformatting, letting you integrate the content into your markdown notes, GitHub README, blog, or other workflows immediately. ## Tool Effectiveness and Features: A Short Review DeepResearch2Markdown proves to be an effective solution for converting Deep Research PDFs. Users have found it easy to use and efficient. ## Evaluation of DeepResearch2Markdown ### Strengths #### Ease of Use The tool is extremely user-friendly. It features a simple drag-and-drop interface and doesn’t require technical know-how. Even those unfamiliar with Markdown can get their content converted with just a couple of clicks. The process is essentially “upload and download,” which makes it accessible to general users. #### Accuracy and Format Preservation One of the standout benefits is how well the converter preserves the original formatting. It maintains the document structure — including section headers, subheadings, bullet points, and numbered lists — and even retains the citation markers and footnotes from the ChatGPT report. In the Markdown output, references are kept intact (e.g., as linked citation notation), which is crucial for research documents. The conversion is also quite accurate in terms of text content; because the tool is tuned specifically for AI-generated research papers and academic content, it upholds the academic formatting conventions in Markdown form. This means less time spent fixing headings or re-formatting lists after conversion. #### Speed and Efficiency DeepResearch2Markdown operates very quickly. Conversion is essentially in real-time – even multiple research PDFs can be processed in a matter of seconds. In practice, this is much faster than manually converting a document or using multi-step methods (like exporting to Word and then to Markdown). The ability to handle batch uploads (up to 10 PDFs at once) further increases productivity if you have several documents to convert. #### Notable Features In addition to basic text conversion, the service supports batch processing and is designed with researchers in mind. Batch conversion means you can drop in a collection of PDFs and get a set of Markdown files in one go, which is convenient. The tool also emphasizes privacy – uploaded documents are processed securely and not stored permanently on the server (files are deleted after conversion). This is an important consideration for users working with sensitive research data. Another useful feature is that the output Markdown is standardized and compatible with common platforms (e.g., ready to use on GitHub or in documentation sites without further tweaks). ### Limitations There are a few limitations to be aware of. First, DeepResearch2Markdown works best on PDF files that contain actual text. If your PDF is essentially a scanned image or contains non-selectable text, the OCR may struggle and the output might not capture everything perfectly. The quality of conversion for purely scanned PDFs could be lower, as with any OCR-based process. Secondly, the service has file size and length limits: each PDF can be up to about 4 MB in size and around 50 pages maximum. Very large Deep Research reports might need to be split into smaller chunks before conversion. In practice, a 50-page limit is plenty for most ChatGPT reports (which often are shorter), but if you have an extremely long session, you may need to break it up. Finally, while the tool is free to try with one document, regular use requires a purchase of conversion tokens. The cost is relatively modest (on the order of a few dollars for a batch of tokens), but it is not an unlimited free service. Despite this, many will find the time saved on manual formatting well worth the minor cost. Overall, DeepResearch2Markdown provides a much-needed solution for those who want to reuse ChatGPT Deep Research content in Markdown workflows. It addresses the key pain points (formatting and embedded content) by delivering a clean Markdown file that mirrors the original PDF’s structure. General users will appreciate the simplicity — you don’t have to be a programmer or spend hours cleaning up text. In just a few clicks, you can take a polished AI-generated research report and integrate it into your notes, blog, or knowledge base in Markdown form. The tool effectively removes the “busy work” of reformatting, allowing you to focus on using the content, not wrestling with it. Aside from a few limits (like handling of scanned PDFs and the need for tokens for heavy use), the converter is a fast, reliable, and convenient way to bridge the gap between ChatGPT’s Deep Research PDF output and the Markdown format that many workflows and platforms prefer. --- ## References [1]: https://deepresearch2markdown.com "DeepResearch2Markdown website" [2]: http://deepresearch2markdown.com/ "DeepResearch2Markdown - Convert ChatGPT PDFs to Markdown" [3]: https://discourse.devontechnologies.com/t/i-can-not-convert-any-of-my-files-into-md-pdf-epub/81583 "DEVONthink - DEVONtechnologies Community discussion on file conversion"
# ChatGPT vs. Google Gemini: Deep Research Comparison Both OpenAI and Google now offer agentic Deep Research modes that automatically browse the web, analyze content, and generate multi-page reports with citations. OpenAI’s Deep Research (in ChatGPT) is powered by its new “o3” model and can handle text, images, and PDFs, while Google’s Gemini Deep Research (in Gemini Advanced) uses Google’s Gemini 2.x models with a Mixture-of-Experts architecture and a million-token context window. In practice, both tools aim to replace hours of manual searching, but they differ in approach, interface, and output. ## Aspect Comparison ### Underlying Model - **ChatGPT Deep Research**: OpenAI o3 (RL-trained, multi-modal). Dynamically adapts research strategy in real time. - **Gemini Deep Research**: Google Gemini (Mixture-of-Experts 2.x). Uses a new planning system and RAG retrieval, with up to 1M tokens of context. ### Output Format - **ChatGPT**: Narrative “report” with headings and sub-headings. Highlights key insights in bold for easy skimming. Can include tables and (soon) images and charts. - **Gemini**: Formal multi-chapter report (numbered sections, executive summary). Often outputs multiple structured tables and comparisons. Integrates with Gemini Canvas to turn results into interactive infographics/quizzes or audio overviews. ### Sources & Citations - **ChatGPT**: Tends to cite fewer but highly reliable, research-backed sources (academic, government). Citations are shown inline and are clickable in the chat. - **Gemini**: Aggregates a larger number of sources (often news, blogs, company sites). Citations appear as footnotes or expandable links, with a full bibliography. Gemini often notes conflicting data across sources. ### Speed - **ChatGPT**: Typically takes 5–30 minutes to complete a query. Reports run sequentially in-chat, but you can work on other tasks and get notified when done. - **Gemini**: Generally faster than ChatGPT. Tests report ~40% shorter run times. Google’s asynchronous task engine lets you start a research job and even close your browser; you’ll be notified later when results are ready. An experimental “Gemini 2.0 Flash” mode also boosts responsiveness. ### Interaction & UI - **ChatGPT**: Accessed via the ChatGPT interface (web or app) by selecting “Deep Research”. ChatGPT asks clarifying questions before starting to ensure focus. A sidebar shows the plan and sources in real time. Reports stay in the chat thread; sharing is via chat link. - **Gemini**: Accessed in the Gemini app or web (select Deep Research mode). Gemini first shows a detailed plan that you can edit before execution. It then displays a “thinking” panel so you see ongoing reasoning. Reports can be exported (e.g., to Google Docs) or turned into Canvas pages. ### Pricing & Limits - **ChatGPT**: $200/month (Pro tier, 100 queries); deep research runs on a limited “o3” model (no free tier). Additional modes (o3-mini, GPT-4o) have different limits. ## Pricing and Availability - $19.99/month for Gemini Advanced (includes Deep Research). - Daily query limits apply. - Available in English on Gemini web/app (45+ languages). ## Accuracy of Retrieved Information Both tools emphasize source-based answers, but neither is perfect. In testing, both produced generally credible reports, yet made occasional factual errors. Reviewers found Gemini’s reports often more extensive but sometimes outdated or imprecise. For example, a smartphone-comparison test showed Gemini mistakenly treating already-released models as upcoming. ChatGPT’s report was shorter and slightly more factual on that point, though it also made minor errors (e.g., listing a slightly older model). Likewise, for financial data or stock analysis, one reviewer found both chatbots made omissions – but overall “Gemini was better… providing a more in-depth report.” In a fact-check, a LivePlan analyst reviewed output from both tools and reported them “very solid” in accuracy. On the specific queries tried, he found “both to be accurate” and even pointed out that Gemini would flag conflicting data from multiple sources. Nevertheless, he gave a slight edge to ChatGPT on accuracy, noting ChatGPT mostly used research-backed sources whereas Gemini sometimes cited less authoritative material. In short, Deep Research dramatically reduces hallucinations compared to standard chat queries (each fact comes with a citation), but users must still verify critical data. In fact, OpenAI cautions that deep research can occasionally produce inaccuracies and formatting glitches. Overall: ChatGPT Deep Research tends to be cautious and uses fewer, high-quality sources, while Gemini Deep Research is aggressive about breadth (citing many sources and emphasizing current data). ChatGPT’s outputs can be more concise and occasionally more reliable on specific points, whereas Gemini’s outputs are longer and more structured but must be checked for occasional stale facts. ## Quality and Credibility of Sources ChatGPT’s Deep Research cites its sources explicitly and emphasizes verifiability. In OpenAI’s demo, it “cites each claim” and provides a summary of its reasoning. A user test found ChatGPT pulled in numerous high-quality sources (e.g., government, academic, and reputable organizational sites) when given a specialized query. In one educator’s test, ChatGPT even generated an APA bibliography (if prompted) and “cited only high-quality sources.” ChatGPT’s citations remain inline and clickable within the chat, making it easy to verify individual facts. Gemini likewise links to source material, but its style differs. In reports, it often attaches superscript footnotes or hidden links at the end of paragraphs, and then lists a bibliography. Google’s marketing explains that Deep Research “provides a comprehensive report with key findings and links to original sources.” ## Review of Gemini and ChatGPT Reviewers noted that Gemini digs up a lot of references: in one analysis, Gemini cited over 50 sources compared to ChatGPT’s approximately 18 for the same query. Gemini often includes multiple sources for a single fact; if sources conflict, it may note the disagreement. However, some of those sources can be less scholarly. For example, one reviewer observed Gemini leaning on news articles and press releases more than peer-reviewed material. In practice, Gemini tends to cast a wide net, citing many web sources, whereas ChatGPT selects fewer but more “authoritative” references. For example, the LivePlan test found ChatGPT used exclusively research-backed data, while Gemini “relied on several sources that didn’t include research-backed or survey-backed facts.” On the upside, Gemini’s volume of citations means you often see dozens of footnotes for each report; ChatGPT’s are fewer but are always linked inline for quick checking. When exporting or sharing, however, both tools have quirks. ChatGPT preserves its links when viewed in-app, while Gemini’s links require expanding widgets or exporting to Google Docs (where they appear as an unlinked bibliography). In either case, the connected sources are accessible. In summary, both Deep Research modes greatly improve source credibility over a plain chat—every statement is (at least) cited—but the user should still vet sources, especially since Gemini’s high volume can include gray literature. ## Speed and Responsiveness Timeliness of information is a key advantage for Gemini, given its Google search integration. Gemini’s Deep Research is generally faster: in side-by-side tests, it completed tasks in roughly 60% of the time ChatGPT did (about 12 minutes vs. 17 minutes). Google even released a “2.0 Flash” experimental model to make Gemini quicker at understanding queries and returning answers. Importantly, Gemini is designed to run in the background: you can start a multi-step research task, close the browser or switch apps, and return later to find it finished. ChatGPT’s Deep Research also supports asynchronous operation: once launched, the user can navigate away and will be notified upon completion. However, it simply runs a series of web scrapes and analyses in sequence. OpenAI notes that Deep Research can take anywhere from 5 to 30 minutes depending on complexity. Reviewers saw this in action: one found ChatGPT took approximately 17 minutes to produce a detailed report. The speed depends on query scope and tools used (ChatGPT can use a Python sandbox for extra analysis, which adds time). Regarding freshness of data, both agents browse the web live. Gemini’s close tie to Google Search means it tends to fetch very recent info (e.g., focusing on 2024–2025 data). ChatGPT also retrieves current web data, but one tester noted its report emphasized slightly older (2018–2023) data when the prompt requested recent trends. In summary, Gemini holds a speed advantage due to optimized models and Google’s infrastructure. ## Comparison of ChatGPT and Gemini ChatGPT is slower by comparison, but both will eventually produce comparable outputs (and both allow offline waiting). Neither is “real-time” instantaneous – they trade speed for depth. Google’s flash model and Gemini’s asynchronous manager aim to improve responsiveness, while OpenAI is likely working on optimizations as well. ## Output Formatting and Structure Both tools output clearly structured reports rather than a simple chat answer. They use headings and subheadings to organize content. In early tests, users noted Gemini’s reports appear very formal – often broken into numbered chapters with an introduction and executive summary. In one example, Gemini’s output began with an executive overview of the smartphone market, defined each criterion in detail, and then presented each phone’s specs in subsections. It even ended with a ranked list of recommendations. Gemini used multiple tables (including a side-by-side specs table and charts) to compare products, and consistently formatted pros/cons lists. ChatGPT’s Deep Research tends to look more like a narrative report. It still uses section headings (e.g., “Background,” “Findings,” “Conclusion”) and can include lists and tables, but the style is less “textbook” and more “blog-post” like. In the same phone-comparison test, ChatGPT’s output was shorter (about 1,700 words vs. Gemini’s 7,500) and focused on concise descriptions. ChatGPT’s report did include a comparison table, but it covered fewer attributes and was more basic. On the positive side, ChatGPT automatically bolds key insights to aid scanning. It also embeds citations inline (e.g., superscript numbers that link to sources) which keeps context. Notably, ChatGPT inserted images of each phone in that test, adding a visual element Gemini did not (Gemini’s output was text-only). Both tools are good at turning data into tables or charts. In LivePlan’s tests, Gemini actually generated more structured tables to compare features side-by-side, whereas ChatGPT sometimes relied more on prose. However, OpenAI has announced upcoming support for images, charts, and data visualizations directly in Deep Research reports. ## Summary Gemini’s Deep Research outputs are highly structured and comprehensive, with formal sections, tables, and rankings. ChatGPT’s reports are more lightweight and prose-driven but highlight key points for easy reading. Both formats are clearer than a raw search result dump, and users can steer the format via prompts (e.g., ask for “tables” or “bullets”). ## Usability and Interface ChatGPT Deep Research is accessed within the ChatGPT interface. In practice, you select the “Deep Research” tool, provide a query (and optionally upload files or spreadsheets for context), and the agent may ask clarifying questions to narrow the scope. Once running, a sidebar shows the current research plan and sources found. After completion, the final report appears in the chat window. ### Gemini Deep Research vs ChatGPT You can continue asking follow-ups or revisions, but these are known to be finicky: in one test, ChatGPT’s attempts to revise or summarize an existing report often just truncated the content. Sharing is possible by sharing the chat link, and reports can be copied or exported, but exported formatting may lose some citation links. A minor UX quirk: users must manually choose “Deep Research” each time they want to start or continue a task. Gemini Deep Research lives in Google’s Gemini app/web. You enter a query and first see a multi-point research plan which you can edit or approve. The UI then shows a “thinking” pane where you watch Gemini browse and reason. During this phase, you can actually leave the page; when done, Gemini notifies you of the results. The final report is formatted like a document on screen. Notably, Gemini can export reports to Google Docs and integrate findings into Canvas for interactive use. It also stays in “Deep Research” mode after you start (unlike ChatGPT’s need to re-select). Some testers found Gemini’s planning step especially helpful for control, while others noted that its follow-ups or revisions (after the fact) can sometimes fail or crash. In user feedback, many appreciate Gemini’s collaborative workflow: the visible plan and ability to adjust scope makes it feel controllable. ChatGPT’s approach – asking questions and then diving in – can be faster to initiate but offers less preview. A business user wrote that Gemini’s mode “stayed in deep research” without extra clicks, whereas ChatGPT repeatedly prompted clarifying questions which “didn’t always feel effective”. ## Key UI Differences ChatGPT’s interface is that of a chat conversation with research turned on, with inline sources and the familiar dark theme; Gemini’s is a document-like interface with a planning wizard at the start. Gemini’s tight integration with Google tools (Workspace, Docs) and multi-language support (Gemini is available globally in 45+ languages) contrasts with ChatGPT’s chat interface and language availability. Both require paid subscriptions (ChatGPT Pro vs Gemini Advanced), and usability will depend on the user’s ecosystem (e.g., Google users may prefer Gemini’s Docs export and Google account sync). ## Technical Architecture Under the hood, ChatGPT Deep Research is essentially a new AI agent built on OpenAI’s upcoming “o3” model (a successor to GPT-4). This model was fine-tuned with reinforcement learning to perform research tasks, integrating tools like web browsing and a Python sandbox. It can ingest and reason over text, images, PDFs, and even code outputs. OpenAI describes it as “optimized for web browsing and data analysis”. During a session, ChatGPT’s agent continuously updates its internal plan based on findings and can pivot “in reaction to information it encounters”. ### System Overview The system is synchronous within the chat: each step is run by the model, with progress shown in the sidebar. Early tests showed this agent scoring 26.6% on Humanity’s Last Exam (a broad benchmark), illustrating the experimental nature of the model. ### Gemini Deep Research Gemini Deep Research employs Google’s Gemini 2.x models with a novel agent architecture. Google engineers built a multi-step planning system: first, the model breaks the user’s query into sub-tasks, then it executes them in parallel or sequence as needed. Importantly, Gemini uses an asynchronous task manager, so if one part fails, it can retry without restarting the whole process. It also uses Retrieval-Augmented Generation (RAG) to “remember” information across hundreds of pages using its 1M-token context window. The Gemini 2.5 Pro model is a Mixture-of-Experts transformer designed for efficiency and long-context reasoning. In essence, Google’s system trades off some flexibility (it follows the initial plan unless adjusted) for greater control and scale. The technical result is a highly distributed pipeline: hundreds of searches and model calls happen behind the scenes, stitching results into the final answer. ### Comparison with ChatGPT Both systems use state-of-the-art language models, but their approach differs: ChatGPT’s agent is more flexible and ad hoc (it “thinks aloud” and adapts on the fly), while Gemini’s is more structured (user reviews and modifies a plan, and the system reports on that plan). Both also perform multiple passes of self-critique to improve clarity. In summary, ChatGPT’s Deep Research is built around OpenAI’s new multi-modal RL agent (o3), whereas Gemini’s is built on Google’s ultra-long-context transformer (Gemini 2.x) with an expert planner and asynchronous backend. ## Expert and User Feedback Early reviews and user tests highlight complementary strengths. In technical comparisons, reviewers tend to praise Gemini for its depth and organization, and ChatGPT for its accuracy and conciseness. For example, AndroidAuthority’s test concluded “Gemini currently holds the edge for Deep Research tasks” due to its depth and structure, even though Gemini also made some factual errors (like an outdated phone release) that ChatGPT avoided. Conversely, the Section.ai reviewer (a business user) found ChatGPT’s research “more valuable” and Gemini’s “too high level” to be actionable. The LivePlan CEO tested both on market analysis and “found them both impressive” – giving a slight nod to ChatGPT on accuracy but noting Gemini’s nicer formatting and table output. Collectively, users say: Gemini feels more like a traditional research assistant (with outlines and formal reports), while ChatGPT feels more like a smart analyst that asks clarifying questions and highlights insights. ## User Feedback on Gemini and ChatGPT A G2 survey of real user feedback summarized that “Gemini excels in real-time research… and handling longer conversations,” while “ChatGPT is great for writing… and coding.” Users also rate Gemini slightly higher on reliability (91% satisfied) than ChatGPT on content accuracy (85% satisfied), reflecting Gemini’s structured consistency. In summary, beta users report that Gemini Deep Research is excellent for comprehensive, up-to-date web research and appreciates its Google integration (Docs export, etc.), whereas ChatGPT Deep Research shines at quickly surfacing key data with clear sources and flexible follow-up. Both are still maturing – some users noted bugs or slowdowns with revisions – but most agree they are powerful “time savers” for research. Experts caution that these tools generate polished outputs, so users should still apply critical judgment when using the results in any high-stakes context. ## Use-Case Scenarios ### Academic Research ChatGPT’s ability to process and cite open web content makes it useful for literature overviews and hypothesis generation. It can ingest PDFs (students often attach papers for summarization) and scour the web for open-access studies. However, it cannot access subscription journals. One educator found it identified key open-access papers in a field but only summarized them passively, lacking critical analysis. Gemini’s Deep Research, with its 1M-token window and multi-modal tools, could theoretically assist in analyzing long academic texts (e.g., theses, code, data). In practice, Gemini’s web search focus means it may surface news articles or reports on academic topics. Neither tool fully replaces domain experts, but both can quickly assemble background material. For instance, OpenAI noted that Deep Research is geared toward professionals in science and engineering, suggesting academic-style use. In short, both tools can jump-start literature reviews by gathering and summarizing sources, but outputs must be scrutinized (they are better at synthesis than novel insight). ### Business Analysis Both are marketed for corporate research tasks. Google explicitly lists “competitive analysis” and “due diligence” as use cases, and OpenAI demonstrated Deep Research on market examples (e.g., target market analysis). In tests, ChatGPT’s Deep Research unearthed niche competitors and provided deeper industry-specific insights, whereas Gemini’s output gave a polished strategic overview (even including product rankings and SWOT-style tables). A startup CEO used both for market research: ChatGPT structured insights with clarifying questions, while Gemini quickly generated a formatted report with graphs. Another user doing an M&A trends report found Gemini’s version to be faster and well-structured, while ChatGPT’s was more detailed (41 sources vs 20) and personalized. ## Integration with Business Workflows Both tools integrate well with business workflows: Gemini can export to Google Docs/Sheets, and ChatGPT can attach spreadsheets and share results via chat threads. For example, when comparing CRM software features or analyzing a new product launch, either agent can gather pricing, features, customer feedback, etc. However, Gemini’s multi-page reports may require more condensing to actionable slides. ### Consumer/Product Comparison Both tools excel at shopping and product research. OpenAI explicitly stated that Deep Research can “examine purchases such as cars and furniture.” Google’s site lists “product comparison” (e.g., comparing appliance models) as a use case. In an illustrative test (choosing a smartphone under $800), Gemini generated a 7,500-word report with side-by-side spec tables, pros/cons lists, and even product images. It ranked candidates and highlighted exactly the user’s criteria (charging, support) in each section. ChatGPT’s report was shorter and more cursory but still useful – it listed four phones with brief commentary and a simpler table. Neither was perfect: Gemini initially omitted one eligible model and misstated a release date, while ChatGPT picked an older model over a new one. Nonetheless, users agreed Gemini’s depth was impressive, and ChatGPT’s conclusions often aligned on the top choice. Other consumer tests (e.g., comparing airline loyalty programs or picking vacation destinations) show similar patterns: Gemini provides a granular, data-rich answer, while ChatGPT offers a succinct overview. For most buyers’ decisions, the best practice is to cross-check both tools’ outputs and then verify critical claims with traditional research. ## Summary In summary, ChatGPT’s Deep Research and Google’s Gemini Deep Research are both breakthroughs in AI-assisted research. ChatGPT offers flexibility and precision: it asks clarifying questions, produces concise reports with clear citations, and excels at analytic tasks (coding, summarization, academic-style queries). Gemini offers scale and structure: it rapidly produces long, formal reports with many tables and ties closely into Google’s tools. Empirical tests find Gemini often generates more content faster (though sometimes with outdated facts), while ChatGPT tends to use higher-quality sources and make fewer factual slips. Both tools cite sources and allow post-hoc inspection, greatly improving on earlier AIs. Neither replaces human judgment, but they complement workflows: for example, a business analyst might use Gemini for a broad market scan and ChatGPT for drilling into specifics. Our summary table above highlights their trade-offs. Ultimately, “which is better” depends on the task: Gemini is excellent for comprehensive, Google-integrated research tasks, and ChatGPT is ideal for targeted analysis and creative work. In any case, these Deep Research features represent a new paradigm: an AI “research assistant” that can save hours of effort – as long as users verify and interpret its findings. ## Sources Product documentation and blogs from OpenAI and Google; reviews and tests in AndroidAuthority, Section.ai, LivePlan, and G2, among others. These provide real-world comparisons of the tools’ accuracy, output, and usability in various use cases. ## Citations 1. [Introducing deep research | OpenAI](https://openai.com/index/introducing-deep-research/) 2. [ChatGPT's Deep Research vs. Google's Gemini 1.5 Pro with Deep Research: A Detailed Comparison | White Beard Strategies](https://whitebeardstrategies.com/ai-prompt-engineering/chatgpts-deep-research-vs-googles-gemini-1-5-pro-with-deep-research-a-detailed-comparison/) 3. [Gemini Deep Research — your personal research assistant](https://gemini.google/overview/deep-research/) 4. [I Tried ChatGPT and Google Gemini's Deep Research - LivePlan](https://www.liveplan.com/blog/planning/deep-research-chatgpt-vs-gemini?srsltid=AfmBOoqyqcsdSi9FM45cOb1F1Lxb-bW5XGe1k78wg9FgdslxeArL9Aon) 5. [Hands on with Deep Research - Leon Furze](https://leonfurze.com/2025/02/15/hands-on-with-deep-research/) 6. [Gemini's vs ChatGPT’s Deep Research: For me, the choice is clear - Android Authority](https://www.androidauthority.com/chatgpt-vs-gemini-deep-research-3555202/) 7. [Gemini: Try Deep Research and Gemini 2.0 Flash Experimental](https://blog.google/products/gemini/google-gemini-deep-research/) 8. [We tested two Deep Research tools. One was unusable.](https://www.sectionai.com/blog/chatgpt-vs-gemini-deep-research) 9. [OpenAI launches ‘deep research’ tool that it says can match research analyst | OpenAI | The Guardian](https://www.theguardian.com/technology/2025/feb/03/openai-deep-research-agent-chatgpt-deepseek) 10. [I Tested Gemini vs. ChatGPT and Found the Clear Winner](https://learn.g2.com/gemini-vs-chatgpt)
Watch this quick tutorial to see how easy it is to convert your ChatGPT research PDFs to clean Markdown format.
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Simple drag-and-drop interface. No technical knowledge required. Just upload and download.
Convert your ChatGPT research PDFs to Markdown in three simple steps.
Drag and drop your ChatGPT research papers or academic PDFs. Support for multiple files at once.
We use a mix of OCR, custom algorithms, and AI to extract.
Get clean, formatted Markdown files ready for GitHub, documentation, or further editing.
Everything you need to know about converting ChatGPT Research PDFs to Markdown
Converting ChatGPT deep research to markdown manually is extremely difficult and time-consuming. Copy-pasting loses formatting, manual conversion takes hours, and maintaining proper structure is nearly impossible. Our tool is the best way to convert ChatGPT deep research to markdown: Simply export your ChatGPT research conversation as a PDF (use your browser's print function and save as PDF), then upload it to our converter above. Within seconds you'll have a clean markdown file that preserves the research structure, questions, and responses in an organized format perfect for documentation or further editing - something that would take hours to do manually.
Our converter is optimized for text-based research documents, especially ChatGPT-generated research papers, academic articles, and structured documents. It works best with PDFs that contain selectable text rather than scanned images.
Our AI-powered conversion maintains high accuracy for formatting, headers, lists, and text structure. The system is specifically tuned for research documents and preserves academic formatting conventions.
Each PDF conversion costs 1 token. You can upload up to 10 files at once (maximum 4MB per file, up to 50 pages each). Additional tokens can be purchased in packages ranging from 10 tokens ($4.99) to 100 tokens ($29.99).
Yes, absolutely. Your documents are processed securely and never stored permanently on our servers. All files are automatically deleted after conversion, ensuring your research remains private and confidential.
Yes! Our batch processing feature allows you to upload and convert multiple PDF files simultaneously. You can upload up to 10 files at once, with each file being a maximum of 4MB. Each file will consume one token and be converted into a separate Markdown file.
You can upload a maximum of 10 PDF files at once, with each file being no larger than 4MB and up to 50 pages each. For optimal processing speed and accuracy, we recommend PDFs with around 10 pages or fewer. These limits ensure reliable conversion and good performance. If you have larger files or need to process more files, you can split them into multiple uploads.
Our converter produces clean, standard Markdown that's compatible with GitHub, GitLab, documentation platforms, and any Markdown editor. Headers, lists, emphasis, and document structure are preserved.
Join researchers and content creators who trust DeepResearch2Markdown for accurate PDF to Markdown conversion.