ChatGPT vs. Claude for File Upload & Reading Capabilities: Full Comparison and Report. Models, File Support, Performance, Use Cases, Pricing
- Graziano Stefanelli
- Jun 13
- 24 min read
Updated: Jul 19
The most recent advances from OpenAI and Anthropic have expanded what is possible when uploading and reading files through AI assistants. Today’s ChatGPT and Claude platforms both allow users to attach a wide variety of files—such as PDFs, Word documents, spreadsheets, code, images, and more—directly into the chat, transforming static documents into interactive resources for analysis, summarization, and problem-solving.
ChatGPT enables users to upload multiple files per session, supporting large documents and diverse file types. It excels in processing multimedia content: not only can it extract and interpret text from documents, but it also analyzes images, reads tables and charts, and even handles audio and video inputs for transcription or analysis. Its built-in code execution environment means that users can upload datasets or scripts and receive real-time computation, automated data cleaning, or even debugging—all within the same chat.
Claude has carved out a distinct edge in managing extremely long or complex files. Thanks to its massive context window, users can upload hundreds of pages or multiple documents at once, and Claude will read, remember, and analyze details across the entire batch without losing track. The system automatically extracts and interprets text from supported files and, with the latest vision enhancements, can also reason about images and diagrams inside those documents. Claude’s user interface supports uploading many files at once and enables collaborative editing of AI-generated outputs through an “Artifacts” pane, where users can iterate on drafts, reports, or code in a side-by-side view. Its particular strength is deep, exhaustive reading—ideal for legal documents, technical manuals, and large data exports—providing users with precise retrieval, summarization, or cross-document comparison on demand.
Both systems now support detailed file handling across free, pro, and enterprise tiers, with varying limits on file size, file count, and advanced features. The practical differences are not only in the types and sizes of files that can be uploaded, but also in the speed, accuracy, and depth with which each platform processes and understands file content.
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Latest Model Versions (Mid-2025)
ChatGPT (OpenAI): The newest flagship model is GPT‑4o, an upgraded version of GPT-4 that is multimodal (accepts text, images, audio, etc.) and tuned for complex reasoning. ChatGPT’s free tier still uses the older GPT-3.5, while Plus and Enterprise users get access to GPT-4o (and other specialized “o-series” models for reasoning tasks). GPT-4o is currently the top choice for most prompts and tool-based tasks, combining advanced reasoning with multimodal input/output capabilities. Notably, GPT-4o can handle very large inputs (up to ~128k tokens of text in recent tests) and produce outputs in various formats (text, images, even audio), making it a versatile model for file analysis and content creation.
Claude (Anthropic): Anthropic’s most recent models belong to the Claude 4 family, with Claude Opus 4 and Claude Sonnet 4 representing the current top tier for advanced AI file interaction. Claude Opus 4, launched in May 2025, is the flagship model and is designed for highly complex, open-ended reasoning, agentic workflows, and coding. Sonnet 4, which supersedes the previous 3.7 Sonnet, is built for high efficiency and general-purpose use at lower cost. Both models support hybrid reasoning, enhanced memory, parallel tool use, and highly nuanced instruction following, making them even more capable than the Claude 3 series. For file analysis, Claude 4 models allow uploading a wide range of document, spreadsheet, and image formats, and can process extremely large files—context windows extend up to 200,000 tokens (with enterprise plans supporting even more), enabling in-depth analysis and summarization of hundreds of pages in a single session. These models also feature improved extraction of information from images, charts, and tables within uploaded documents, closing the multimodal gap with OpenAI’s GPT-4o and GPT-4.5 Orion models. In short, Claude 4 Opus and Sonnet now set the benchmark for document and data analysis, long-context understanding, and interactive report generation within Anthropic’s ecosystem.
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File Upload Functionalities
Supported File Types
ChatGPT: ChatGPT Plus/Enterprise users can upload and work with virtually all common document formats. This includes text files (TXT, Markdown, HTML, JSON, etc.), documents (PDF, DOC/DOCX, ODT, RTF, PPT/PPTX), spreadsheets (CSV, XLSX), and images (PNG, JPG, etc.). In fact, GPT-4o’s multimodal capability means it can even accept audio or video inputs (e.g. for transcription or analysis) and produce rich outputs (it can generate images or spoken responses), though these features are primarily available through specific interfaces or API routes. For most users, the ChatGPT web and mobile apps allow attaching files or dragging-and-dropping them into the chat. The AI then processes the file’s content (text is read directly; images are analyzed via GPT-4o’s vision; audio is transcribed by Whisper, etc.).
Claude: Claude.ai also supports a wide range of file types for upload. Officially supported formats include PDFs, Word documents (DOC/DOCX), spreadsheets (CSV, XLSX), text files (TXT, HTML, JSON, etc.), ebooks (EPUB), and more. Essentially, most common text-based documents are accepted. Anthropic has confirmed that images can be uploaded to Claude as well (e.g. PNG or JPG files). When an image or a PDF with images is provided, Claude 3’s vision model can interpret the visual elements in many cases. (There are some limits: for instance, Claude will analyze visual elements in PDFs only if the PDF is under 100 pages and using a vision-capable model like Claude 4 or Claude 3.5/3.7 “Sonnet.” Longer PDFs, or non-vision models, will fall back to text-only OCR extraction.) For non-PDF documents, Claude currently extracts and reads only the text content – images inside, say, a DOCX or PPTX won’t be interpreted. Overall, both ChatGPT and Claude cover all typical file types one would use for text analysis, data, or coding, with ChatGPT having an edge in built-in multimedia handling (audio/video) and Claude focusing on text and images.
File Upload Limits and Interface
ChatGPT: OpenAI significantly expanded ChatGPT’s file-handling in 2025 with the introduction of the new file upload feature (built on the Advanced Data Analysis tools, formerly Code Interpreter). Users can attach up to 20 files per chat (or per custom GPT workspace). The interface supports multi-file upload in one go – you can drag-and-drop or click an attachment icon to select files, which then appear as uploaded “bubbles” in the chat. Each individual file can be quite large: 512 MB per file is the hard limit. In practice, text documents are additionally limited to ~2 million tokens of content each (the system will truncate or ask to summarize beyond that), and spreadsheets are limited to ~50 MB (since extremely large sheets are impractical to handle). Images can be up to 20 MB each. Free-tier users have very restrictive limits (only 3 file uploads per day on the free plan), whereas Plus users can upload as many as 80 files every 3 hours on GPT-4o before hitting a cap. ChatGPT’s interface does not display the full content of an uploaded file in the chat window (it just shows the file name and size), but the AI can summarize or quote from it on request. There’s no built-in file preview for, say, a PDF – you rely on ChatGPT’s analysis to get the content, or you can download any transformed output it produces. One notable advantage of ChatGPT’s approach is that it actually executes code and tools to process files behind the scenes. When you upload complex files (data, code, PDFs), GPT-4o can use Python scripts (in a sandbox environment) to parse PDFs, read spreadsheets or even run analysis on data. This means ChatGPT can handle tasks like reading a 300-page PDF by programmatically chunking it and summarizing, or executing SQL on a CSV – all automatically. The user experience is streamlined: just attach files and ask questions or give instructions; ChatGPT will figure out how to parse and analyze them (combining or comparing multiple files as needed). The Plus UI also integrates features like image preview and editing (for example, on the mobile app you can send a photo and even highlight regions for GPT-4o to examine). Overall, ChatGPT’s file upload tool feels like having a data assistant that can code, analyze, and report on your files within the chat.
Claude: Claude.ai’s interface likewise allows attaching multiple files within a conversation. Users can upload up to 20 files per chat session on Claude. Each file is limited to 30 MB in size. Unlike ChatGPT, which has a notion of persistent “chat sessions,” Claude also offers a Project workspace (knowledge base) where you can upload an unlimited number of files for long-term reference – with the caveat that the total content you use at once still must fit in the model’s context window. (In other words, you could build a repository of documents, but when querying, Claude can only draw on ~200K tokens worth of that content at a time.) Uploading files to Claude is straightforward: you click the paperclip (or drop files) and they appear listed in the chat sidebar or area. Claude will automatically extract text from each document upon upload. For supported text-based formats, it reads in the full text. If the file is a PDF under 100 pages and you’re using a vision-capable model (e.g. Claude 3.5/3.7 Sonnet or Claude 4), Claude will also analyze images/charts within the PDF. However, Claude’s interface does not render the file content visually for the user – it works behind the scenes. You might, for example, upload a PDF and then ask Claude “What does the chart on page 5 indicate?” and it will describe it, but you won’t see the chart image in the chat (similar to ChatGPT’s behavior). One unique feature Claude introduced is Artifacts: certain outputs (like a long document draft, code file, or simulation) can open in a side-by-side editor panel, rather than clogging the main chat. This lets you collaboratively develop longer content with Claude – you can edit or iterate on a draft in the artifact pane and see updates in real time, then download the final file when done. In essence, Claude’s UI is geared toward collaborative document creation and editing, whereas ChatGPT’s UI keeps everything in the chat transcript or as downloadable files. Both platforms support multi-file analysis (e.g. “compare these two documents” or “combine data from file1.csv and file2.csv”), but ChatGPT’s approach may involve more behind-the-scenes coding to merge data, while Claude uses its large context to hold multiple files’ content at once.
Uploading Code & Formats for Coding Tasks
Both ChatGPT and Claude can ingest code files (as plain text or in supported formats like .py, .js, .java, etc.) and other structured text like JSON or YAML. ChatGPT (with Advanced Data Analysis) allows uploading source code files or even a ZIP archive of an entire project. It will unpack and read multiple files, enabling it to answer questions about the code or even execute parts of it. For example, you can upload a Python script and ask ChatGPT to debug it; GPT-4o will run the code in a sandbox to observe errors or outputs, then suggest fixes – a powerful capability for debugging that Claude lacks. Claude, on the other hand, leverages its 100K+ token context to handle very large codebases in memory. You can paste or attach large chunks of code (or many files) and Claude will remember all of it. This is ideal for understanding cross-file relationships or generating documentation for an entire repository. In fact, Claude’s Enterprise offering includes direct integration with GitHub: engineers can link Claude to a repo and ask questions about the codebase, get code review suggestions, etc., all powered by Claude reading the code in context. In terms of interface, ChatGPT’s code interpreter will present any program output (logs, images from code, etc.) in the chat, and allow downloading results. Claude’s new artifact feature similarly can show generated code in an editor pane for review. Overall, for coding workflows, ChatGPT provides a more automated runtime environment for code, whereas Claude provides a larger reading capacity for code. We’ll compare their performance on coding next.
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Performance in File Reading & Analysis
Document Summarization and Q&A
Context Length & Comprehensiveness: Claude has a clear advantage in raw context capacity. With a 200K token window (and up to 500K for some enterprise users), Claude 3 can summarize or analyze extremely long documents in one go. Users have reported Claude maintaining a coherent understanding of 100+ page documents without losing track, whereas ChatGPT (with a ~32K-128K token limit on GPT-4o) struggled beyond ~50 pages in direct tests. This means if you feed a very large report or book, Claude is more likely to handle it gracefully in a single session, capturing details from beginning, middle, and end. ChatGPT may need to chunk the text (either automatically via its tools or manually by the user) and might miss some context if not managed carefully. However, within moderate lengths (say, dozens of pages), ChatGPT’s summaries are on par with Claude’s and often more structured. In one comparison, a long text summary by Claude was concise and informative, but ChatGPT’s was more detailed and sectioned – reflecting GPT-4o’s strength in following explicit formatting instructions and organizational style. ChatGPT also excels at pulling out specific information when asked (thanks to its strong reasoning and perhaps slightly more cautious approach to facts), but if the information lies far back in a very lengthy text, Claude’s near-eidetic recall shines. Anthropic reported that Claude 3 achieved 99%+ accuracy on a “needle in a haystack” retrieval test (finding specific sentences buried in huge text corpora), indicating it seldom misses relevant details even in massive contexts. Practically, for Q&A on a document – e.g. “What does the contract say about termination clauses?” – both models do well. Claude might have an edge in very long or technical documents (it can quote the exact clause from 300 pages ago), while ChatGPT might sometimes summarize or analyze the clause more critically if within its grasp. It’s also worth noting Claude’s style tends to be very factual and neutral in summaries, whereas ChatGPT can be tuned to be more interpretive or creative as needed.
Handling Visual Data in Documents: ChatGPT (GPT-4o) can analyze images, charts, and tables within documents, which is a key advantage for reports or PDFs with mixed content. For instance, ChatGPT can read a research paper PDF and describe what a complex chart means, or parse a table of data and perform calculations or visualizations on it. Claude 3, in contrast, is primarily text-focused for documents. Claude 3.5 “Sonnet” does not interpret images or graphics in PDFs – it will ignore or skip them beyond simple OCR of captions. (Claude 4 Opus is expected to improve on vision, but at mid-2025 Claude’s multimodal vision was mainly advertised for standalone images, diagrams or smaller documents.) In summary, Claude is superior for very large, text-heavy documents (e.g. lengthy legal texts, books) where retaining every detail is important, while ChatGPT is superior for documents that include visuals or need on-the-fly calculations, such as slide decks with charts, data-rich PDFs, or forms. According to one analysis, “Claude is better for long, text-heavy documents, while ChatGPT is ideal for PDFs with charts, tables or visuals.”.
Accuracy and Hallucination: Both GPT-4o and Claude 3 are high-performing in extracting facts from given files, but ChatGPT has a slight edge in avoiding hallucinations when uncertain. GPT-4’s training and OpenAI’s tuning have made it quite reliable at saying “I don’t know” or using provided sources. One reviewer noted that switching from ChatGPT-4 to Claude 3.5 felt “jarring because [Claude] gave a lot of incorrect information” in factual scenarios. Claude currently lacks a web-browsing feature, so it cannot double-check facts against the internet, whereas ChatGPT Plus can use a built-in browser to verify information and even return sources. This means for research questions or up-to-date factual queries based on file content, ChatGPT might provide citations or at least cross-verify with external data (if you enable browsing), reducing the chance of error. That said, within the scope of a provided document, both models generally stick to the source – Claude especially will quote verbatim passages when appropriate, given its recall ability. For critical factual analyses, ChatGPT’s conservative approach and tool use give it a reliability advantage, whereas Claude’s strength is comprehensiveness (it won’t omit an important detail even if it’s 200 pages deep).
Coding Tasks (Code Understanding, Debugging, Documentation)
Code Understanding: Both ChatGPT GPT-4 and Claude are proficient at reading and explaining code. GPT-4 has established itself as an excellent coding assistant, scoring 84%+ on coding benchmarks like HumanEval (Python problem-solving), significantly higher than Claude’s scores (~64% on similar tests). In practical terms, ChatGPT is often better at reasoning through algorithmic problems and fixing tricky bugs. It not only explains what a piece of code does, but with Advanced Data Analysis it can actually run the code, observe the output or errors, and adjust its answer. This execution capability is hugely beneficial for debugging: ChatGPT can iterate through a failing code snippet, run tests, and correct mistakes step by step. Claude does not execute code; it approaches debugging by static analysis – reading the code and reasoning about it. Claude’s analyses are quite strong (and its large memory means it can consider a whole project’s context when diagnosing an issue), but it might miss issues that only manifest at runtime.
Multi-File and Large Codebases: Claude’s advantage is handling scale. If you give Claude an entire repository (thousands of lines across many files), it can keep all that context at once and answer high-level questions like “How do these modules interact?” or “Find any potential security vulnerabilities in this codebase.” ChatGPT with GPT-4 (8K or 32K context for most users) might need a more piecemeal approach – for example, analyzing one file at a time or summarizing parts of the codebase before it can reason about the whole. However, ChatGPT’s tools partially offset this: one can upload a zip of a project, have ChatGPT selectively open files, summarize each, and then ask it to combine the info. This is more laborious, whereas Claude can directly ingest large swaths of code up front. Moreover, Claude’s enterprise “Claude for Code” offering (for Pro/Max subscribers) streamlines working with codebases by integrating with developer workflows (terminal integration and GitHub). For instance, a developer can use Claude in the terminal to query the codebase, and Claude will utilize its context window to provide answers or even generate new code, treating the repository as context. ChatGPT doesn’t have a native “codebase mode” yet, though third-party plugins and GitHub’s Copilot Chat serve similar purposes in the IDE.
Code Generation & Documentation: When it comes to generating new code or writing documentation, both models are capable, but they have different styles. ChatGPT tends to produce very well-formatted, structured outputs (including full code with comments, or neatly organized documentation sections) thanks to its training and perhaps a bit more “training on code reference materials.” Claude is quite capable too and sometimes gives more extensive explanations in plain language. Users often find Claude’s writing to be a bit more verbose and philosophically inclined, which can be helpful for understanding but sometimes needs refocusing for concise documentation. ChatGPT, especially when instructed to be brief, will produce tighter descriptions of code functionality. On the flip side, Claude’s willingness to supply extra detail can mean it documents edge cases or rationale in depth. For generating examples or usage documentation from code, both do well; ChatGPT might integrate external knowledge (e.g. known best practices) a bit more readily, whereas Claude sticks closely to the code given. One important difference: ChatGPT Plus can use plugins or tools for coding – for example, it can call a compiler or linter and then inform you of syntax errors or stylistic issues. Claude doesn’t have plugin architecture accessible to end-users in mid-2025.
Speed: In coding tasks, speed can matter when iterating. Claude 3, particularly the “Haiku” and “Sonnet” variants, is designed to be fast. Claude Haiku can skim a 10K-token research paper (or similarly sized code) and respond in under 3 seconds in some cases. Claude Sonnet is reported to be 2× faster than the previous Claude 2 model while handling complex tasks – meaning it’s generally very responsive even with large inputs. ChatGPT’s GPT-4, historically, was slower in interactive coding (often a few seconds of “thinking” and then a typed-out answer). With GPT-4o, performance improved, but it’s still not unusual for a large code explanation or multi-step solution to take several seconds or longer, streaming out token by token. Claude often outputs in larger chunks, making it feel instantaneous for shorter answers. That said, when running actual code (in Advanced Data Analysis), ChatGPT’s speed depends on the execution time of the code – it could be slower if it’s crunching data or running tests, whereas Claude would just analyze text and respond. In general, for pure Q&A or explanation on code, Claude might return an answer slightly faster; for debugging that requires running the code or trying various fixes, ChatGPT achieves a quicker resolution because it can dynamically test solutions rather than hypothesize repeatedly.
Overall Accuracy and Reliability
Both platforms are highly advanced, so differences in accuracy will depend on the task: GPT-4o still holds the crown in many standard benchmarks (logic puzzles, math word problems, coding challenges) with a comfortable margin. Claude 3’s improvements have narrowed the gap in reasoning and made it less likely to refuse valid requests, but it occasionally overconfidently asserts something incorrect (especially without tools or references). ChatGPT’s answers, especially when tools are involved or with GPT-4o’s latest tuning, tend to be very reliable and precise, albeit sometimes too cautious or slow. One area Claude appears to equal or surpass GPT-4o is in recall and extracting factual info from provided text – as mentioned, Claude 3 achieved near-perfect recall in tests of retrieving facts from a large text corpus. In real usage, this means Claude is excellent for tasks like, “Find every time project X is mentioned across these reports” or “Extract the key requirements from all these specification documents,” producing exhaustive, accurate outputs. ChatGPT can certainly do the same, but if the volume is huge, it may summarize more and potentially miss minor points unless asked carefully.
Use Case Suitability: A balanced view, echoed by many users, is that each model excels in different scenarios. Claude (especially Sonnet/Opus) is the go-to for deep analysis of large or numerous files – it can digest hundreds of pages or multiple documents and provide a comprehensive analysis in one shot. It’s also great for brainstorming and iterative writing with those files, thanks to the artifact feature and its willingness to follow long, structured instructions. ChatGPT is often preferred for interactive, tool-augmented tasks – if you have a smaller dataset or a moderate-length document and you want quick insights, charts, or code executions, ChatGPT will do more for you (e.g. generate a visualization from your CSV, or fact-check a statement via web search). ChatGPT also wins for any task that involves image generation or advanced multimodal output (Claude cannot produce images or speak audio; GPT-4o can generate images via DALL·E integration and even voice responses on some platforms). In summary, Claude is a clear winner for longer, complex documents, providing detailed and coherent analyses at scale, whereas ChatGPT is more versatile and typically more reliable for coded tasks, factual queries, and multimedia-rich content. Many power-users leverage both: they might use Claude to summarize or extract from a huge file, then feed that summary to ChatGPT to refine, verify, or elaborate with external knowledge.
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Pricing Tiers and Feature Differences
Both ChatGPT and Claude offer free access as well as paid tiers, but their approaches differ in target audiences and limits:
ChatGPT Free (OpenAI): Free users can access the baseline GPT-3.5 model with no ability to upload files or use advanced features. Interaction is limited to text prompts (and maybe image input in the mobile app). There is also a fairly low message cap (e.g. GPT-4 is not available on free, and even GPT-3.5 may have a rate limit per hour). Recently, OpenAI has allowed free users limited file uploads (up to 3 files per day) for trial purposes, but the GPT-3.5 model itself is not as skilled at analyzing those files compared to GPT-4o, so serious file analysis still effectively requires a paid plan. In short, free ChatGPT is powerful for conversational answers but lacks file upload capability in any practical sense, as well as other enhancements like longer context or multimodal input.
ChatGPT Plus ($20/month): Plus subscribers get priority access to GPT-4o and all advanced features. This includes the ability to upload files (20 per session, 512 MB each as detailed earlier), use Advanced Data Analysis (running code, working with data), use Browsing, and use image analysis/generation features. Plus users have much higher usage caps: for instance, they can upload up to 80 files per 3 hours to GPT-4o and generally have faster response speeds. The context window available on Plus is also larger – GPT-4o on Plus supports 128K tokens input according to some reports (the standard GPT-4 model was 8K, and a 32K version is likely available or in testing for Plus/Enterprise). Essentially, Plus unlocks ChatGPT’s full potential for individual power users or small businesses. All file types and analysis tools are available here.
ChatGPT Enterprise (and Team/Pro plans): For organizations, OpenAI offers ChatGPT Enterprise (with custom pricing) which features unlimited GPT-4 access, higher performance and reliability, and enhanced privacy (no training on your data) – plus some extras relevant to file usage. One key feature: Enterprise users get “Visual Retrieval” for PDFs, meaning GPT-4o on Enterprise can actually interpret images inside PDFs (charts/graphs) and perhaps more accurately handle formatted content. Enterprise accounts also come with longer context windows by default (at least 32K tokens, and possibly the full 128K in GPT-4o if available) and higher file retention limits (files might be saved longer for your account, and data controls let you manage retention). The Enterprise tier is designed for heavy-duty use: no daily caps, priority processing even during peak times, and the ability to share custom GPTs (with preloaded knowledge or instructions) across a team. Another difference is integration – ChatGPT Enterprise can integrate with company knowledge bases or applications (OpenAI provides an API and also allows plugins on Enterprise, so one could connect internal databases, etc.). In summary, free vs Plus vs Enterprise on ChatGPT mainly affects capacity and access to features: free has none of the file abilities, Plus has all features with some limits, Enterprise increases those limits and adds professional integrations and data guarantees. Notably, the price jump from $20 Plus to Enterprise is significant, but Enterprise is the only tier where you get things like guaranteed 32K+ context and certain vision features on files.
Claude Free (Claude.ai Beta): Anthropic has an open beta web interface for Claude that anyone can sign up for. The free tier allows a limited number of messages per day (the exact quota “varies based on demand” and resets daily, but users report something on the order of maybe 10–20 messages a day in practice). Free users on Claude.ai have access to the same Claude 3 model (often Claude Instant or Claude 3.5 depending on what’s available) for chatting, including the ability to upload files. So unlike free ChatGPT, free Claude does let you attach documents or images in the chat. The main limitation is volume: if you have a lot of questions or large files, you will quickly hit the daily limit on the free plan. Additionally, heavy features like the 100K context might implicitly be curtailed if the message limit is hit (because one large prompt counts as many messages worth of tokens). Free Claude is a good way to test the service, but for regular use (especially with file analysis), you’d need to upgrade once you run into the limits.
Claude Pro ($20/month): The Pro plan (comparable in cost to ChatGPT Plus) increases your usage limits roughly 5× over free. This means you can send many more messages per day – on the order of dozens of prompts in a 8-hour span, depending on size. Pro users also get priority access to new features and models. For example, Claude 3.5 Sonnet (with the full 200K context and faster speeds) is available to Pro users, whereas free users might have been on a slightly lower tier model or had slower response during overload times. All file upload features (20 files per chat, 30MB each, etc.) are fully enabled for Pro. Anthropic does not impose specific file count caps like OpenAI does, but general usage is governed by the token limits and daily message limits. Pro users can likely utilize the Claude analysis tool (which is needed for some file types like XLSX spreadsheets) – this tool can be toggled on in the account settings for advanced document parsing. Pro also includes Claude’s coding assistant features to an extent: Anthropic has a separate Claude Code CLI tool for developers, and Pro subscribers can use Claude Code for “light coding work on small repos” as part of their plan. In terms of model access, Claude Pro gives you the high-performance Claude 3.5 Sonnet model by default. (Claude 3 Opus, the very top model, might not be included in the $20 plan’s real-time chat due to its higher compute cost – Anthropic’s documentation suggests Opus 4 is only available on higher tiers like the Max plan or enterprise. If Claude 3 Opus is available, it may be via a pay-as-you-go API or a more expensive plan.)
Claude “Max” and Enterprise: Anthropic has tiered plans above Pro for power users and organizations. The Max plan (priced at $100/mo for 5× Pro usage, or $200/mo for 20× Pro usage) is aimed at individuals or teams that need a lot more throughput. These plans drastically raise the message limits (hundreds of messages per 5-hour window) and also unlock full Claude 4 Opus model access in some cases. For example, Max plan users can switch the Claude model to Opus (for maximum quality) or use Sonnet for faster responses, as needed. Claude Enterprise (for companies) builds on this with organization-wide features: enhanced security (SSO, encryption, audit logs), custom data retention policies, and the ability to integrate Claude with internal knowledge sources. Critically, Enterprise unlocks the highest context windows: up to 500K tokens with Claude 3.7 (an enterprise-specific model iteration). It means enterprise users could upload or reference hundreds of thousands of tokens of documents (e.g. “hundreds of sales transcripts, dozens of 100+ page documents, and 100K lines of code” as Anthropic describes) in a single session. Enterprise Claude also offers native integrations (like the GitHub connection for code, and likely other data connectors) to enable workflows like analyzing a whole repository or a large database with AI. In summary, Claude’s free and Pro are aimed at individual usage with generous context and file support, while the higher tiers are about scaling up usage and context for heavy-duty needs. Notably, Anthropic’s pricing beyond Pro is usage-based (the more you pay, the more messages/compute you get), whereas OpenAI’s Enterprise is more of an “all-inclusive” custom deal but with an emphasis on unlimited use and premium features.
Finally, when comparing free vs paid: ChatGPT’s free version is more limited relative to its paid version than Claude’s free vs paid. Claude’s free beta gives a taste of the full model (with file uploads and big context, just capped by daily uses), whereas ChatGPT’s free tier is a completely lower-grade model without file capabilities. On the high end, both offer enterprise solutions that remove most limits and target different needs (OpenAI focusing on tool integrations and data control, Anthropic on massive context and workflow integrations).
Below is a comparison table summarizing key differences between ChatGPT and Claude in mid-2025 regarding file upload and reading capabilities:
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