Claude AI Document Reading: supported formats, limits, and long-context capabilities

Graziano Stefanelli
1 day ago
5 min read

Claude AI has become a complete document analysis system that blends long-context understanding with native file reading. It allows users to upload PDFs, spreadsheets, and text documents, process scanned materials with visual reasoning, and reuse files across projects. In 2025, the platform extends these features through both Claude.ai and the Anthropic API, adding greater file capacity, persistent knowledge bases, and advanced OCR-style recognition for complex layouts.

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How Claude reads documents and where it is available.

Claude’s document-reading features operate across three main environments:

Claude.ai (web and desktop): users can upload documents directly within a chat or into a Project, where files become part of a reusable knowledge base. Each project retains its own file library, enabling Claude to answer questions across all included sources.
Anthropic API: developers can submit documents through the Messages API or upload large assets to the Files API for persistent access. The Files API supports heavy workloads like long reports, research papers, or codebases.
Amazon Bedrock (Claude on AWS): organizations using AWS services can integrate Claude with controlled file attachments and predictable size caps, suitable for enterprise applications where data residency and compliance are critical.

Together, these options provide continuity between individual and enterprise use, allowing the same models to process both private files and large document repositories.

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Supported file formats and typical use cases.

Claude reads an extensive range of document formats commonly used for professional and technical work. The supported extensions include PDF, DOCX, TXT, HTML, RTF, CSV, JSON, ODT, EPUB, and XLSX.

PDF: primary format for reports, research papers, contracts, and academic material. Claude handles both selectable text and embedded images within PDFs.
DOCX and RTF: ideal for manuscripts or internal memos, providing consistent formatting.
CSV and XLSX: useful for structured data tables; Claude can summarize or extract figures directly.
TXT, JSON, and HTML: standard for unformatted data or web-based material.

In each case, Claude converts the file into tokenized text representations while preserving layout and table relationships as much as possible.

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File size and upload limits across environments.

File capacity varies depending on whether the document is uploaded through the web interface, the API, or Bedrock.

Platform	Upload Method	Maximum File Size	Notes
Claude.ai (chat or project)	Direct upload	Tens of MB per file (adaptive limits)	Files stored within the project library; best for individual research.
Anthropic API – Messages endpoint	Inline content	32 MB total request size (prompt + file)	Suitable for small to medium files. Larger inputs return an error.
Anthropic API – Files endpoint	Separate upload reference	500 MB per file	Recommended for high-volume workloads or large documents.
Amazon Bedrock (Claude)	In-message attachments	Up to 5 documents, each ≤ 4.5 MB, and 20 images ≤ 3.75 MB	Ideal for regulated or multi-user systems with AWS data controls.

This configuration allows developers and enterprise users to balance convenience and capacity. The Files API remains the most scalable route for long documents or repeated analysis, while Claude.ai offers straightforward drag-and-drop uploads for everyday use.

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How Claude handles long documents and context windows.

The Claude Sonnet 4 model supports up to 1 million tokens of context in a single request, allowing entire books, legal codes, or research archives to be processed at once. The long-context window is used intelligently: Claude loads document segments into memory dynamically, summarizing or retrieving relevant portions based on user prompts.

This approach lets users query large collections—such as “Summarize all sections discussing IFRS treatment of deferred taxes” or “Extract all tables mentioning EBITDA margins”—without manually segmenting the files.

Even with a million-token limit, efficiency improves when prompts are scoped clearly. Section-based instructions (for example, “Analyze pages 50–75 of Report_A.pdf”) maintain higher response precision and reduce processing time.

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Reading scanned PDFs and images.

Claude can interpret both the text and visual content inside a document. When a PDF contains scanned pages or embedded charts, the model applies vision-based OCR reasoning to reconstruct the content. It detects table borders, headings, and image-embedded text, producing a hybrid analysis that combines layout awareness with textual understanding.

For low-quality scans, users can improve results by:

Uploading page-level images instead of the full document.
Using high-resolution (300 dpi+) scans to preserve fine text.
Requesting structured outputs such as CSV or JSON to force consistent data formatting.

This hybrid OCR approach is particularly effective for financial statements, invoices, academic papers, or research datasets that mix text, tables, and images.

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Project-based memory and persistent knowledge.

Claude’s Projects feature creates a persistent workspace where uploaded documents remain accessible across multiple chats. Within a project, users can:

Upload and organize numerous files into a dedicated repository.
Ask cross-document questions such as “Compare trends in both quarterly reports” or “Summarize key policy changes across all PDFs.”
Add new files gradually, maintaining continuity without re-uploading previous data.

This system functions as medium-term memory, retaining content within the project scope while maintaining strict privacy and isolation between different projects. Developers achieve similar persistence using the Files API, which stores documents for reuse in multiple programmatic sessions.

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Creating and editing documents after reading.

After analyzing a file, Claude can generate new documents using the Create and Edit capability or through Artifacts. These outputs may include:

Summaries or briefs in DOCX or TXT format.
Extracted datasets converted into CSV files.
Generated presentations or formatted PDFs.

Artifacts appear beside the chat window in Claude.ai, allowing real-time refinement of generated content without leaving the workspace. This dual functionality—reading and authoring—positions Claude as both a document interpreter and content producer.

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Practical workflow for document analysis.

Prepare the files. Ensure PDFs are searchable or use high-resolution scans for visual documents.
Upload efficiently. Use Claude.ai for personal projects; use the Files API for larger datasets or batch workflows.
Ask scoped questions. Specify page numbers, headings, or sections rather than requesting full-document summaries.
Use structured outputs. Request JSON or CSV formatting for tables, lists, and statistical data.
Validate extracted information. Have Claude re-quote or cross-reference key figures for verification.

Following this method keeps performance consistent even with complex or long-form materials.

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Comparison between Anthropic API and Bedrock for document workflows.

Criterion	Anthropic API	Amazon Bedrock
Context length	Up to 1M tokens (Sonnet 4)	Varies by deployment; typically shorter
Upload capacity	32 MB via Messages; 500 MB via Files API	4.5 MB per document, 5 documents per request
File persistence	Files API (stored & reusable)	Temporary per session
Use case	Deep reading and persistent analysis	Cloud-based business integrations

This comparison shows that Anthropic’s own API is ideal for high-volume, research, or archival work, while Bedrock emphasizes stability and governance for AWS-linked enterprise systems.

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Summary of Claude’s document reading capabilities in late 2025 and beyond

Claude AI now provides a unified workflow for reading, interpreting, and reusing documents at scale. Its combination of long-context processing, OCR-based vision, and persistent project memory allows users to interact with thousands of pages of data within a single conversational environment.

Users handling corporate filings, policy reports, or academic materials can rely on Claude for detailed, structure-aware reading, while developers can extend the same capability through the API or Bedrock. With the 1M-token context and 500 MB file support, Claude has established itself as one of the most flexible document intelligence systems available in 2025.

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DATA STUDIOS

.....[datastudios.org]