DeepSeek File Uploading: Supported File Types, Maximum Size Limits, Upload Rules, And Document Reading Features

DeepSeek provides file uploading capabilities primarily through its consumer-facing app and web chat interface, where uploaded documents are processed through text extraction and then used for analysis, summarization, and question answering.

File handling behavior differs significantly between the consumer interface and developer workflows, making it important to understand what is officially supported, what is implied by usage patterns, and what limitations apply in practice.

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DeepSeek Supports File Uploading In The Consumer App With A Focus On Text Extraction.

DeepSeek’s consumer app explicitly supports file upload combined with text extraction, allowing users to submit documents and interact with their contents conversationally.

Supported file categories are not exhaustively documented in official specifications, but common and consistently reported formats include PDF documents, office files such as Word and PowerPoint, spreadsheets such as Excel and CSV, plain text and Markdown files, and code-oriented text files.

Image files may also be accepted in some interfaces, though document workflows are primarily optimized for text-first content rather than image-heavy inputs.

The platform’s design emphasizes extracting readable text from files rather than preserving full visual fidelity or complex layout structures.

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Commonly Supported File Types In DeepSeek Consumer Uploads

File Category	Typical Formats	Primary Use
Documents	PDF, DOCX, PPTX	Summarization, Q&A, text extraction
Spreadsheets	XLSX, CSV	Table reading, numeric extraction
Plain text	TXT, Markdown	Direct analysis and transformation
Code files	Various text-based formats	Review, explanation, refactoring
Images	PNG, JPG	Limited, interface-dependent

These formats enable a broad range of document-centric use cases, provided the underlying text can be reliably extracted.

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Maximum File Size Limits Are Not Publicly Fixed And Can Vary By Interface.

DeepSeek does not publish a single, definitive maximum file size limit for uploads in its public documentation.

In practice, community reports and third-party testing indicate that the consumer web and app interfaces often enforce relatively modest per-file size limits, especially on free usage tiers. These limits appear to vary by file type, interface version, and system load, with smaller documents processing more reliably than large or dense files.

Because limits are not formally guaranteed, large documents frequently require splitting into smaller parts to avoid upload failures or partial extraction.

This variability means users should treat file size limits as dynamic rather than fixed and design workflows accordingly.

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Observed File Size Behavior In DeepSeek Uploads

Aspect	Typical Behavior	Practical Implication
Small documents	Upload and extract reliably	Best user experience
Large PDFs or spreadsheets	May fail or partially extract	Requires chunking
Multi-file sessions	Context can degrade over time	Stage uploads and prompts
Official hard limits	Not clearly published	Avoid relying on single large files

Managing file size proactively improves reliability across most DeepSeek sessions.

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Upload Rules Favor Text-First Documents And Incremental Workflows.

DeepSeek’s upload handling is optimized for text extraction rather than full document rendering.

Text-based PDFs and digitally generated documents tend to work best, while scanned or image-only files depend on extraction quality and may produce incomplete results. Documents with complex layouts, dense tables, or embedded graphics may lose structural fidelity during extraction.

Long documents or multi-file workflows are constrained by context limits, meaning that only a portion of extracted text can remain actively usable at one time. As a result, incremental prompting and staged uploads are more effective than attempting to process everything in a single interaction.

Password-protected or encrypted documents are generally not suitable for upload unless unlocked beforehand.

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Document Reading Features Are Centered On Post-Extraction Analysis.

Once text is successfully extracted, DeepSeek enables several document-oriented interactions.

Users commonly rely on the platform for summarizing long reports, answering questions about specific sections, extracting named entities or numeric values, and performing light analysis on tabular data from spreadsheets.

Table understanding works best when tables are cleanly structured and text-based rather than embedded as images. Layout awareness is limited, meaning that headings, sections, and tables may not always be perfectly reconstructed, especially in visually complex documents.

The quality of document reading is therefore directly tied to the quality of the initial extraction step.

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Document Reading Capabilities In DeepSeek

Capability	Description	Reliability Factors
Summarization	Condenses extracted text	Depends on extraction completeness
Question answering	Answers based on document content	Strong for clear text sections
Field extraction	Pulls quotes, values, entities	Best with structured text
Table reading	Interprets spreadsheet data	Works best with clean tables

DeepSeek excels at reasoning over extracted text rather than reproducing exact document layouts.

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Developer Workflows Rely On External Text Extraction Rather Than Native File Uploads.

DeepSeek’s public API documentation focuses on chat and completion endpoints and does not present a dedicated, first-class file upload or storage API.

Developers typically handle file ingestion by extracting text themselves using PDF parsers or OCR tools and then injecting that text directly into prompts. This approach mirrors the internal behavior of the consumer app but places responsibility for extraction quality on the developer.

As a result, file uploading in DeepSeek should be viewed as a consumer convenience feature rather than a fully specified developer-grade document ingestion system.

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