DeepSeek: What File Types Can Be Uploaded and How They Are Processed
- Graziano Stefanelli
- Sep 30
- 3 min read
DeepSeek has emerged as a versatile large language model with both text and vision capabilities. Among its practical uses is the ability to read and analyze files uploaded directly into the chat interface. While the public API is primarily text-centric, the consumer apps on web and mobile support direct file attachments, enabling users to ask questions about documents, spreadsheets, and images. Understanding which file formats are supported, and how DeepSeek handles them, is essential for making the most of the platform.
File uploads in DeepSeek’s chat interface.
DeepSeek advertises file upload and text extraction as a core feature of its app. Users can drag and drop documents or select them from local storage, after which the model interprets the content and allows queries. This is the most direct way to analyze documents without setting up a custom retrieval pipeline.
The range of formats supported is not exhaustively listed in official documentation, but community experience and integration guides provide a clear picture. In practice, DeepSeek can handle PDFs, Office files, plain text and Markdown, spreadsheets, and images.
Document formats supported.
The most reliable way to interact with DeepSeek is through standard document files.
PDF: Widely supported for reports, research papers, and manuals. DeepSeek can process both text-based and scanned PDFs, though the latter require OCR for accurate extraction. Password-protected PDFs are not supported.
DOC/DOCX: Microsoft Word files can be uploaded directly, making it simple to query meeting notes, essays, or reports.
TXT/MD: Plain text and Markdown files are processed without difficulty, ideal for simple notes or code documentation.
PPT/PPTX: PowerPoint presentations are recognized in some implementations, allowing summaries of slide content.
Category | Formats | Notes |
Standard documents | PDF, DOC, DOCX | PDF is the most reliable; DOCX widely supported |
Text formats | TXT, MD | Lightweight and fast to parse |
Presentations | PPT, PPTX | Works in chat, though layout fidelity may vary |
Data and spreadsheet formats supported.
DeepSeek can also interpret structured data formats, allowing for direct analysis of tables and numeric content.
CSV: Provides clean row and column parsing, ensuring structured data can be summarized or reformatted.
XLSX: Excel workbooks are supported for tabular analysis.
For best results, tables should be uploaded in CSV or XLSX form rather than as screenshots.
Image formats supported.
As a multimodal model, DeepSeek accepts common image types.
PNGÂ and JPG/JPEGÂ are the most consistently supported.
These formats allow DeepSeek to extract text from images via OCR or to describe visual content such as charts and diagrams.
Very large or high-resolution images may fail to upload, so compression is recommended.
File size and count limitations.
Exact limits vary by client implementation, but community reports suggest practical boundaries:
File sizes of up to 100–512 MB are typically accepted, though smaller files are more reliable.
Users can upload multiple files in a single conversation—some reports indicate up to 20 files are supported.
Regardless of upload limits, the model’s context window restricts how much text can be processed in a single answer, meaning chunking may be required for very long documents.
API access versus chat interface.
In the consumer chat apps, users can directly upload files. The model handles parsing internally.
In the developer API, there is no dedicated file upload endpoint. Instead, developers are expected to extract text from documents themselves, split it into smaller pieces, and feed it into the model as context. This is often implemented as a retrieval-augmented generation (RAG)Â workflow, where a vector database indexes document content and retrieves relevant chunks for each query.
Environment | File handling | Notes |
Chat UI (web/app) | Drag-and-drop uploads | Automatic text extraction and OCR |
API (developers) | No file endpoint; supply extracted text | Best implemented with RAG pipelines |
Practical guidance for working with uploads.
Use standard PDFs: Export documents to text-based PDF whenever possible; avoid password-protected files.
Prefer CSV/XLSX for data: These formats preserve tabular structure, unlike screenshots.
Run OCR on scans: For scanned PDFs or images, OCR ensures DeepSeek can process text accurately.
Compress images: PNG or JPG files should be kept small to avoid upload failures.
Split large documents: Break long files into sections to avoid hitting context window limits.
Why file support matters for DeepSeek users.
The ability to upload and analyze diverse file types makes DeepSeek useful for business, research, and education. Reports, financial spreadsheets, technical papers, and presentation decks can all be turned into interactive, query-ready resources. Developers can build on this functionality by creating pipelines that feed extracted text into the API for structured retrieval.
DeepSeek’s flexibility—accepting PDFs, Office files, spreadsheets, and images—places it among the more capable platforms for document analysis. While its API requires additional preparation, the consumer chat interface makes direct file uploads accessible to any user. By aligning file preparation with the formats DeepSeek handles best, users can transform static documents into dynamic, conversational assets.
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