DeepSeek file upload and reading: supported formats, context limits, and document analysis workflows
- Graziano Stefanelli
- 2 hours ago
- 3 min read
DeepSeek has gradually expanded its ability to read and analyze uploaded documents, positioning file upload as a practical but intentionally lightweight feature.
Rather than acting as a long-term document workspace, DeepSeek treats files as session-bound context that can be queried, summarized, and reasoned over in real time.
Here we share how file upload and reading work in DeepSeek today, which formats are supported, how context limits affect document length, and which workflows are realistic for document-based tasks.
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DeepSeek supports direct file upload with a focus on text-based documents.
DeepSeek allows users to upload files directly into the chat interface.
Uploaded documents are parsed, converted into text, and injected into the active conversation context.
The system is optimized for fast ingestion rather than persistent storage.
Once the session ends, uploaded files are no longer available.
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Supported file formats favor simplicity and textual structure.
DeepSeek reliably handles text-centric document formats.
PDF files are supported when they contain embedded, selectable text.
Plain text and basic Word documents are also accepted.
Complex or media-heavy formats are handled inconsistently.
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DeepSeek supported file formats
Format | Support level | Notes |
PDF (text-based) | High | Best results with selectable text |
TXT | High | Clean and fast parsing |
DOCX | Medium | Basic structure preserved |
Scanned PDF | Low | Requires external OCR |
XLSX / CSV | Limited | Not optimized for spreadsheets |
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Uploaded files are parsed into the active context window.
When a document is uploaded, DeepSeek extracts its textual content and places it inside the model’s context window.
The entire document or a truncated version counts toward context usage.
There is no separate document memory layer.
This means document length directly affects how much additional prompting or conversation can occur.
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Context window size determines practical document limits.
DeepSeek models support large but finite context windows.
Exact token limits are not always exposed in the interface.
Medium-length documents are processed reliably.
Very long documents may be silently truncated or summarized when limits are reached.
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Practical document length behavior
Document size | Observed behavior |
Short files | Fully ingested |
Medium PDFs | Reliable reading |
Long PDFs | Partial truncation |
Multi-document | Only if total fits context |
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DeepSeek enables core document reasoning and extraction tasks.
Once a file is uploaded, DeepSeek can answer questions grounded in the document.
It can summarize sections, extract key points, and explain concepts found in the text.
Reasoning is semantic rather than keyword-based.
Precision improves when prompts explicitly reference sections or topics.
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Table handling works only for simple, text-encoded structures.
Tables inside PDFs are processed only if they are encoded as selectable text.
Simple column layouts may be flattened but remain readable.
Complex, multi-page, or heavily formatted tables lose structure.
DeepSeek does not reliably preserve spreadsheet-like relationships inside documents.
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OCR and image-based documents require preprocessing.
DeepSeek does not perform robust optical character recognition internally.
Scanned PDFs without embedded text often fail to parse correctly.
Image-based content may be skipped entirely.
For reliable results, documents should be converted to text using external OCR tools before upload.
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File size limits are enforced dynamically rather than explicitly.
DeepSeek does not publish formal file size limits.
Observed behavior suggests that small to medium files upload consistently.
Very large files may fail silently or parse only partially.
Limits can vary depending on server load and model selection.
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Observed upload constraints
Aspect | Behavior |
File size | Soft-limited |
Warnings | Minimal or absent |
Truncation | Possible without notice |
Persistence | Session-only |
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File access is strictly session-based with no persistent library.
Uploaded documents exist only within the active conversation.
There is no project-based document storage.
Files cannot be reused across chats.
This reinforces DeepSeek’s focus on immediate analysis rather than long-term document management.
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API usage requires manual document preprocessing.
DeepSeek’s API does not currently provide a native document upload endpoint equivalent to document-first assistants.
Developers typically extract text, chunk it, and send it as prompt input.
This approach works well for controlled pipelines but requires more engineering effort.
Automated large-scale document ingestion is not DeepSeek’s primary use case.
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DeepSeek file reading prioritizes speed over exhaustive document handling.
DeepSeek’s document features are designed for fast inspection rather than exhaustive review.
It excels at quick summaries and targeted questions.
It is less suited for legal-grade analysis or multi-document synthesis.
Understanding this positioning helps align expectations and workflows.
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