DeepSeek and PDF analysis: what works, what doesn’t, and what’s coming next
- Graziano Stefanelli
- 10 hours ago
- 4 min read
DeepSeek’s document handling has matured, but key limitations remain in place.
As of September 2025, DeepSeek offers powerful tools for processing and analyzing textual documents, with support for long-context reasoning, citation tracing, and formula recognition. However, its handling of PDF files—especially large, image-based, or scanned ones—presents several known constraints. While models like DeepSeek-R1 (“Reasoner”) and DeepSeek-V3.1 offer competitive context sizes and inference quality, uploading and parsing capabilities are uneven across platforms. This article provides a complete technical summary of DeepSeek’s current PDF features and limitations, with a focus on web, mobile, and (future) API environments.
DeepSeek’s two main models support 128K tokens and sentence-level citation.
DeepSeek exposes two main models for PDF and document interaction. DeepSeek-V3.1 is the default web inference model, while DeepSeek-R1, a reasoning-enhanced variant, is used for structured extraction, chain-of-thought, and API-ready deployments. Both offer a 128,000-token context window as of the latest documentation, up from the earlier 64K limit.
Table 1 – DeepSeek models and document capability comparison
Model | Context Window | Citation Support | Reasoning Chain | Use Case |
DeepSeek-V3.1 | 128,000 tokens | Page / Sentence | Basic | Fast retrieval, summaries |
DeepSeek-R1 | 128,000 tokens | Page / Sentence | Multi-step, logic-driven | PDF deep analysis, multi-doc Q&A |
ChatDOC (frontend) | Same (R1 backend) | Enhanced | Formula capture, OCR | Best choice for scanned docs |
The reasoning chain in R1 is better suited for legal, financial, and scientific workflows, where the model must follow user instructions across many pages or link patterns between tables and text.
The web interface allows PDF uploads but applies silent truncation.
DeepSeek’s native interface supports uploading PDFs via drag-and-drop or multi-file folder input. Each file is limited to roughly 10 megabytes, with known truncation issues beyond ~70 pages, even when files remain within the size threshold. The UI allows up to 20 files per session, but the actual processing limit is dictated by token budget. No warning is given when documents are partially parsed.
Table 2 – Web uploader constraints for PDFs (Sept 2025)
Feature | Limit / Behavior |
Max file size (PDF) | ≈ 10 MB (practical ceiling) |
Page count limitation | Silent truncation after ~70 pages |
Total files per chat | Up to 20 |
OCR support | None (text layer required) |
Format support | PDF, DOCX, TXT, ePub, Markdown |
Citation modes | Page or sentence granularity |
Formula/figure preview | Enabled via ChatDOC (not native) |
For documents longer than 70 pages, DeepSeek typically parses the first portion and silently ignores the rest. The recommendation remains to split large documents into parts before upload and confirm completeness through prompting.
API upload support remains unavailable despite roadmap inclusion.
As of this update, DeepSeek does not expose a public API endpoint for file uploading. While internal documentation and early roadmap entries included a planned /v1/files endpoint for uploading PDFs and ZIP bundles, these endpoints currently return 404 errors and are not usable in production.
This means that developers wishing to work with DeepSeek via API must still pre-tokenize and feed extracted document content directly into prompt payloads. The 128K token limit still applies, with structured JSON outputs supported through the return_sources=true flag.
OCR, scanned PDFs, and image-based documents require ChatDOC.
One of the most important limitations of DeepSeek remains its inability to read image-based or scanned PDFs directly. If a PDF lacks an embedded text layer (as is common in scanned forms, old contracts, or faxes), DeepSeek will treat it as blank and provide no output.
For users who need OCR capabilities, table reconstruction, or formula parsing from scientific papers, the only viable solution within the DeepSeek ecosystem is to route documents through ChatDOC, a dedicated frontend built on DeepSeek-R1. ChatDOC enables:
OCR-based parsing of scanned documents
Figure previews and traceable citations
Sentence-level anchoring of extracted insights
Mathematical expression recognition from embedded images
DeepSeek does not currently offer these features through its standard interface.
Performance constraints include server-side load limits and lack of feedback.
DeepSeek’s web infrastructure applies rate throttling during periods of high usage, especially for free-tier users. “Server busy” messages and dropped follow-up queries are frequent under load. The system does not currently offer a real-time file status report, nor does it display how many tokens are consumed by uploaded documents.
Table 3 – Common performance and parsing issues
Issue | Cause / Trigger | Mitigation |
Server busy / query dropped | Load shedded for free-tier accounts | Retry or upgrade to paid plan |
Pages missing from output | Truncation past 70 pages | Split large PDFs manually |
File appears parsed but returns “null” | No text layer (image-based) | Pre-OCR or upload via ChatDOC |
Delayed citation or missing highlights | Token cap exceeded | Reduce file count or document size |
Users are advised to keep individual PDFs under 70 pages or pre-process them to extract and tokenize only relevant segments. There is currently no built-in chunking or summarization layer that processes entire books or PDFs beyond token limits.
DeepSeek compared to Claude and ChatGPT for PDF workflows.
While DeepSeek offers clear benefits in terms of cost-efficiency and logic performance, its PDF capabilities remain more limited than other top-tier models, particularly Claude Opus 4 and ChatGPT‑5. The lack of native OCR, small file size caps, and missing file API prevent DeepSeek from being a complete document solution.
Table 4 – PDF analysis comparison (September 2025)
Platform | OCR Support | Max File Size (Web) | Context Window | Citation Handling | Ideal For |
DeepSeek (R1) | No | ≈ 10 MB | 128K tokens | Page/Sentence | Legal, logic, short docs |
ChatGPT-5 | Yes | 512 MB (Pro) | 128K / 196K tokens | Structured with ADA | Full-scale data extraction |
Claude Opus 4 | Yes (beta) | 30 MB | 200K tokens | Inline + footnotes | Long scientific PDFs |
DeepSeek’s strength lies in multi-step reasoning, low-latency inference, and predictable output for structured queries, but it lacks the scale and versatility of Claude or ChatGPT when working with complex or image-based files.
The current DeepSeek workflow for PDF users remains semi-manual and requires document curation.
Until a native file upload API and larger document window are released, developers and knowledge workers using DeepSeek must treat it as a logic engine—not a full ingestion pipeline. Its 128K-token context provides room for hundreds of paragraphs of pre-cleaned text, but all content must be verified, segmented, and prepared ahead of time.
For users processing scanned contracts, image-based reports, or complete white papers, the recommended path is:
Run OCR externally, or use ChatDOC if staying inside the DeepSeek ecosystem.
Split PDFs manually into 50–70 page segments.
Upload no more than 10 MB per file, and stage no more than 20 files per session.
Enable return_sources=true for citation-enhanced output.
Avoid concurrent uploads during peak hours to reduce throttling.
____________
FOLLOW US FOR MORE.
DATA STUDIOS