Claude and PDF Documents: Technical Complete Overview

Claude reads entire PDFs, not just snippets. Instant extraction turns scanned pages into searchable text.

A 200-k-token window lets you query up to 500 dense pages at once. Cross-document synthesis merges findings across multiple reports.

1 What Claude Actually Ingests

Claude accepts native PDF files up to roughly 10 MB per file and up to five files per message. The ingestion pipeline performs several passes:

• Format detection – The service distinguishes between text-based PDFs, scanned images, forms, and hybrid files that mix vector text and embedded raster pages.

• Text extraction – True text layers are converted to UTF-8, preserving page order, headings, tables, and footnote markers. Vector drawings are labelled so that captions and axis titles survive.

• OCR fallback – Pages that lack an embedded text layer are routed to optical character recognition. An ensemble recogniser handles Latin, Cyrillic, CJK, and common math glyphs; confidence scores below a configurable threshold trigger a warning in the chat.

• Structural markup – Headers, list bullets, table borders, and figure captions are tagged with lightweight XML so that the language model can reference them by type (“table_row”, “caption”, “heading_level_2”). This markup never surfaces to the end user but guides token allocation.

• Token normalisation – After extraction, everything is chunked into 1 024-token segments, interleaved with page-number separators. This approach prevents the model from dropping the original order when answering positional questions such as “What does the note on page 47 say about lease liabilities?”

The pipeline runs inside Anthropic’s secured VPC and rejects encrypted or password-protected PDFs outright; users must unlock those files before upload.

2 Context Capacity

Claude Sonnet 4 and Opus 4 can accept prompts up to 200 000 tokens. In concrete terms that equates to:

• ~500 pages of dense academic prose

• ~750 pages of typical annual-report layout with wide margins

• ~1 400 pages of double-spaced legal briefs

For Enterprise customers, an experimental 500 000-token mode is available on Sonnet. It increases latency by 30-50 % and nearly doubles cost per thousand tokens, so the flag must be explicitly toggled per conversation. When the window is exceeded, Claude automatically truncates earliest context first unless the user pins specific segments. Pinned chunks remain seated in memory but count toward the quota, so judicious pinning is required to keep the effective window healthy on long research sessions.

3 Upload Workflow inside Claude.ai

The web and mobile apps expose a unified Attach control.

1. Select one or more PDFs. A blue thumbnail with page count appears in the chat composer.

2. Preview (optional) – Hovering the thumbnail opens a 5-page quick look to ensure the right file was chosen.

3. Send the message. A progress ring indicates server-side extraction. Files under 5 MB finish in two–three seconds; larger files take proportionally longer.

4. Query the content. Users typically begin with a scoping prompt (“Skim the risk factors section and tell me what changed versus last year”).

5. Iterate. Follow-up questions automatically reuse the file context until the conversation is cleared or the file is removed via the side panel.

Multiple PDFs can be referenced in the same question. Claude tracks origin internally, so replies include inline identifiers such as “(Report A, page 112)”.

4 API Workflow

Developers pass a file_refs array in the body of the /messages endpoint. Each element points to a pre-uploaded object store ID. Key mechanics:

• Chunking – Files are sliced into 4 000-token segments with overlap to protect sentence boundaries.

• Parallel embedding – Segments are embedded asynchronously, stored, and then composed into a single retrieval index for that call.

• Window planning – At generation time, a retrieval planner selects the top-K segments that match the user query, favouring high similarity and recency within the thread.

• Billing – Usage equals tokens_in_prompt + tokens_in_response. Retrieval embeddings are free; only the selected segments that flow into the prompt are billed.

This design means a 300-page PDF incurs a cost only when its relevant slices are pulled into the context window, not at upload time.

5 Advanced Queries Claude Handles

Claude’s semantic search and reasoning enable workflows that go beyond simple summarisation:

• Cross-document synthesis – Load three quarterly reports and ask, “List shared supply-chain risks and explain how each company mitigates them.” The model clusters similar disclosures across documents, merges phrasing, and produces a unified matrix.

• Chart deconstruction – Point to an embedded bar chart and request underlying numbers. The OCR stage reads axis labels; the visual parser estimates bar heights with pixel heuristics, then normalises values relative to the scale legend.

• Reg-tech compliance – Provide a 200-page regulation and a 60-page internal policy. Ask Claude to mark every direct conflict, citing paragraphs and recommending red-line edits.

• M&A redlining – Upload two versions of a share-purchase agreement. Claude outputs a clause-by-clause diff, highlighting new indemnity carve-outs and warranty changes.

• Data-table extraction – “Extract all tables containing EBITDA and convert them to CSV.” Claude reconstructs row headers, handles merged cells, and streams a plaintext CSV

  block into the chat.

6 Best-Practice Prompt Engineering

• Scope before depth – Open with a boundary (“Focus only on pages 30–60”) to avoid burning tokens on irrelevant chapters.

• Anchor with direct quotes – Paste a critical paragraph, then ask Claude to interpret or contextualise it. Anchoring increases answer stability.

• Iterative drilling – Large audits or legal opinions benefit from a funnel: start with a high-level summary, then pose increasingly granular follow-ups referring to the same file slice.

• Role directives – Preface with a professional persona (“You are a forensic accountant”) to bias style and jargon.

• Structured output requests – Ask explicitly for JSON, Markdown, or tab-separated lists. The model will format accordingly and is less likely to waffle.

• Memory hygiene – If the conversation surpasses 20 000 tokens, archive or delete sidebar detritus. This keeps retrieval focused on the PDF, not loose chat fragments.

7 Performance and Cost Watch-Outs

• Token creep – Long follow-ups that repeatedly quote large excerpts inflate cost. Instead of pasting 5 000-word blocks, reference page numbers or prior summary IDs.

• Latency cliffs – Beyond 150 000 tokens, generation speed can drop from ~30 tokens/s to ~10 tokens/s. Splitting work into two threads often yields faster overall turnaround.

• Quota evaporation – A single 200 k-token prompt plus a 2 k-token reply consumes about 202 k tokens, exhausting many daily plans in one go. Use hierarchical questioning to stay economical.

• Parallel jobs – Each conversation has its own token window. Spinning up multiple chats with the same PDF avoids window collisions but multiplies consumption.

8 Security and Compliance

Claude’s PDF handling pipeline is bound by end-to-end encryption: TLS 1.3 in transit and AES-256 at rest. Uploaded files are isolated per-tenant:

• Retention – Raw binaries are deleted after 30 days; extracted text persists with the chat until the user deletes the thread.

• Audit controls – Enterprise admins can export full access logs, including who uploaded a document and when it was viewed.

• Data residency – EU customers can pin storage to Frankfurt; US public-sector tenants store data exclusively in FedRAMP-authorised regions.

• Certification – Claude’s environment is SOC 2 Type II and ISO 27001 compliant, with annual penetration tests and continuous vulnerability scanning.

• Least-privilege model – PDF processing workers run in container sandboxes with no network egress, preventing accidental data exfiltration.

9 Current Limitations

• Low-resolution scans – Handwritten or fax-grade images below 150 dpi yield patchy OCR; key figures may be mis-recognised.

• Mathematical notation – Complex LaTeX formulas are flattened to plain text. Integral signs, summation symbols, and Greek letters often degrade to placeholders.

• Embedded media – Audio, video, and 3-D objects inside PDFs are stripped. Only their placeholder metadata survives.

• Dynamic forms – XFA and AcroForm scripts are ignored; filled values convert but interactive logic is lost.

• Digital signatures – Certificate blocks are removed during extraction, so signature validity cannot be verified inside Claude.

• Bi-directional text – Mixed left-to-right and right-to-left scripts occasionally reorder sentences incorrectly.

10 Positioning against Competitors

Feature	Claude	ChatGPT (Code Interpreter)	Gemini AI Studio	Proprietary E-discovery Tools
Max context window	200 k tokens (500 k experimental)	~32 k tokens	~32 k tokens	Varies, typically 10 k–20 k
Native PDF parser	Yes, built-in	No, requires Python parsing	Reads via Drive viewer, limited tagging	Often yes, but closed ecosystem
Cross-file retrieval	Seamless	Possible with manual stitching	Limited	Strong, but expensive
Cost per million tokens	Moderate	Low to moderate	Low	High, licence-based
Compliance features	SOC 2, ISO, regional storage	SOC 2, ISO	SOC 2, ISO	HIPAA, GDPR modules

Claude leads on context size and integrated retrieval. ChatGPT offers a richer programming surface but requires manual PDF wrangling via Python libraries. Gemini excels at Drive-native workflows yet lags on token capacity. Traditional e-discovery suites have deeper legal tooling but charge per-custodian fees and lack generative reasoning.

11 Roadmap Signals

Anthropic engineers are actively piloting the following enhancements:

• Auto-citation mode – Each factual statement in Claude’s answer will carry an inline page reference, easing audit and traceability.

• Inline PDF viewer – A split-pane UI will let users scroll the source page while reading Claude’s commentary, eliminating context-switch friction.

• Streaming diff – Users will be able to drop a “before” and “after” version of a contract and receive a live red-line overlay, not just a textual diff.

• Voice highlights – While listening to a long PDF with Voice Mode on mobile, users can say “mark that” to drop a persistent bookmark.

• Extended connector catalog – Planned integrations with SharePoint, Box, Notion, and Atlassian Confluence will allow multi-repo retrieval without file re-uploads.

Internal timelines suggest that auto-citation and the split-pane viewer will hit public beta within this calendar year, while streaming diff and cross-repo connectors are slated for early next year.

These expanded sections provide a comprehensive view of how Claude processes, analyses, and safeguards PDF content, plus practical guidance for users and developers who want to leverage its large-context reasoning on document-heavy workflows.

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