Claude vs GPT-4 for Document Analysis

Claude 3.5 Sonnet supports 200k tokens — roughly 500 pages of text. More importantly, retrieval quality degrades more slowly at the extremes. Claude maintains coherence across the full context in a way GPT-4 does not reliably match.

GPT-4o supports 128k context. Sufficient for many document analysis tasks, but performance on very long documents degrades faster — information from the middle of the context is more likely to be missed or deprioritized.

Strong at finding specific clauses, summarizing obligations, flagging unusual terms, and comparing contract versions. The large context window means full contracts fit without chunking, which avoids clause-boundary errors.

Capable contract reviewer, especially with GPT-4o. Context limit means very long contracts may require chunking, which creates risk of missing cross-clause dependencies.

Excellent at tasks like "here are five analyst reports — what are the points of consensus and where do they diverge?" Maintains distinct source attribution across a long context without blending sources incorrectly.

Can handle multi-document synthesis but is more prone to blending sources without attribution. Works better with explicit instructions to cite source numbers when drawing conclusions.

Reliable at extracting structured data from unstructured documents — pulling table data, lists, dates, names, and financial figures into JSON or CSV format. Strong at following exact schema requirements.

Equally capable for structured extraction. GPT-4's function calling / structured output mode is mature and well-documented for developers building extraction pipelines.

Handles annual reports, 10-Ks, earnings transcripts, and financial statements well. Good at connecting narrative discussion in MD&A sections with actual financial figures — not just summarizing tables.

Solid for financial document analysis. Code interpreter integration (in ChatGPT) adds value for running calculations on extracted figures, which Claude lacks in chat mode.

Lower hallucination rate on factual extraction tasks — less likely to invent a number or clause that wasn't in the source document. Still requires verification on high-stakes outputs, but the baseline reliability is higher.

Generally accurate on factual extraction but hallucination risk increases on long documents where source material is sparse. Should be verified on any legally or financially significant output.

Handles PDF uploads natively in Claude.ai and via the API (base64 encoded). Good OCR-equivalent accuracy on well-formatted documents.

GPT-4o handles PDF uploads in ChatGPT. API file handling is less flexible than Claude's base64 approach for complex document pipelines.

Claude Sonnet processes large documents quickly. Initial latency on very long contexts is noticeable but within acceptable range for non-interactive workflows.

Comparable speed. For interactive document Q&A where response latency matters, both models are similar in practice.

Prompt caching is a major cost advantage for document analysis at scale: if you're running many queries against the same document, cache the document and pay 90% less on input tokens. Meaningful for high-volume analysis pipelines.

OpenAI has prompt caching too, but implementation details differ. For one-off analysis, costs are comparable. For repeat-query workflows against the same document, compare caching behavior directly for your use case.