Optimizing PDF Analysis: Text vs Vision Costs
2026-04-02Knowledge Base
When feeding a 100-page PDF report into an AI model, developers face an architectural choice: do we send the PDF as a visual document (images), or do we extract the text first?
Approach 1: Vision Processing (The Expensive Way)
When you upload a PDF directly to a multimodal model like Claude 3.5 Sonnet or Gemini 1.5 Pro, the API converts every page into an image.
- Cost: If one page is treated as a high-resolution image (~1,600 tokens), a 100-page document costs 160,000 tokens.
- Pros: The AI understands charts, layout, bold text, and handwritten notes.
Approach 2: Text Extraction (The Cheap Way)
If you run a local script (like PyPDF2) to extract the text and send only the raw text to the API.
- Cost: A typical 100-page document contains about 30,000 words, roughly 40,000 tokens.
- Pros: Costs 75% less.
- Cons: All visual context (graphs, tables) is completely lost, often leading to hallucinations.
The Hybrid Solution
Extract text where possible, but use vision APIs only for pages containing complex charts. To see how much 100 images would cost compared to 40,000 text tokens, check out our Cost Calculator.