The Gemini API supports PDF input, including long documents (up to 1000 pages). Gemini models process PDFs with native vision, and are therefore able to understand both text and image contents inside documents. With native PDF vision support, Gemini models are able to:
- Analyze diagrams, charts, and tables inside documents
- Extract information into structured output formats
- Answer questions about visual and text contents in documents
- Summarize documents
- Transcribe document content (e.g. to HTML) preserving layouts and formatting, for use in downstream applications
This tutorial demonstrates some possible ways to use the Gemini API to process PDF documents.
Technical details
Gemini supports a maximum of 1,000 document pages. Document pages must be in one of the following text data MIME types:
- PDF -
application/pdf - JavaScript -
application/x-javascript,text/javascript - Python -
application/x-python,text/x-python - TXT -
text/plain - HTML -
text/html - CSS -
text/css - Markdown -
text/md - CSV -
text/csv - XML -
text/xml - RTF -
text/rtf
Each document page is equivalent to 258 tokens.
While there are no specific limits to the number of pixels in a document besides the model's context window, larger pages are scaled down to a maximum resolution of 3072x3072 while preserving their original aspect ratio, while smaller pages are scaled up to 768x768 pixels. There is no cost reduction for pages at lower sizes, other than bandwidth, or performance improvement for pages at higher resolution.
For best results:
- Rotate pages to the correct orientation before uploading.
- Avoid blurry pages.
- If using a single page, place the text prompt after the page.
What's next
To learn more, see the following resources:
- File prompting strategies: The Gemini API supports prompting with text, image, audio, and video data, also known as multimodal prompting.
- System instructions: System instructions let you steer the behavior of the model based on your specific needs and use cases.