Google Gemini file upload size limits, supported types, and advanced document processing
- Graziano Stefanelli
- Sep 13
- 4 min read
Google’s Gemini 2.5 series—especially the Flash and Pro variants—has introduced powerful multimodal capabilities, including advanced document processing across PDFs, spreadsheets, audio, and video. These features are supported in both consumer-facing apps (Gemini web/mobile) and enterprise-level services through the Vertex AI and File API platforms. This article outlines the exact file limits, accepted formats, and best practices for using Gemini for large-scale document interaction as of late 2025.
File upload limits vary by plan and interface across Gemini products.
Gemini offers different upload capabilities depending on whether you’re using the free consumer app, a Pro/Ultra subscription, or deploying models via Vertex AI or the Gemini File API. Here is a detailed comparison of the current file input limits:
The consumer Gemini app enforces strict limits for audio and video. While file size may allow for large uploads, Free users are capped at 5 minutes of total video, and Pro/Ultra subscribers can upload up to 1 hour per session across clips.
Enterprise and developer users via Vertex AI benefit from batch prompts with thousands of files, making Gemini suitable for workflows such as legal reviews, bulk financial analysis, and research datasets.
Supported file formats include text, spreadsheets, media, and code archives.
Gemini accepts a broad range of file types, from documents to images to structured code folders. Supported formats include:
Documents are processed with layout fidelity, which enables accurate reference extraction and footnote interpretation. Spreadsheets can be read for structure and values, though full formula evaluation may not be available in the Gemini interface outside of API environments.
Gemini’s document processing is powered by layout-aware AI and multimodal integration.
When handling complex documents—especially PDFs, scientific articles, or forms—Gemini 2.5 Flash and Pro apply internal layout recognition to extract structure and meaning. The models can distinguish between:
Headings, body text, footnotes, and sidebars
Multi-column layouts and table structures
Lists, references, and form elements
This system is active across both consumer and enterprise interfaces and forms the basis of Gemini’s document-understanding and content parsing APIs within Vertex AI.
Enterprise users can combine Gemini with the Document AI Form Parser to extract tables from scanned reports, invoices, or academic papers. While table recognition works well for simple structures, row/column span is not yet supported—merged cells are flattened and presented linearly.
For media, Gemini’s models allow cross-modal prompting, where uploaded images or video timestamps can be referenced during text queries (e.g., “Summarize slide 2” or “Describe the audio after minute 3”).
File API options enable large-scale uploads and document automation.
Google offers a Gemini File API designed for automation, asynchronous processing, and long document workflows. This API supports:
Direct uploads up to 2 GB per file
Batch file management (20 GB per project quota)
Temporary storage (48 hours) for uploaded content
Secure access tokens to manage file visibility and cleanup
This API can be used by developers to preload training data, legal documents, or customer reports that need to be read and analyzed by Gemini agents. It is also compatible with the Vertex AI content generation endpoints for pipeline automation.
Limitations and behaviors users should be aware of.
Despite its document capabilities, Gemini still presents some practical restrictions:
Files must be uploaded in each prompt—there is no memory of previous uploads outside the current session or token window.
Long or complex spreadsheets may trigger truncation warnings, especially if they include macros or contain over 1 million cells.
File outputs are read-only—Gemini cannot return modified files, only summaries or structured extractions.
Video processing is not available on Vertex AI and only partially supported in the Gemini app interface.
Uploaded content is stored only for the current session (Apps) or 48 hours (File API), with no persistent workspace.
Best practices for document workflows with Gemini.
To optimize the experience of working with large or varied files in Gemini:
Compress or merge small PDFs before upload to minimize token fragmentation. Ideal size: 25–30 MB or 500–1,000 pages.
Use Gemini Pro (not Flash) for research-style workflows where file content will be queried repeatedly.
Split long video/audio files into ≤5-minute chunks (Free) or ≤15-minute parts (Pro) to ensure better frame alignment and transcription accuracy.
Avoid spreadsheets with excessive formulas or macros, which are often ignored or partially interpreted.
Leverage GitHub repo inputs when summarizing large codebases or documentation libraries.
Gemini performs best when documents are well-formatted, under the token cap, and uploaded with specific instructions (e.g., “Extract the summary and conclusion of this report”).
Gemini is evolving into a multimodal document assistant with strong API potential.
Gemini’s document processing features now cover a wide spectrum—from consumer use cases (resume reviews, contract summaries) to enterprise-scale parsing (bulk legal files, invoices, scientific papers). With support for large context windows, structured layout extraction, and asynchronous uploads, it stands as a viable document assistant when paired with Pro-tier access or developer integrations.
Although it still lacks persistent memory and some features like dynamic table exports or full spreadsheet execution, Gemini’s file-processing roadmap continues to expand rapidly across both apps and APIs. Users seeking flexible, high-volume document interaction now have a well-defined set of tools at their disposal.
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