Gemini File Upload Support Explained: Supported Formats, Size Constraints, and Document Handling Across Apps, API, and Enterprise Workflows

Gemini, Google’s generative AI platform, has steadily broadened its support for file uploads, enabling users to supply documents, media, code, and datasets as part of conversational, analytical, and developer-driven interactions.

The practical realities of what Gemini will accept—and how those files are interpreted, constrained, and integrated into the reasoning workflow—are determined by a combination of product surface, subscription plan, and technical implementation, making an in-depth understanding of supported formats, size limits, and document processing pipelines indispensable for maximizing effectiveness in research, productivity, and automation.

Gemini’s approach to file input emphasizes broad compatibility for everyday use while enforcing concrete boundaries for scalability, safety, and performance, with nuanced behaviors emerging when uploads push against the upper edges of capacity or involve non-standard data types.

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Gemini’s file upload support is differentiated by platform, with consumer apps, API endpoints, and enterprise tools each applying their own set of rules and constraints.

At the consumer level, Gemini Apps—including the web experience at gemini.google.com and its associated mobile applications—present a streamlined interface for uploading documents, spreadsheets, NotebookLM notebooks, photos, videos, and entire code folders or GitHub repositories, with each modality subject to its own size ceiling and prompt quota.

Within Gemini Apps, users may upload up to ten files per prompt, blending formats as needed for research, content generation, or multi-file analysis, although video and audio uploads are further restricted by duration and aggregate size.

Google’s published guidance makes clear that while “most file types” are permitted, the system draws sharp lines at certain thresholds: individual video files must not exceed two gigabytes and are capped at five minutes in length for most users, with premium plans unlocking up to an hour of total video processing.

For all other supported file types, the default limit stands at one hundred megabytes per file, with ZIP uploads allowed as long as they remain under one hundred megabytes and do not contain media files such as audio or video.

Code inputs are handled as single code folders or referenced GitHub repositories, both subject to a limit of five thousand files and a combined size of one hundred megabytes per upload batch.

When the Gemini Apps experience is pushed to its technical or plan-based limits—whether by file size, file count, or media duration—the system may decline the upload, truncate content, or issue warnings that analysis quality will be degraded due to practical reading constraints tied to the plan’s active context window.

........Gemini File Upload: Supported Formats and Size Limits by Platform

Platform / Surface	Supported File Types and Modalities	Per-File Size Limit	Special Handling and Notes
Gemini Apps (consumer)	Documents, spreadsheets, NotebookLM, photos, videos, code	Video: 2 GB; others: 100 MB	Up to 10 files per prompt; code/GitHub: 5,000 files/100 MB; ZIP: 100 MB, no AV
Gemini API (inline)	Media (docs, images, audio, video) as inline data	Inline: 100 MB; PDFs: 50 MB	For >100 MB total, use Files API; PDFs above 50 MB may not be accepted
Gemini API (Files API)	Audio, images, video, documents, GCS/URL objects	Use when request >100 MB; PDFs: 50 MB	Upload once, reuse via URI; designed for large or frequently referenced assets
Vertex AI doc understanding	PDF, text/plain	50 MB (API/GCS); 7 MB (console)	OCR applied only as needed; high-scale batch processing supported
Firebase AI Logic	Explicit MIME types for each modality (PDF, audio, video)	PDFs: 50 MB; others vary	PDFs treated as images if scanned; machine-readable text preferred

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The specific formats Gemini can process are defined by a combination of user-facing experience and MIME-type-based validation, with text, tabular, media, and code all supported in mainstream scenarios.

Gemini Apps and API endpoints have been engineered to accept a wide spectrum of file types, centering on everyday productivity documents and datasets, but also extending into code repositories, multimedia, and notebook formats to facilitate diverse research and creative workflows.

For document uploads, the system recognizes and efficiently parses PDFs, plain text files, CSV spreadsheets, Microsoft Excel files, and NotebookLM notebook exports, reflecting the priorities of business users, researchers, and students working with structured data and long-form content.

When working with ZIP files, Gemini enforces content-aware validation: each archive may include up to ten files and must exclude any audio or video, ensuring both security and processing predictability.

Images and photos may be uploaded directly for vision-based analysis, subject to standard file type recognition (JPEG, PNG, etc.), while audio and video inputs must adhere to widely used encoding standards and are further constrained by both size and aggregate duration as dictated by the user’s plan and the system’s real-time processing capacity.

Codebase uploads are treated as monolithic folders or repository references, with Gemini recursively indexing up to five thousand files within a maximum payload of one hundred megabytes, a design that supports multi-language code review, automated documentation, and in-depth technical search.

The underlying principle is that file types must be either natively machine-readable or conform to published MIME type lists, with system-side extraction, chunking, and semantic indexing optimized for formats with well-established structure and encoding.

Gemini will attempt to process files outside this mainstream set, but practical support, reasoning fidelity, and extraction quality may degrade when encountering proprietary, encrypted, or non-standard formats.

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Size limits and handling strategies are designed to maintain system responsiveness, promote fair usage, and align document processing with model context windows.

A central tension in Gemini’s file upload pipeline is the interplay between file size ceilings, per-request quotas, and the model’s inherent context window, which governs how much of any uploaded document can be actively reasoned over at one time.

While Gemini Apps nominally accept up to one hundred megabytes per document or image file, and up to two gigabytes for video, the system will alert users if their content is likely to exceed the current context window, especially on basic plans where context may be as low as thirty-two thousand tokens.

Gemini’s processing logic is optimized to extract, chunk, and summarize uploaded content, mapping individual pages, spreadsheet rows, or code files into manageable segments for downstream analysis.

If a file contains more text, data, or imagery than can be considered in a single reasoning pass, Gemini will selectively summarize, sample, or focus on the most salient sections, issuing explicit warnings when omissions or missed connections are likely due to these architectural constraints.

On higher-tier plans, including Pro and Ultra subscriptions, the available context window increases—up to a million tokens—enabling analysis of larger documents, extended codebases, and longer audio/video segments without as much aggressive truncation.

This relationship between file size, context window, and processing fidelity is also observed in the developer APIs, where inline uploads are recommended only for assets below one hundred megabytes, with larger, reusable documents handled via the Files API to minimize latency and maximize flexibility.

Enterprise-grade document understanding on Vertex AI is centered around PDFs and plain text, with processing optimized for high-volume, batch scenarios, and an explicit preference for machine-readable files over scanned images or handwritten documents, as the latter may require additional optical character recognition steps that increase cost and reduce extraction accuracy.

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Document handling after upload is governed by extraction logic, content type, and the nature of the input (text-native vs. image-based), impacting both reasoning and cost.

Once a file is uploaded, Gemini applies specialized extraction and chunking logic tailored to the detected file type, with the aim of maximizing the information available for synthesis while respecting token, size, and performance limits.

For text-native documents such as PDFs with embedded text or plain text files, the system parses and indexes the content directly, allowing rapid access to specific passages, sections, or tables, and enabling precise referencing during analysis.

For scanned PDFs or image-based documents, Gemini treats each page as an image, triggering internal image-processing or OCR routines, which may reduce the granularity and reliability of extracted information and substantially increase the number of tokens consumed per page, thus impacting both cost and processing depth.

Spreadsheet and CSV uploads are ingested as tabular data, with Gemini mapping rows and columns to semantic representations that can be queried, summarized, or transformed as part of the reasoning pipeline.

Audio and video inputs are transcribed and segmented, with system-imposed limits on the length and number of segments processed per request, ensuring that the most relevant portions are prioritized for reasoning.

Codebase and repository uploads are recursively indexed, enabling Gemini to search, summarize, and cross-reference across thousands of files, although total size and context window limitations may require prioritization of key files or sections during synthesis.

Throughout this process, Gemini’s document handling logic is tightly coupled to its active reasoning context: when the cumulative size of extracted content approaches or exceeds the context window, the system dynamically prunes, compresses, or defers less critical information to maintain answer quality, and may explicitly notify the user when detail is lost due to these boundaries.

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Best practices for Gemini file uploads focus on ensuring extractability, adhering to published limits, and leveraging plan tiers to optimize document analysis.

To achieve optimal results when uploading files to Gemini—whether through the consumer apps, API, or enterprise platforms—users and developers should prioritize machine-readable formats, avoid scanned image PDFs where possible, and break up very large documents into smaller, thematically organized files.

Where bulk uploads or persistent assets are required, such as in data science projects or long-term code review, the Files API or batch document-understanding endpoints on Vertex AI are the preferred solution, as these are engineered to handle large payloads with robust error handling and efficient resource allocation.

Users should remain attentive to published limits, not only on individual file size but also on total files per prompt, duration of media files, and the size of code repositories, adjusting their workflow to fit within the technical and subscription-imposed boundaries of their current Gemini environment.

Finally, understanding the interplay between file size, model context window, and document extraction logic enables more predictable outcomes, with users empowered to anticipate where information may be truncated, summarized, or omitted and to adapt their prompts, file organization, or plan selection accordingly for the most accurate and comprehensive analysis.

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