Gemini Max File Size: Limits for Web, Advanced, and API Uploads

The Google Gemini max file size is 100 MB per document and 2 GB per video in the web interface, while the Gemini Files API allows up to 2 GB per file across all supported formats, as documented in Google's official Gemini Apps support specifications on October 17, 2026. This dual boundary creates distinct operating rules depending on whether you interact with the consumer web application, run Gemini Advanced, or build programmatic applications through Google AI Studio and Vertex AI.

Gemini max file size is the documented individual file size ceiling, typically 100 MB per document in the Gemini web interface and up to 2 GB via the Gemini Files API or 100 MB for inline base64 payloads, that can be uploaded in a single turn. A file over the threshold is rejected at upload, before the model processes any text.

Google structures upload boundaries across five distinct environments:

• Gemini Web Free: Accepts up to 10 files in a single prompt. Documents, spreadsheets, plain text, and code archives are capped at 100 MB per file. Video files can reach up to 2 GB with a maximum duration of 5 minutes. Audio recordings are capped at 10 minutes.
• Gemini Advanced (Google AI Pro and Google AI Ultra): Maintains the same 10-file attachment limit and 100 MB document size ceiling as the standard free web interface. The paid tier expands media duration, allowing up to 1 hour of video analysis and up to 3 hours of audio processing.
• Google Workspace Accounts: Follows standard web file size limits, but document access depends on administrator configuration. Accessing files from Google Drive requires the Workspace domain administrator to enable Gemini Apps integrations.
• Gemini Files API: Engineered for developers passing large multimodal datasets to Gemini models. Supports individual files up to 2 GB across text, audio, image, and video formats, with a cumulative project storage allocation of 20 GB. Uploaded files remain stored for 48 hours before automatic deletion.
• Inline API Payloads: For developers who pass raw data directly inside JSON request bodies without intermediate staging, the Gemini API enforces a 100 MB limit per request (50 MB for PDFs).

The following comparison table summarizes documented upload limits across Gemini interfaces:

Understanding these differences saves engineering time. Upload failures often stem from selecting an interface mismatched with the file's binary size.

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Many guides conflate Gemini's context window with raw disk file size limits, failing to explain why a 150 MB PDF gets rejected even when its text easily fits token limits. The confusion stems from treating transport layer upload boundaries and inference attention capacity as the same mechanism.

Google does not describe how the limit is enforced, but the check happens at upload, before the model pipeline. If a PDF measures 150 MB on disk because of high-resolution scanned page images, uncompressed raster graphics, or embedded print typography, the upload is rejected against the 100 MB limit. The model never evaluates the document text, because the file never finishes uploading.

Disk file size measures the raw binary footprint of data stored on a physical volume. In contrast, context window capacity measures tokens, the semantic and visual fragments used during neural network attention computations. A dense 40 MB database dump containing raw text can contain several million words, vastly exceeding the model context window. Conversely, a 150 MB PDF containing an architectural plan might contain only 1,200 words of text, fitting easily into less than 2,000 text tokens, yet it fails the web upload check.

Uploading compound file structures introduces additional constraints that catch teams off guard. Gemini provides distinct ingestion pathways for standard directory structures, compressed ZIP archives, and specialized code repositories.

In the Gemini web app, dragging a folder directly into the chat interface initiates an automatic unpacking routine. The application parses the folder recursively, with each individual file counting toward the 10-file prompt maximum. If an uploaded folder holds 14 small text documents totaling 2 MB, it exceeds the file count ceiling, regardless of the negligible byte size.

ZIP archive files follow different rules:

• Maximum Archive Size: A single ZIP file cannot exceed 100 MB.
• Internal File Count: The archive cannot contain more than 10 internal files.
• Media Restrictions: ZIP files uploaded to the web interface cannot contain audio or video files. An archive with nine text files and one short audio recording breaks this rule.

To support software engineering projects, Google provides a dedicated code import pipeline. Users can upload a project directory or connect a GitHub repository directly:

• Code Folder Capacity: Accepts up to 5,000 individual files.
• Total Archive Size: The combined payload must remain within 100 MB.
• File Type Focus: The import is built for source code, configuration files, and documentation markdown rather than compiled binaries or business documents.

The following table compares compound file upload constraints:

Teams managing multi-component projects must select the correct pipeline. Uploading a software repository through standard folder upload runs into the 10-file limit if the repository contains more than 10 files.

Google provides the Gemini Files API to support multimodal developer pipelines that exceed chat payload sizes, but the infrastructure is designed as an ephemeral ingestion cache rather than a durable file repository. Google's developer documentation specifies that the Files API allows storing up to 20 GB of files per project, with a per-file maximum size of 2 GB, and files are stored for 48 hours.

Google offers the Files API at no cost in regions where the Gemini API is available, and files are deleted automatically after two days, which makes the Files API a staging area rather than a storage service. Developers can inspect metadata through the files.get method or delete files manually using files.delete. However, the API does not allow downloading user-uploaded files back to local environments. The files.download method is restricted to model-generated content such as rendered video assets.

Query Large Document Collections Beyond Gemini Upload Limits

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Organizations managing extensive corporate documentation, compliance libraries, or legal evidence sets cannot operate within 10-file prompt limits or 48-hour temporary API retention. Several technical patterns exist for evaluating large collections against LLMs, each with distinct tradeoffs:

• Local Scripted File Splitting: Engineers use Python libraries such as pypdf or pdfcpu to divide large manuals into 50-page increments. While this keeps files under the 100 MB upload ceiling, it fragments semantic context, breaks cross-table references, and requires manual maintenance.
• Google Cloud Storage Bucket Registration: The Gemini API allows registering files stored directly in Google Cloud Storage buckets (up to 2 GB per file) or passing public or signed HTTPS URLs (up to 15 MB per file). While this avoids moving file bytes to the temporary Files API, it requires setting up Google Cloud IAM permissions and leaves the underlying storage unindexed for semantic search.
• Custom Vector Database Pipelines: Teams build Retrieval Augmented Generation pipelines using vector databases like Pinecone or Qdrant. While functional, this approach requires building custom parsing engines, managing embedding models, maintaining chunking boundaries, and synchronizing vector indexes as files change.
• Intelligent Cloud Workspaces via Model Context Protocol: A direct approach connects Gemini to an intelligent cloud workspace. Instead of attaching massive files to prompts or re-uploading documents every 48 hours, teams keep their complete document library in a persistent workspace on Fastio workspace storage for agents.

Fastio provides an intelligent workspace platform designed for human and agent collaboration. When documents are placed in a workspace, Intelligence Mode indexes the content automatically using hybrid search, which unifies full-text keyword matching, semantic vector indexing, and structured search by metadata value. Rather than attaching a 400 MB technical manual to a prompt, an assistant queries the workspace and retrieves only the precise paragraphs needed to answer the question, complete with source citations.

Workspaces provide shared org-owned repositories, per-file version history, and a detailed activity log that records modifications across team members and agents. Documents can be imported from Google Drive, Dropbox, Box, and OneDrive. Fastio supports Cloud Sync for Google Drive, Dropbox, Box, and OneDrive on a schedule or on demand.

Connecting Gemini CLI or custom developer agents to Fastio takes minutes using the remote Model Context Protocol (MCP) server over Streamable HTTP. In the Gemini CLI configuration file (settings.json), declare the server under mcpServers:

{
  "mcpServers": {
    "fastio": {
      "httpUrl": "https://mcp.fast.io/mcp/code"
    }
  }
}

In Gemini CLI and its forks, using "httpUrl" selects Streamable HTTP, whereas "url" selects legacy SSE transport. Authenticate by running /mcp auth fastio in the client interface to initiate OAuth sign-in in your browser. Once connected, the agent calls the search tool on /mcp/code to query indexed documents. When an agent searches a multi-gigabyte corporate manual, Fastio returns the exact passages required for inference, using a small share of the context window instead of exhausting it. Review setup details in the Fastio MCP setup documentation.

For structured extraction across contracts, invoices, or technical specifications, teams deploy Metadata Views. Metadata Views extracts typed fields (Text, Integer, Decimal, Boolean, URL, JSON, Date & Time) from unstructured documents into a queryable table without template setup.

Monthly plans start with a 14-day free trial, which requires a credit card. Plans are Starter at $9.99/mo (3 seats, 250 GB storage, 5 workspaces, 100,000 credits a month), Business at $49.99/mo (10 seats, 5 TB storage, 50 workspaces, 600,000 credits a month), and Enterprise at $199.99/mo (30 seats included, more at $1 a seat up to 200 seats, 25 TB storage, 200 workspaces, 3,000,000 credits a month). Maximum upload sizes reach 25 GB on Starter, 50 GB on Business, and 100 GB on Enterprise. Credits meter AI operations, with overage billed at $10 per 100,000 credits. Extra storage is 1.5 cents per GB a month and extra bandwidth is 4 cents per GB. Explore complete pricing options on the Fastio pricing page.

Evaluating file upload constraints across frontier AI platforms highlights how different vendors manage data ingestion:

Anthropic Claude enforces distinct file handling between standard chat and project workspaces. In Claude chat, users can attach up to 20 files at up to 500 MB each. In Claude Projects, individual document uploads are capped at 30 MB, but the total file count is unlimited. Claude Projects has no fixed file-count cap; the practical ceiling is the model's context window, which ranges from 200,000 to 1 million tokens depending on the model. Once the cumulative tokens of project files approach the context boundary, users must prune documents or move to external retrieval.

OpenAI's help center documents an individual file cap of 512 MB for documents and 20 MB for images in ChatGPT, with a rolling limit of 80 files every 3 hours (free users can upload 3 files a day).

Google Gemini balances smaller web chat document caps with expansive media limits and a generous developer staging API. The Gemini web interface limits documents to 100 MB and 10 files per prompt, but supports 2 GB video files. Programmatically, the Gemini Files API handles 2 GB files with a 48-hour retention period, and inline API payloads support up to 100 MB (50 MB for PDFs).

The following comparison table contrasts file constraints across the three major platforms:

All three web chat environments share a common architectural boundary: they are optimized for focused, interactive dialogues rather than long-term knowledge management. When teams need to analyze collections spanning hundreds of documents, storing the corpus in an indexed workspace allows Gemini, Claude, and OpenAI assistants to query the same data without hitting individual vendor upload barriers.