Document understanding

Gemini models can process documents in PDF format, using native vision to understand entire document contexts. This goes beyond just text extraction, allowing Gemini to:

  • Analyze and interpret content, including text, images, diagrams, charts, and tables, even in long documents up to 1000 pages.
  • Extract information into structured output formats.
  • Summarize and answer questions based on both the visual and textual elements in a document.
  • Transcribe document content (e.g. to HTML), preserving layouts and formatting, for use in downstream applications.

You can also pass non-PDF documents in the same way but Gemini will see them as normal text which will eliminate context like charts or formatting.

Passing PDF data inline

You can pass PDF data inline in the request. This is best suited for smaller documents or temporary processing where you don't need to reference the file in subsequent requests. We recommend using the Files API for larger documents that you need to refer to in multi-turn interactions to improve request latency and reduce bandwidth usage.

The following example shows you how to pass PDF data inline:

Python

from google import genai
import base64

client = genai.Client()

with open('path/to/document.pdf', 'rb') as f:
    pdf_bytes = f.read()

interaction = client.interactions.create(
    model="gemini-3-flash-preview",
    input=[
        {
            "type": "document",
            "data": base64.b64encode(pdf_bytes).decode('utf-8'),
            "mime_type": "application/pdf"
        },
        {"type": "text", "text": "Summarize this document"}
    ]
)

print(interaction.steps[-1].content[0].text)

JavaScript

import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";

const ai = new GoogleGenAI({});

async function main() {
    const pdfData = fs.readFileSync("path/to/document.pdf", {
        encoding: "base64"
    });

    const interaction = await ai.interactions.create({
        model: "gemini-3-flash-preview",
        input: [
            { type: "text", text: "Summarize this document" },
            {
                type: "document",
                data: pdfData,
                mimeType: "application/pdf"
            }
        ]
    });
    console.log(interaction.steps.at(-1).content[0].text);
}

main();

REST

PDF_PATH="path/to/document.pdf"

if [[ "$(base64 --version 2>&1)" = *"FreeBSD"* ]]; then
  B64FLAGS="--input"
else
  B64FLAGS="-w0"
fi

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gemini-3-flash-preview",
    "input": [
      {
        "type": "document",
        "data": "'$(base64 $B64FLAGS $PDF_PATH)'",
        "mimeType": "application/pdf"
      },
      {"type": "text", "text": "Summarize this document"}
    ]
  }'

You can also upload a local PDF file for processing:

Python

from google import genai

client = genai.Client()

uploaded_file = client.files.upload(file="file.pdf")

interaction = client.interactions.create(
    model="gemini-3-flash-preview",
    input=[
        {"type": "document", "uri": uploaded_file.uri, "mime_type": uploaded_file.mime_type},
        {"type": "text", "text": "Summarize this document"}
    ]
)
print(interaction.steps[-1].content[0].text)

JavaScript

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({});

async function main() {
    const uploadedFile = await ai.files.upload({
        file: "file.pdf",
        config: { mimeType: "application/pdf" }
    });

    const interaction = await ai.interactions.create({
        model: "gemini-3-flash-preview",
        input: [
            { type: "text", text: "Summarize this document" },
            {
                type: "document",
                uri: uploadedFile.uri,
                mimeType: uploadedFile.mimeType
            }
        ]
    });
    console.log(interaction.steps.at(-1).content[0].text);
}

main();

Uploading PDFs using the Files API

We recommend you use Files API for larger files or when you intend to reuse a document across multiple requests. This improves request latency and reduces bandwidth usage by decoupling the file upload from the model requests.

Large PDFs from URLs

Use the File API to simplify uploading and processing large PDF files from URLs:

Python

from google import genai
import io
import httpx

client = genai.Client()

long_context_pdf_path = "https://www.nasa.gov/wp-content/uploads/static/history/alsj/a17/A17_FlightPlan.pdf"

# Retrieve and upload the PDF using the File API
doc_io = io.BytesIO(httpx.get(long_context_pdf_path).content)

sample_doc = client.files.upload(
  # You can pass a path or a file-like object here
  file=doc_io,
  config=dict(
    mime_type='application/pdf')
)

prompt = "Summarize this document"

interaction = client.interactions.create(
    model="gemini-3-flash-preview",
    input=[
        {"type": "document", "uri": sample_doc.uri, "mime_type": sample_doc.mime_type},
        {"type": "text", "text": prompt}
    ]
)
print(interaction.steps[-1].content[0].text)

JavaScript

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({});

async function main() {

    const pdfBuffer = await fetch("https://www.nasa.gov/wp-content/uploads/static/history/alsj/a17/A17_FlightPlan.pdf")
        .then((response) => response.arrayBuffer());

    const fileBlob = new Blob([pdfBuffer], { type: 'application/pdf' });

    const file = await ai.files.upload({
        file: fileBlob,
        config: {
            displayName: 'A17_FlightPlan.pdf',
        },
    });

    // Wait for the file to be processed.
    let getFile = await ai.files.get({ name: file.name });
    while (getFile.state === 'PROCESSING') {
        getFile = await ai.files.get({ name: file.name });
        console.log(`current file status: ${getFile.state}`);
        console.log('File is still processing, retrying in 5 seconds');

        await new Promise((resolve) => {
            setTimeout(resolve, 5000);
        });
    }
    if (file.state === 'FAILED') {
        throw new Error('File processing failed.');
    }

    const interaction = await ai.interactions.create({
        model: 'gemini-3-flash-preview',
        input: [
            { type: "document", uri: file.uri, mimeType: file.mimeType },
            { type: "text", text: "Summarize this document" }
        ],
    });

    console.log(interaction.steps.at(-1).content[0].text);

}

main();

REST

PDF_PATH="https://www.nasa.gov/wp-content/uploads/static/history/alsj/a17/A17_FlightPlan.pdf"
DISPLAY_NAME="A17_FlightPlan"
PROMPT="Summarize this document"

# Download the PDF from the provided URL
wget -O "${DISPLAY_NAME}.pdf" "${PDF_PATH}"

MIME_TYPE=$(file -b --mime-type "${DISPLAY_NAME}.pdf")
NUM_BYTES=$(wc -c < "${DISPLAY_NAME}.pdf")

echo "MIME_TYPE: ${MIME_TYPE}"
echo "NUM_BYTES: ${NUM_BYTES}"

tmp_header_file=upload-header.tmp

# Initial resumable request defining metadata.
# The upload url is in the response headers dump them to a file.
curl "https://generativelanguage.googleapis.com/upload/v1beta/files?key=${GOOGLE_API_KEY}" \
  -D upload-header.tmp \
  -H "X-Goog-Upload-Protocol: resumable" \
  -H "X-Goog-Upload-Command: start" \
  -H "X-Goog-Upload-Header-Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Header-Content-Type: ${MIME_TYPE}" \
  -H "Content-Type: application/json" \
  -d "{'file': {'display_name': '${DISPLAY_NAME}'}}" 2> /dev/null

upload_url=$(grep -i "x-goog-upload-url: " "${tmp_header_file}" | cut -d" " -f2 | tr -d "\r")
rm "${tmp_header_file}"

# Upload the actual bytes.
curl "${upload_url}" \
  -H "Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Offset: 0" \
  -H "X-Goog-Upload-Command: upload, finalize" \
  --data-binary "@${DISPLAY_NAME}.pdf" 2> /dev/null > file_info.json

file_uri=$(jq ".file.uri" file_info.json)
echo "file_uri: ${file_uri}"

# Now create an interaction using that file
curl "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "model": "gemini-3-flash-preview",
      "input": [
        {"type": "text", "text": "'$PROMPT'"},
        {"type": "document", "uri": '$file_uri', "mimeType": "application/pdf"}
      ]
    }' 2> /dev/null > response.json

cat response.json
echo

jq ".steps[-1].content[0].text" response.json

# Clean up the downloaded PDF
rm "${DISPLAY_NAME}.pdf"

Large PDFs stored locally

Python

from google import genai
import pathlib

client = genai.Client()

# Upload the PDF using the File API
file_path = pathlib.Path('large_file.pdf')
sample_file = client.files.upload(
    file=file_path,
)

interaction = client.interactions.create(
    model="gemini-3-flash-preview",
    input=[
        {"type": "document", "uri": sample_file.uri, "mime_type": sample_file.mime_type},
        {"type": "text", "text": "Summarize this document"}
    ]
)
print(interaction.steps[-1].content[0].text)

JavaScript

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({});

async function main() {
    const file = await ai.files.upload({
        file: 'path-to-localfile.pdf',
        config: {
            displayName: 'A17_FlightPlan.pdf',
        },
    });

    // Wait for the file to be processed.
    let getFile = await ai.files.get({ name: file.name });
    while (getFile.state === 'PROCESSING') {
        getFile = await ai.files.get({ name: file.name });
        console.log(`current file status: ${getFile.state}`);
        console.log('File is still processing, retrying in 5 seconds');

        await new Promise((resolve) => {
            setTimeout(resolve, 5000);
        });
    }
    if (file.state === 'FAILED') {
        throw new Error('File processing failed.');
    }

    const interaction = await ai.interactions.create({
        model: 'gemini-3-flash-preview',
        input: [
            { type: "document", uri: file.uri, mimeType: file.mimeType },
            { type: "text", text: "Summarize this document" }
        ],
    });

    console.log(interaction.steps.at(-1).content[0].text);

}

main();

REST

PDF_PATH="path/to/large_file.pdf"
NUM_BYTES=$(wc -c < "${PDF_PATH}")
DISPLAY_NAME=TEXT
tmp_header_file=upload-header.tmp

# Initial resumable request defining metadata.
# The upload url is in the response headers dump them to a file.
curl "https://generativelanguage.googleapis.com/upload/v1beta/files?key=${GEMINI_API_KEY}" \
  -D upload-header.tmp \
  -H "X-Goog-Upload-Protocol: resumable" \
  -H "X-Goog-Upload-Command: start" \
  -H "X-Goog-Upload-Header-Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Header-Content-Type: application/pdf" \
  -H "Content-Type: application/json" \
  -d "{'file': {'display_name': '${DISPLAY_NAME}'}}" 2> /dev/null

upload_url=$(grep -i "x-goog-upload-url: " "${tmp_header_file}" | cut -d" " -f2 | tr -d "\r")
rm "${tmp_header_file}"

# Upload the actual bytes.
curl "${upload_url}" \
  -H "Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Offset: 0" \
  -H "X-Goog-Upload-Command: upload, finalize" \
  --data-binary "@${PDF_PATH}" 2> /dev/null > file_info.json

file_uri=$(jq ".file.uri" file_info.json)
echo file_uri=$file_uri

# Now create an interaction using that file
curl "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "model": "gemini-3-flash-preview",
      "input": [
        {"type": "document", "uri": '$file_uri', "mimeType": "application/pdf"},
        {"type": "text", "text": "Can you add a few more lines to this poem?"}
      ]
    }' 2> /dev/null > response.json

cat response.json
echo

jq ".steps[-1].content[0].text" response.json

You can verify the API successfully stored the uploaded file and get its metadata by calling files.get. Only the name (and by extension, the uri) are unique.

Python

from google import genai
import pathlib

client = genai.Client()

fpath = pathlib.Path('example.pdf')
fpath.write_text('hello')

file = client.files.upload(file='example.pdf')

file_info = client.files.get(name=file.name)
print(file_info.model_dump_json(indent=4))

REST

name=$(jq ".file.name" file_info.json)
# Get the file of interest to check state
curl https://generativelanguage.googleapis.com/v1beta/files/$name?key=$GEMINI_API_KEY > file_info.json
# Print some information about the file you got
name=$(jq ".file.name" file_info.json)
echo name=$name
file_uri=$(jq ".file.uri" file_info.json)
echo file_uri=$file_uri

Passing multiple PDFs

The Gemini API is capable of processing multiple PDF documents (up to 1000 pages) in a single request, as long as the combined size of the documents and the text prompt stays within the model's context window.

Python

from google import genai
import io
import httpx

client = genai.Client()

doc_url_1 = "https://arxiv.org/pdf/2312.11805"
doc_url_2 = "https://arxiv.org/pdf/2403.05530"

# Retrieve and upload both PDFs using the File API
doc_data_1 = io.BytesIO(httpx.get(doc_url_1).content)
doc_data_2 = io.BytesIO(httpx.get(doc_url_2).content)

sample_pdf_1 = client.files.upload(
  file=doc_data_1,
  config=dict(mime_type='application/pdf')
)
sample_pdf_2 = client.files.upload(
  file=doc_data_2,
  config=dict(mime_type='application/pdf')
)

prompt = "What is the difference between each of the main benchmarks between these two papers? Output these in a table."

interaction = client.interactions.create(
    model="gemini-3-flash-preview",
    input=[
        {"type": "document", "uri": sample_pdf_1.uri, "mime_type": sample_pdf_1.mime_type},
        {"type": "document", "uri": sample_pdf_2.uri, "mime_type": sample_pdf_2.mime_type},
        {"type": "text", "text": prompt}
    ]
)

print(interaction.steps[-1].content[0].text)

JavaScript

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({});

async function uploadRemotePDF(url, displayName) {
    const pdfBuffer = await fetch(url)
        .then((response) => response.arrayBuffer());

    const fileBlob = new Blob([pdfBuffer], { type: 'application/pdf' });

    const file = await ai.files.upload({
        file: fileBlob,
        config: {
            displayName: displayName,
        },
    });

    // Wait for the file to be processed.
    let getFile = await ai.files.get({ name: file.name });
    while (getFile.state === 'PROCESSING') {
        getFile = await ai.files.get({ name: file.name });
        console.log(`current file status: ${getFile.state}`);
        console.log('File is still processing, retrying in 5 seconds');

        await new Promise((resolve) => {
            setTimeout(resolve, 5000);
        });
    }
    if (file.state === 'FAILED') {
        throw new Error('File processing failed.');
    }

    return file;
}

async function main() {
    const file1 = await uploadRemotePDF("https://arxiv.org/pdf/2312.11805", "PDF 1");
    const file2 = await uploadRemotePDF("https://arxiv.org/pdf/2403.05530", "PDF 2");

    const interaction = await ai.interactions.create({
        model: 'gemini-3-flash-preview',
        input: [
            { type: "document", uri: file1.uri, mimeType: file1.mimeType },
            { type: "document", uri: file2.uri, mimeType: file2.mimeType },
            { type: "text", text: "What is the difference between each of the main benchmarks between these two papers? Output these in a table." }
        ],
    });

    console.log(interaction.steps.at(-1).content[0].text);
}

main();

REST

DOC_URL_1="https://arxiv.org/pdf/2312.11805"
DOC_URL_2="https://arxiv.org/pdf/2403.05530"
DISPLAY_NAME_1="Gemini_paper"
DISPLAY_NAME_2="Gemini_1.5_paper"
PROMPT="What is the difference between each of the main benchmarks between these two papers? Output these in a table."

# Function to download and upload a PDF
upload_pdf() {
  local doc_url="$1"
  local display_name="$2"

  # Download the PDF
  wget -O "${display_name}.pdf" "${doc_url}"

  local MIME_TYPE=$(file -b --mime-type "${display_name}.pdf")
  local NUM_BYTES=$(wc -c < "${display_name}.pdf")

  echo "MIME_TYPE: ${MIME_TYPE}"
  echo "NUM_BYTES: ${NUM_BYTES}"

  local tmp_header_file=upload-header.tmp

  # Initial resumable request
  curl "https://generativelanguage.googleapis.com/upload/v1beta/files?key=${GOOGLE_API_KEY}" \
    -D "${tmp_header_file}" \
    -H "X-Goog-Upload-Protocol: resumable" \
    -H "X-Goog-Upload-Command: start" \
    -H "X-Goog-Upload-Header-Content-Length: ${NUM_BYTES}" \
    -H "X-Goog-Upload-Header-Content-Type: ${MIME_TYPE}" \
    -H "Content-Type: application/json" \
    -d "{'file': {'display_name': '${display_name}'}}" 2> /dev/null

  local upload_url=$(grep -i "x-goog-upload-url: " "${tmp_header_file}" | cut -d" " -f2 | tr -d "\r")
  rm "${tmp_header_file}"

  # Upload the PDF
  curl "${upload_url}" \
    -H "Content-Length: ${NUM_BYTES}" \
    -H "X-Goog-Upload-Offset: 0" \
    -H "X-Goog-Upload-Command: upload, finalize" \
    --data-binary "@${display_name}.pdf" 2> /dev/null > "file_info_${display_name}.json"

  local file_uri=$(jq ".file.uri" "file_info_${display_name}.json")
  echo "file_uri for ${display_name}: ${file_uri}"

  # Clean up the downloaded PDF
  rm "${display_name}.pdf"

  echo "${file_uri}"
}

# Upload the first PDF
file_uri_1=$(upload_pdf "${DOC_URL_1}" "${DISPLAY_NAME_1}")

# Upload the second PDF
file_uri_2=$(upload_pdf "${DOC_URL_2}" "${DISPLAY_NAME_2}")

# Now create an interaction using both files
curl "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "model": "gemini-3-flash-preview",
      "input": [
        {"type": "document", "uri": '$file_uri_1', "mimeType": "application/pdf"},
        {"type": "document", "uri": '$file_uri_2', "mimeType": "application/pdf"},
        {"type": "text", "text": "'$PROMPT'"}
      ]
    }' 2> /dev/null > response.json

cat response.json
echo

jq ".steps[-1].content[0].text" response.json

Technical details

Gemini supports PDF files up to 50MB or 1000 pages. This limit applies to both inline data and Files API uploads. 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 3072 x 3072 while preserving their original aspect ratio, while smaller pages are scaled up to 768 x 768 pixels. There is no cost reduction for pages at lower sizes, other than bandwidth, or performance improvement for pages at higher resolution.

Gemini 3 models

Gemini 3 introduces granular control over multimodal vision processing with the media_resolution parameter. You can now set the resolution to low, medium, or high per individual media part. With this addition, the processing of PDF documents has been updated:

  1. Native text inclusion: Text natively embedded in the PDF is extracted and provided to the model.
  2. Billing & token reporting:
    • You are not charged for tokens originating from the extracted native text in PDFs.
    • In the usage_metadata section of the API response, tokens generated from processing PDF pages (as images) are now counted under the IMAGE modality, not a separate DOCUMENT modality as in some earlier versions.

Document types

Technically, you can pass other MIME types for document understanding, like TXT, Markdown, HTML, XML, etc. However, document vision only meaningfully understands PDFs. Other types will be extracted as pure text, and the model won't be able to interpret what we see in the rendering of those files. Any file-type specifics like charts, diagrams, HTML tags, Markdown formatting, etc., will be lost.

To learn about other file input methods, see the File input methods guide.

Best practices

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.