Run Gemma with the Gemini API

The Gemini API provides hosted access to Gemma as a programming API you can use in application development or prototyping. This API is a convenient alternative to setting up your own local instance of Gemma and web service to handle generative AI tasks.

Supported Models

The Gemini API supports the following Gemma 4 models:

  • gemma-4-31b-it
  • gemma-4-26b-a4b-it

The following example shows how to use Gemma with the Gemini API:

Python

from google import genai

client = genai.Client(api_key="YOUR_API_KEY")

response = client.models.generate_content(
    model="gemma-4-31b-it",
    contents="Roses are red...",
)

print(response.text)

Node.js

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

const ai = new GoogleGenAI({ apiKey: "YOUR_API_KEY"});

const response = await ai.models.generateContent({
  model: "gemma-4-31b-it",
  contents: "Roses are red...",
});
console.log(response.text);

REST

curl "https://generativelanguage.googleapis.com/v1beta/models/gemma-4-31b-it:generateContent?key=YOUR_API_KEY" \
-H 'Content-Type: application/json' \
-X POST \
-d '{
  "contents": [{
    "parts":[{"text": "Roses are red..."}]
    }]
   }'

Get API Key

You can access the Gemini API on many platforms, such as mobile, web, and cloud services, and with multiple programming languages. For more information on Gemini API SDK packages, see the Gemini API SDK downloads page. For a general introduction to the Gemini API, see the Gemini API quickstart.

Thinking

Gemma 4 utilizes an internal "thinking process" that optimizes its multi-step reasoning, delivering superior performance in logically demanding domains such as algorithmic coding and advanced mathematical proofs.

While Gemma 4 strictly supports toggling this feature on or off, you enable it in the API by setting the thinking level to "high".

The following example demonstrates how to activate the thinking process:

Python

from google import genai

client = genai.Client(api_key="YOUR_API_KEY")

response = client.models.generate_content(
    model="gemma-4-31b-it",
    contents="What is the water formula?",
    config=types.GenerateContentConfig(
        thinking_config=types.ThinkingConfig(thinking_level="high")
    ),
)

print(response.text)

Node.js

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

const ai = new GoogleGenAI({ apiKey: "YOUR_API_KEY"});

const response = await ai.models.generateContent({
  model: "gemma-4-31b-it",
  contents: "What is the water formula?",
  config: {
    thinkingConfig: {
      thinkingLevel: ThinkingLevel.HIGH,
    },
  },
});
console.log(response.text);

REST

curl "https://generativelanguage.googleapis.com/v1beta/models/gemma-4-31b-it:generateContent?key=YOUR_API_KEY" \
-H 'Content-Type: application/json' \
-X POST \
-d '{
  "contents": [{
    "parts":[{"text": "What is the water formula?"}]
    }],
    "generationConfig": {
      "thinkingConfig": {
            "thinkingLevel": "high"
      }
    }
   }'

Learn more about Thinking:

Image Understanding

Gemma 4 models can process images, enabling many frontier developer use cases that would have historically required domain specific models.

The following example shows how to use Gemma Image inputs with the Gemini API:

Python

from google import genai

client = genai.Client(api_key="YOUR_API_KEY")

my_file = client.files.upload(file="path/to/sample.jpg")

response = client.models.generate_content(
    model="gemma-4-31b-it",
    contents=[my_file, "Caption this image."],
)

print(response.text)

Node.js

import {
  GoogleGenAI,
  createUserContent,
  createPartFromUri,
} from "@google/genai";

const ai = new GoogleGenAI({ apiKey: "YOUR_API_KEY" });

const myfile = await ai.files.upload({
  file: "path/to/sample.jpg",
  config: { mimeType: "image/jpeg" },
});

const response = await ai.models.generateContent({
  model: "gemma-4-31b-it",
  contents: createUserContent([
    createPartFromUri(myfile.uri, myfile.mimeType),
    "Caption this image.",
  ]),
});
console.log(response.text);
 ```

REST

IMAGE_PATH="cats-and-dogs.jpg"
MIME_TYPE=$(file -b --mime-type "${IMAGE_PATH}")
NUM_BYTES=$(wc -c < "${IMAGE_PATH}")
DISPLAY_NAME=IMAGE

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=YOUR_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: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Offset: 0" \
  -H "X-Goog-Upload-Command: upload, finalize" \
  --data-binary "@${IMAGE_PATH}" 2> /dev/null > file_info.json

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

# Now generate content using that file
curl "https://generativelanguage.googleapis.com/v1beta/models/gemma-4-31b-it:generateContent?key=YOUR_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[
          {"file_data":{"mime_type": "'"${MIME_TYPE}"'", "file_uri": "'"${file_uri}"'"}},
          {"text": "Caption this image."}]
        }]
      }' 2> /dev/null > response.json

cat response.json
echo

jq -r ".candidates[].content.parts[].text" response.json

Learn more about Image Understanding: