# Nano Banana image generation

Prompt to prototype fully-functional, UI-complete apps, and see Nano Banana 2 integrated with real-world tools, data, and the Gemini ecosystem. All before writing a single line of code.

- [Try a Nano Banana 2 app](https://aistudio.google.com/apps/bundled/pet_passport)
- Or build your own from prompts:
- ![magazine](https://storage.googleapis.com/generativeai-downloads/images/magazine-2.jpg) ![london](https://storage.googleapis.com/generativeai-downloads/images/Nano%20Banana%20Pro%20outputs%20for%20docs/05-output.jpg) ![restore](https://storage.googleapis.com/generativeai-downloads/images/quetzal.png) ![banana](https://storage.googleapis.com/generativeai-downloads/images/Nano%20Banana%20Pro%20outputs%20for%20docs/06-output.jpg) ![cafe](https://storage.googleapis.com/generativeai-downloads/images/Nano%20Banana%20Pro%20outputs%20for%20docs/02-a-photo-of-an-everyday-scene-at-a-busy-cafe-servin.jpg) ![article](https://storage.googleapis.com/generativeai-downloads/images/Nano%20Banana%20Pro%20outputs%20for%20docs/10-use-search-to-find-how-the-gemini-3-flash-launch-h.jpg) ![dog](https://storage.googleapis.com/generativeai-downloads/images/Nano%20Banana%20Pro%20outputs%20for%20docs/01-an-icon-representing-a-cute-dog-the-background-is-.jpg) ![isometric](https://storage.googleapis.com/generativeai-downloads/images/isometric-pool.jpg)
- ![magazine](https://storage.googleapis.com/generativeai-downloads/images/magazine-2.jpg) Generated by Nano Banana 2 **Prompt:** "A photo of a glossy magazine cover, the minimal blue cover has the large bold words Nano Banana. The text is in a serif font and fills the view. No other text. In front of the text there is a portrait of a person in a sleek and minimal dress. She is playfully holding the number 2, which is the focal point.  
  Put the issue number and "Feb 2026" date in the corner along with a barcode. The magazine is on a shelf against an orange plastered wall, within a designer store." Create [professional product shots](https://ai.google.dev/gemini-api/docs/image-generation#4_product_mockups_commercial_photography) in [AI Studio](https://aistudio.google.com/apps?features=chat_based_image_editing)
- ![london](https://storage.googleapis.com/generativeai-downloads/images/Nano%20Banana%20Pro%20outputs%20for%20docs/05-output.jpg) Generated by Nano Banana Pro **Prompt:** "Present a clear, 45° top-down isometric miniature 3D cartoon scene of London, featuring its most iconic landmarks and architectural elements. Use soft, refined textures with realistic PBR materials and gentle, lifelike lighting and shadows. Integrate the current weather conditions directly into the city environment to create an immersive atmospheric mood. Use a clean, minimalistic composition with a soft, solid-colored background. At the top-center, place the title "London" in large bold text, a prominent weather icon beneath it, then the date (small text) and temperature (medium text). All text must be centered with consistent spacing, and may subtly overlap the tops of the buildings." Learn more about [search grounding](https://ai.google.dev/gemini-api/docs/image-generation#use-with-grounding) and try it in [AI Studio](https://aistudio.google.com/apps?features=chat_based_image_editing,search_grounding)
- ![quetzal](https://storage.googleapis.com/generativeai-downloads/images/quetzal.png) Generated by Nano Banana 2 **Prompt:** "Use image search to find accurate images of a resplendent quetzal bird. Create a beautiful 3:2 wallpaper of this bird, with a natural top to bottom gradient and minimal composition." Use Google [Image Search](https://ai.google.dev/gemini-api/docs/image-generation#image-search) grounding with Nano Banana 2. Try it in [AI Studio](https://aistudio.google.com/apps?features=chat_based_image_editing,search_grounding)
- ![banana](https://storage.googleapis.com/generativeai-downloads/images/Nano%20Banana%20Pro%20outputs%20for%20docs/06.jpg) Generated by Nano Banana Pro **Prompt:** "Put this logo on a high-end ad for a banana scented perfume. The logo is perfectly integrated into the bottle." Try Nano Banana's [high fidelity detail preservation](https://ai.google.dev/gemini-api/docs/image-generation#5_high-fidelity_detail_preservation) in [AI Studio](https://aistudio.google.com/apps?features=chat_based_image_editing)
- ![cafe](https://storage.googleapis.com/generativeai-downloads/images/Nano%20Banana%20Pro%20outputs%20for%20docs/02-a-photo-of-an-everyday-scene-at-a-busy-cafe-servin.jpg) Generated by Nano Banana Pro **Prompt:** "A photo of an everyday scene at a busy cafe serving breakfast. In the foreground is an anime man with blue hair, one of the people is a pencil sketch, another is a claymation person" Experiment with different [artistic styles](https://ai.google.dev/gemini-api/docs/image-generation#3_style_transfer) with Nano Banana in [AI Studio](https://aistudio.google.com/apps?features=chat_based_image_editing)
- ![article](https://storage.googleapis.com/generativeai-downloads/images/Nano%20Banana%20Pro%20outputs%20for%20docs/10-use-search-to-find-how-the-gemini-3-flash-launch-h.jpg) Generated by Nano Banana Pro **Prompt:** "Use search to find how the Gemini 3 Flash launch has been received. Use this information to write a short article about it (with headings). Return a photo of the article as it appeared in a design focused glossy magazine. It is a photo of a single folded over page, showing the article about Gemini 3 Flash. One hero photo. Headline in serif." Generate [accurate text](https://ai.google.dev/gemini-api/docs/image-generation#3_accurate_text_in_images) from [search](https://ai.google.dev/gemini-api/docs/image-generation#use-with-grounding). Try Nano Banana in [AI Studio](https://aistudio.google.com/apps?features=chat_based_image_editing,search_grounding)
- ![dog](https://storage.googleapis.com/generativeai-downloads/images/Nano%20Banana%20Pro%20outputs%20for%20docs/01-an-icon-representing-a-cute-dog-the-background-is-.jpg) Generated by Nano Banana Pro **Prompt:** "An icon representing a cute dog. The background is white. Make the icons in a colorful and tactile 3D style. No text." Create [icons, stickers, and assets](https://ai.google.dev/gemini-api/docs/image-generation#2_stylized_illustrations_stickers) with Nano Banana in [AI Studio](https://aistudio.google.com/apps?features=chat_based_image_editing,search_grounding)
- ![isometric](https://storage.googleapis.com/generativeai-downloads/images/isometric-pool.jpg) Generated by Nano Banana 2 **Prompt:** "Make a photo that is perfectly isometric. It is not a miniature, it is a captured photo that just happened to be perfectly isometric. It is a photo of a beautiful modern garden. There's a large 2 shaped pool and the words: Nano Banana 2." Try [photorealistic image generation](https://ai.google.dev/gemini-api/docs/image-generation#1_photorealistic_scenes) in [AI Studio](https://aistudio.google.com/apps?features=chat_based_image_editing)

**Nano Banana** is the name for Gemini's native image generation capabilities.
Gemini can generate and process images conversationally
with text, images, or a combination of both. This lets you create, edit, and
iterate on visuals with unprecedented control.

Nano Banana refers to three distinct models available in the Gemini API:

- **Nano Banana 2** : The [Gemini 3.1 Flash Image Preview](https://ai.google.dev/gemini-api/docs/models/gemini-3.1-flash-image-preview) model (`gemini-3.1-flash-image-preview`). This model serves as the high-efficiency counterpart to Gemini 3 Pro Image, optimized for speed and high-volume developer use cases.
- **Nano Banana Pro** : The [Gemini 3 Pro Image Preview](https://ai.google.dev/gemini-api/docs/models/gemini-3-pro-image-preview) model (`gemini-3-pro-image-preview`). This model is designed for professional asset production, utilizing advanced reasoning ("Thinking") to follow complex instructions and render high-fidelity text.
- **Nano Banana** : The [Gemini 2.5 Flash Image](https://ai.google.dev/gemini-api/docs/models/gemini-2.5-flash-image) model (`gemini-2.5-flash-image`). This model is designed for speed and efficiency, optimized for high-volume, low-latency tasks.

All generated images include a [SynthID watermark](https://ai.google.dev/responsible/docs/safeguards/synthid).

## Image generation (text-to-image)

### Python

    from google import genai
    from google.genai import types
    from PIL import Image

    client = genai.Client()

    prompt = ("Create a picture of a nano banana dish in a fancy restaurant with a Gemini theme")
    response = client.models.generate_content(
        model="gemini-3.1-flash-image-preview",
        contents=[prompt],
    )

    for part in response.parts:
        if part.text is not None:
            print(part.text)
        elif part.inline_data is not None:
            image = part.as_image()
            image.save("generated_image.png")

### JavaScript

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

    async function main() {

      const ai = new GoogleGenAI({});

      const prompt =
        "Create a picture of a nano banana dish in a fancy restaurant with a Gemini theme";

      const response = await ai.models.generateContent({
        model: "gemini-3.1-flash-image-preview",
        contents: prompt,
      });
      for (const part of response.candidates[0].content.parts) {
        if (part.text) {
          console.log(part.text);
        } else if (part.inlineData) {
          const imageData = part.inlineData.data;
          const buffer = Buffer.from(imageData, "base64");
          fs.writeFileSync("gemini-native-image.png", buffer);
          console.log("Image saved as gemini-native-image.png");
        }
      }
    }

    main();

### Go

    package main

    import (
      "context"
      "fmt"
      "log"
      "os"
      "google.golang.org/genai"
    )

    func main() {

      ctx := context.Background()
      client, err := genai.NewClient(ctx, nil)
      if err != nil {
          log.Fatal(err)
      }

      result, _ := client.Models.GenerateContent(
          ctx,
          "gemini-3.1-flash-image-preview",
          genai.Text("Create a picture of a nano banana dish in a " +
                     " fancy restaurant with a Gemini theme"),
      )

      for _, part := range result.Candidates[0].Content.Parts {
          if part.Text != "" {
              fmt.Println(part.Text)
          } else if part.InlineData != nil {
              imageBytes := part.InlineData.Data
              outputFilename := "gemini_generated_image.png"
              _ = os.WriteFile(outputFilename, imageBytes, 0644)
          }
      }
    }

### Java

    import com.google.genai.Client;
    import com.google.genai.types.GenerateContentConfig;
    import com.google.genai.types.GenerateContentResponse;
    import com.google.genai.types.Part;

    import java.io.IOException;
    import java.nio.file.Files;
    import java.nio.file.Paths;

    public class TextToImage {
      public static void main(String[] args) throws IOException {

        try (Client client = new Client()) {
          GenerateContentConfig config = GenerateContentConfig.builder()
              .responseModalities("TEXT", "IMAGE")
              .build();

          GenerateContentResponse response = client.models.generateContent(
              "gemini-3.1-flash-image-preview",
              "Create a picture of a nano banana dish in a fancy restaurant with a Gemini theme",
              config);

          for (Part part : response.parts()) {
            if (part.text().isPresent()) {
              System.out.println(part.text().get());
            } else if (part.inlineData().isPresent()) {
              var blob = part.inlineData().get();
              if (blob.data().isPresent()) {
                Files.write(Paths.get("_01_generated_image.png"), blob.data().get());
              }
            }
          }
        }
      }
    }

### REST

    curl -s -X POST \
      "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent" \
      -H "x-goog-api-key: $GEMINI_API_KEY" \
      -H "Content-Type: application/json" \
      -d '{
        "contents": [{
          "parts": [
            {"text": "Create a picture of a nano banana dish in a fancy restaurant with a Gemini theme"}
          ]
        }]
      }'

## Image editing (text-and-image-to-image)

**Reminder** : Make sure you have the necessary rights to any images you upload.
Don't generate content that infringe on others' rights, including videos or
images that deceive, harass, or harm. Your use of this generative AI service is
subject to our [Prohibited Use Policy](https://policies.google.com/terms/generative-ai/use-policy).

Provide an image and use text prompts to add, remove, or modify elements,
change the style, or adjust the color grading.

The following example demonstrates uploading `base64` encoded images.
For multiple images, larger payloads, and supported MIME types, check the [Image
understanding](https://ai.google.dev/gemini-api/docs/image-understanding) page.

### Python

    from google import genai
    from google.genai import types
    from PIL import Image

    client = genai.Client()

    prompt = (
        "Create a picture of my cat eating a nano-banana in a "
        "fancy restaurant under the Gemini constellation",
    )

    image = Image.open("/path/to/cat_image.png")

    response = client.models.generate_content(
        model="gemini-3.1-flash-image-preview",
        contents=[prompt, image],
    )

    for part in response.parts:
        if part.text is not None:
            print(part.text)
        elif part.inline_data is not None:
            image = part.as_image()
            image.save("generated_image.png")

### JavaScript

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

    async function main() {

      const ai = new GoogleGenAI({});

      const imagePath = "path/to/cat_image.png";
      const imageData = fs.readFileSync(imagePath);
      const base64Image = imageData.toString("base64");

      const prompt = [
        { text: "Create a picture of my cat eating a nano-banana in a" +
                "fancy restaurant under the Gemini constellation" },
        {
          inlineData: {
            mimeType: "image/png",
            data: base64Image,
          },
        },
      ];

      const response = await ai.models.generateContent({
        model: "gemini-3.1-flash-image-preview",
        contents: prompt,
      });
      for (const part of response.candidates[0].content.parts) {
        if (part.text) {
          console.log(part.text);
        } else if (part.inlineData) {
          const imageData = part.inlineData.data;
          const buffer = Buffer.from(imageData, "base64");
          fs.writeFileSync("gemini-native-image.png", buffer);
          console.log("Image saved as gemini-native-image.png");
        }
      }
    }

    main();

### Go

    package main

    import (
     "context"
     "fmt"
     "log"
     "os"
     "google.golang.org/genai"
    )

    func main() {

     ctx := context.Background()
     client, err := genai.NewClient(ctx, nil)
     if err != nil {
         log.Fatal(err)
     }

     imagePath := "/path/to/cat_image.png"
     imgData, _ := os.ReadFile(imagePath)

     parts := []*genai.Part{
       genai.NewPartFromText("Create a picture of my cat eating a nano-banana in a fancy restaurant under the Gemini constellation"),
       &genai.Part{
         InlineData: &genai.Blob{
           MIMEType: "image/png",
           Data:     imgData,
         },
       },
     }

     contents := []*genai.Content{
       genai.NewContentFromParts(parts, genai.RoleUser),
     }

     result, _ := client.Models.GenerateContent(
         ctx,
         "gemini-3.1-flash-image-preview",
         contents,
     )

     for _, part := range result.Candidates[0].Content.Parts {
         if part.Text != "" {
             fmt.Println(part.Text)
         } else if part.InlineData != nil {
             imageBytes := part.InlineData.Data
             outputFilename := "gemini_generated_image.png"
             _ = os.WriteFile(outputFilename, imageBytes, 0644)
         }
     }
    }

### Java

    import com.google.genai.Client;
    import com.google.genai.types.Content;
    import com.google.genai.types.GenerateContentConfig;
    import com.google.genai.types.GenerateContentResponse;
    import com.google.genai.types.Part;

    import java.io.IOException;
    import java.nio.file.Files;
    import java.nio.file.Path;
    import java.nio.file.Paths;

    public class TextAndImageToImage {
      public static void main(String[] args) throws IOException {

        try (Client client = new Client()) {
          GenerateContentConfig config = GenerateContentConfig.builder()
              .responseModalities("TEXT", "IMAGE")
              .build();

          GenerateContentResponse response = client.models.generateContent(
              "gemini-3.1-flash-image-preview",
              Content.fromParts(
                  Part.fromText("""
                      Create a picture of my cat eating a nano-banana in
                      a fancy restaurant under the Gemini constellation
                      """),
                  Part.fromBytes(
                      Files.readAllBytes(
                          Path.of("src/main/resources/cat.jpg")),
                      "image/jpeg")),
              config);

          for (Part part : response.parts()) {
            if (part.text().isPresent()) {
              System.out.println(part.text().get());
            } else if (part.inlineData().isPresent()) {
              var blob = part.inlineData().get();
              if (blob.data().isPresent()) {
                Files.write(Paths.get("gemini_generated_image.png"), blob.data().get());
              }
            }
          }
        }
      }
    }

### REST

    curl -s -X POST \
      "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent" \
        -H "x-goog-api-key: $GEMINI_API_KEY" \
        -H 'Content-Type: application/json' \
        -d "{
          \"contents\": [{
            \"parts\":[
                {\"text\": \"'Create a picture of my cat eating a nano-banana in a fancy restaurant under the Gemini constellation\"},
                {
                  \"inline_data\": {
                    \"mime_type\":\"image/jpeg\",
                    \"data\": \"<BASE64_IMAGE_DATA>\"
                  }
                }
            ]
          }]
        }"

### Multi-turn image editing

Keep generating and editing images conversationally. Chat or multi-turn
conversation is the recommended way to iterate on images. The following
example shows a prompt to generate an infographic about photosynthesis.

### Python

    from google import genai
    from google.genai import types

    client = genai.Client()

    chat = client.chats.create(
        model="gemini-3.1-flash-image-preview",
        config=types.GenerateContentConfig(
            response_modalities=['TEXT', 'IMAGE'],
            tools=[{"google_search": {}}]
        )
    )

    message = "Create a vibrant infographic that explains photosynthesis as if it were a recipe for a plant's favorite food. Show the \"ingredients\" (sunlight, water, CO2) and the \"finished dish\" (sugar/energy). The style should be like a page from a colorful kids' cookbook, suitable for a 4th grader."

    response = chat.send_message(message)

    for part in response.parts:
        if part.text is not None:
            print(part.text)
        elif image:= part.as_image():
            image.save("photosynthesis.png")

### Javascript

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

    const ai = new GoogleGenAI({});

    async function main() {
      const chat = ai.chats.create({
        model: "gemini-3.1-flash-image-preview",
        config: {
          responseModalities: ['TEXT', 'IMAGE'],
          tools: [{googleSearch: {}}],
        },
      });
    }

    await main();

    const message = "Create a vibrant infographic that explains photosynthesis as if it were a recipe for a plant's favorite food. Show the \"ingredients\" (sunlight, water, CO2) and the \"finished dish\" (sugar/energy). The style should be like a page from a colorful kids' cookbook, suitable for a 4th grader."

    let response = await chat.sendMessage({message});

    for (const part of response.candidates[0].content.parts) {
        if (part.text) {
          console.log(part.text);
        } else if (part.inlineData) {
          const imageData = part.inlineData.data;
          const buffer = Buffer.from(imageData, "base64");
          fs.writeFileSync("photosynthesis.png", buffer);
          console.log("Image saved as photosynthesis.png");
        }
    }

### Go

    package main

    import (
        "context"
        "fmt"
        "log"
        "os"

        "google.golang.org/genai"
    )

    func main() {
        ctx := context.Background()
        client, err := genai.NewClient(ctx, nil)
        if err != nil {
            log.Fatal(err)
        }
        defer client.Close()

        model := client.GenerativeModel("gemini-3.1-flash-image-preview")
        model.GenerationConfig = &pb.GenerationConfig{
            ResponseModalities: []pb.ResponseModality{genai.Text, genai.Image},
        }
        chat := model.StartChat()

        message := "Create a vibrant infographic that explains photosynthesis as if it were a recipe for a plant's favorite food. Show the \"ingredients\" (sunlight, water, CO2) and the \"finished dish\" (sugar/energy). The style should be like a page from a colorful kids' cookbook, suitable for a 4th grader."

        resp, err := chat.SendMessage(ctx, genai.Text(message))
        if err != nil {
            log.Fatal(err)
        }

        for _, part := range resp.Candidates[0].Content.Parts {
            if txt, ok := part.(genai.Text); ok {
                fmt.Printf("%s", string(txt))
            } else if img, ok := part.(genai.ImageData); ok {
                err := os.WriteFile("photosynthesis.png", img.Data, 0644)
                if err != nil {
                    log.Fatal(err)
                }
            }
        }
    }

### Java

    import com.google.genai.Chat;
    import com.google.genai.Client;
    import com.google.genai.types.Content;
    import com.google.genai.types.GenerateContentConfig;
    import com.google.genai.types.GenerateContentResponse;
    import com.google.genai.types.GoogleSearch;
    import com.google.genai.types.ImageConfig;
    import com.google.genai.types.Part;
    import com.google.genai.types.RetrievalConfig;
    import com.google.genai.types.Tool;
    import com.google.genai.types.ToolConfig;

    import java.io.IOException;
    import java.nio.file.Files;
    import java.nio.file.Path;
    import java.nio.file.Paths;

    public class MultiturnImageEditing {
      public static void main(String[] args) throws IOException {

        try (Client client = new Client()) {

          GenerateContentConfig config = GenerateContentConfig.builder()
              .responseModalities("TEXT", "IMAGE")
              .tools(Tool.builder()
                  .googleSearch(GoogleSearch.builder().build())
                  .build())
              .build();

          Chat chat = client.chats.create("gemini-3.1-flash-image-preview", config);

          GenerateContentResponse response = chat.sendMessage("""
              Create a vibrant infographic that explains photosynthesis
              as if it were a recipe for a plant's favorite food.
              Show the "ingredients" (sunlight, water, CO2)
              and the "finished dish" (sugar/energy).
              The style should be like a page from a colorful
              kids' cookbook, suitable for a 4th grader.
              """);

          for (Part part : response.parts()) {
            if (part.text().isPresent()) {
              System.out.println(part.text().get());
            } else if (part.inlineData().isPresent()) {
              var blob = part.inlineData().get();
              if (blob.data().isPresent()) {
                Files.write(Paths.get("photosynthesis.png"), blob.data().get());
              }
            }
          }
          // ...
        }
      }
    }

### REST

    curl -s -X POST \
      "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent" \
      -H "x-goog-api-key: $GEMINI_API_KEY" \
      -H "Content-Type: application/json" \
      -d '{
        "contents": [{
          "role": "user",
          "parts": [
            {"text": "Create a vibrant infographic that explains photosynthesis as if it were a recipe for a plants favorite food. Show the \"ingredients\" (sunlight, water, CO2) and the \"finished dish\" (sugar/energy). The style should be like a page from a colorful kids cookbook, suitable for a 4th grader."}
          ]
        }],
        "generationConfig": {
          "responseModalities": ["TEXT", "IMAGE"]
        }
      }'

![AI-generated infographic about photosynthesis](https://ai.google.dev/static/gemini-api/docs/images/infographic-eng.png) AI-generated infographic about photosynthesis

You can then use the same chat to change the language on the graphic to Spanish.

### Python

    message = "Update this infographic to be in Spanish. Do not change any other elements of the image."
    aspect_ratio = "16:9" # "1:1","1:4","1:8","2:3","3:2","3:4","4:1","4:3","4:5","5:4","8:1","9:16","16:9","21:9"
    resolution = "2K" # "512", "1K", "2K", "4K"

    response = chat.send_message(message,
        config=types.GenerateContentConfig(
            image_config=types.ImageConfig(
                aspect_ratio=aspect_ratio,
                image_size=resolution
            ),
        ))

    for part in response.parts:
        if part.text is not None:
            print(part.text)
        elif image:= part.as_image():
            image.save("photosynthesis_spanish.png")

### Javascript

    const message = 'Update this infographic to be in Spanish. Do not change any other elements of the image.';
    const aspectRatio = '16:9';
    const resolution = '2K';

    let response = await chat.sendMessage({
      message,
      config: {
        responseModalities: ['TEXT', 'IMAGE'],
        imageConfig: {
          aspectRatio: aspectRatio,
          imageSize: resolution,
        },
        tools: [{googleSearch: {}}],
      },
    });

    for (const part of response.candidates[0].content.parts) {
        if (part.text) {
          console.log(part.text);
        } else if (part.inlineData) {
          const imageData = part.inlineData.data;
          const buffer = Buffer.from(imageData, "base64");
          fs.writeFileSync("photosynthesis2.png", buffer);
          console.log("Image saved as photosynthesis2.png");
        }
    }

### Go

    message = "Update this infographic to be in Spanish. Do not change any other elements of the image."
    aspect_ratio = "16:9" // "1:1","1:4","1:8","2:3","3:2","3:4","4:1","4:3","4:5","5:4","8:1","9:16","16:9","21:9"
    resolution = "2K"     // "512", "1K", "2K", "4K"

    model.GenerationConfig.ImageConfig = &pb.ImageConfig{
        AspectRatio: aspect_ratio,
        ImageSize:   resolution,
    }

    resp, err = chat.SendMessage(ctx, genai.Text(message))
    if err != nil {
        log.Fatal(err)
    }

    for _, part := range resp.Candidates[0].Content.Parts {
        if txt, ok := part.(genai.Text); ok {
            fmt.Printf("%s", string(txt))
        } else if img, ok := part.(genai.ImageData); ok {
            err := os.WriteFile("photosynthesis_spanish.png", img.Data, 0644)
            if err != nil {
                log.Fatal(err)
            }
        }
    }

### Java

    String aspectRatio = "16:9"; // "1:1","1:4","1:8","2:3","3:2","3:4","4:1","4:3","4:5","5:4","8:1","9:16","16:9","21:9"
    String resolution = "2K"; // "512", "1K", "2K", "4K"

    config = GenerateContentConfig.builder()
        .responseModalities("TEXT", "IMAGE")
        .imageConfig(ImageConfig.builder()
            .aspectRatio(aspectRatio)
            .imageSize(resolution)
            .build())
        .build();

    response = chat.sendMessage(
        "Update this infographic to be in Spanish. " +
        "Do not change any other elements of the image.",
        config);

    for (Part part : response.parts()) {
      if (part.text().isPresent()) {
        System.out.println(part.text().get());
      } else if (part.inlineData().isPresent()) {
        var blob = part.inlineData().get();
        if (blob.data().isPresent()) {
          Files.write(Paths.get("photosynthesis_spanish.png"), blob.data().get());
        }
      }
    }

### REST

    curl -s -X POST \
      "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent" \
      -H "x-goog-api-key: $GEMINI_API_KEY" \
      -H 'Content-Type: application/json' \
      -d '{
        "contents": [
          {
            "role": "user",
            "parts": [{"text": "Create a vibrant infographic that explains photosynthesis..."}]
          },
          {
            "role": "model",
            "parts": [{"inline_data": {"mime_type": "image/png", "data": "<PREVIOUS_IMAGE_DATA>"}}]
          },
          {
            "role": "user",
            "parts": [{"text": "Update this infographic to be in Spanish. Do not change any other elements of the image."}]
          }
        ],
        "tools": [{"google_search": {}}],
        "generationConfig": {
          "responseModalities": ["TEXT", "IMAGE"],
          "imageConfig": {
            "aspectRatio": "16:9",
            "imageSize": "2K"
          }
        }
      }'

![AI-generated infographic of photosynthesis in Spanish](https://ai.google.dev/static/gemini-api/docs/images/infographic-spanish.png) AI-generated infographic of photosynthesis in Spanish

## New with Gemini 3 Image models

Gemini 3 offers state-of-the-art image generation and editing models. Gemini 3.1
Flash Image is optimized for speed and high-volume use-cases, and Gemini 3
Pro Image is optimized for professional asset production.
Designed to tackle the most challenging workflows through advanced reasoning,
they excel at complex, multi-turn creation and modification tasks.

- **High-resolution output** : Built-in generation capabilities for 1K, 2K, and 4K visuals.
  - **Gemini 3.1 Flash Image** adds the smaller 512 (0.5K) resolution.
- **Advanced text rendering**: Capable of generating legible, stylized text for infographics, menus, diagrams, and marketing assets.
- **Grounding with Google Search** : The model can use Google Search as a tool to verify facts and generate imagery based on real-time data (e.g., current weather maps, stock charts, recent events).
  - **Gemini 3.1 Flash Image** adds the integration of Grounding with Google Search for Images alongside Web Search.
- **Thinking mode**: The model utilizes a "thinking" process to reason through complex prompts. It generates interim "thought images" (visible in the backend but not charged) to refine the composition before producing the final high-quality output.
- **Up to 14 reference images**: You can now mix up to 14 reference images to produce the final image.
- **New aspect ratios** : Gemini 3.1 Flash Image Preview adds 1:4, 4:1, 1:8, and 8:1 [aspect ratios](https://ai.google.dev/gemini-api/docs/image-generation#aspect_ratios_and_image_size).

### Use up to 14 reference images

Gemini 3 image models let you to mix up to 14 reference images. These 14 images
can include the following:

| Gemini 3.1 Flash Image Preview | Gemini 3 Pro Image Preview |
|---|---|
| Up to 10 images of objects with high-fidelity to include in the final image | Up to 6 images of objects with high-fidelity to include in the final image |
| Up to 4 images of characters to maintain character consistency | Up to 5 images of characters to maintain character consistency |

### Python

    from google import genai
    from google.genai import types
    from PIL import Image

    prompt = "An office group photo of these people, they are making funny faces."
    aspect_ratio = "5:4" # "1:1","1:4","1:8","2:3","3:2","3:4","4:1","4:3","4:5","5:4","8:1","9:16","16:9","21:9"
    resolution = "2K" # "512", "1K", "2K", "4K"

    client = genai.Client()

    response = client.models.generate_content(
        model="gemini-3.1-flash-image-preview",
        contents=[
            prompt,
            Image.open('person1.png'),
            Image.open('person2.png'),
            Image.open('person3.png'),
            Image.open('person4.png'),
            Image.open('person5.png'),
        ],
        config=types.GenerateContentConfig(
            response_modalities=['TEXT', 'IMAGE'],
            image_config=types.ImageConfig(
                aspect_ratio=aspect_ratio,
                image_size=resolution
            ),
        )
    )

    for part in response.parts:
        if part.text is not None:
            print(part.text)
        elif image:= part.as_image():
            image.save("office.png")

### Javascript

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

    async function main() {

      const ai = new GoogleGenAI({});

      const prompt =
          'An office group photo of these people, they are making funny faces.';
      const aspectRatio = '5:4';
      const resolution = '2K';

    const contents = [
      { text: prompt },
      {
        inlineData: {
          mimeType: "image/jpeg",
          data: base64ImageFile1,
        },
      },
      {
        inlineData: {
          mimeType: "image/jpeg",
          data: base64ImageFile2,
        },
      },
      {
        inlineData: {
          mimeType: "image/jpeg",
          data: base64ImageFile3,
        },
      },
      {
        inlineData: {
          mimeType: "image/jpeg",
          data: base64ImageFile4,
        },
      },
      {
        inlineData: {
          mimeType: "image/jpeg",
          data: base64ImageFile5,
        },
      }
    ];

    const response = await ai.models.generateContent({
        model: 'gemini-3.1-flash-image-preview',
        contents: contents,
        config: {
          responseModalities: ['TEXT', 'IMAGE'],
          imageConfig: {
            aspectRatio: aspectRatio,
            imageSize: resolution,
          },
        },
      });

      for (const part of response.candidates[0].content.parts) {
        if (part.text) {
          console.log(part.text);
        } else if (part.inlineData) {
          const imageData = part.inlineData.data;
          const buffer = Buffer.from(imageData, "base64");
          fs.writeFileSync("image.png", buffer);
          console.log("Image saved as image.png");
        }
      }

    }

    main();

### Go

    package main

    import (
        "context"
        "fmt"
        "log"
        "os"

        "google.golang.org/genai"
    )

    func main() {
        ctx := context.Background()
        client, err := genai.NewClient(ctx, nil)
        if err != nil {
            log.Fatal(err)
        }
        defer client.Close()

        model := client.GenerativeModel("gemini-3.1-flash-image-preview")
        model.GenerationConfig = &pb.GenerationConfig{
            ResponseModalities: []pb.ResponseModality{genai.Text, genai.Image},
            ImageConfig: &pb.ImageConfig{
                AspectRatio: "5:4",
                ImageSize:   "2K",
            },
        }

        img1, err := os.ReadFile("person1.png")
        if err != nil { log.Fatal(err) }
        img2, err := os.ReadFile("person2.png")
        if err != nil { log.Fatal(err) }
        img3, err := os.ReadFile("person3.png")
        if err != nil { log.Fatal(err) }
        img4, err := os.ReadFile("person4.png")
        if err != nil { log.Fatal(err) }
        img5, err := os.ReadFile("person5.png")
        if err != nil { log.Fatal(err) }

        parts := []genai.Part{
            genai.Text("An office group photo of these people, they are making funny faces."),
            genai.ImageData{MIMEType: "image/png", Data: img1},
            genai.ImageData{MIMEType: "image/png", Data: img2},
            genai.ImageData{MIMEType: "image/png", Data: img3},
            genai.ImageData{MIMEType: "image/png", Data: img4},
            genai.ImageData{MIMEType: "image/png", Data: img5},
        }

        resp, err := model.GenerateContent(ctx, parts...)
        if err != nil {
            log.Fatal(err)
        }

        for _, part := range resp.Candidates[0].Content.Parts {
            if txt, ok := part.(genai.Text); ok {
                fmt.Printf("%s", string(txt))
            } else if img, ok := part.(genai.ImageData); ok {
                err := os.WriteFile("office.png", img.Data, 0644)
                if err != nil {
                    log.Fatal(err)
                }
            }
        }
    }

### Java

    import com.google.genai.Client;
    import com.google.genai.types.Content;
    import com.google.genai.types.GenerateContentConfig;
    import com.google.genai.types.GenerateContentResponse;
    import com.google.genai.types.ImageConfig;
    import com.google.genai.types.Part;

    import java.io.IOException;
    import java.nio.file.Files;
    import java.nio.file.Path;
    import java.nio.file.Paths;

    public class GroupPhoto {
      public static void main(String[] args) throws IOException {

        try (Client client = new Client()) {
          GenerateContentConfig config = GenerateContentConfig.builder()
              .responseModalities("TEXT", "IMAGE")
              .imageConfig(ImageConfig.builder()
                  .aspectRatio("5:4")
                  .imageSize("2K")
                  .build())
              .build();

          GenerateContentResponse response = client.models.generateContent(
              "gemini-3.1-flash-image-preview",
              Content.fromParts(
                  Part.fromText("An office group photo of these people, they are making funny faces."),
                  Part.fromBytes(Files.readAllBytes(Path.of("person1.png")), "image/png"),
                  Part.fromBytes(Files.readAllBytes(Path.of("person2.png")), "image/png"),
                  Part.fromBytes(Files.readAllBytes(Path.of("person3.png")), "image/png"),
                  Part.fromBytes(Files.readAllBytes(Path.of("person4.png")), "image/png"),
                  Part.fromBytes(Files.readAllBytes(Path.of("person5.png")), "image/png")
              ), config);

          for (Part part : response.parts()) {
            if (part.text().isPresent()) {
              System.out.println(part.text().get());
            } else if (part.inlineData().isPresent()) {
              var blob = part.inlineData().get();
              if (blob.data().isPresent()) {
                Files.write(Paths.get("office.png"), blob.data().get());
              }
            }
          }
        }
      }
    }

### REST

    curl -s -X POST \
      "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent" \
        -H "x-goog-api-key: $GEMINI_API_KEY" \
        -H 'Content-Type: application/json' \
        -d "{
          \"contents\": [{
            \"parts\":[
                {\"text\": \"An office group photo of these people, they are making funny faces.\"},
                {\"inline_data\": {\"mime_type\":\"image/png\", \"data\": \"<BASE64_DATA_IMG_1>\"}},
                {\"inline_data\": {\"mime_type\":\"image/png\", \"data\": \"<BASE64_DATA_IMG_2>\"}},
                {\"inline_data\": {\"mime_type\":\"image/png\", \"data\": \"<BASE64_DATA_IMG_3>\"}},
                {\"inline_data\": {\"mime_type\":\"image/png\", \"data\": \"<BASE64_DATA_IMG_4>\"}},
                {\"inline_data\": {\"mime_type\":\"image/png\", \"data\": \"<BASE64_DATA_IMG_5>\"}}
            ]
          }],
          \"generationConfig\": {
            \"responseModalities\": [\"TEXT\", \"IMAGE\"],
            \"imageConfig\": {
              \"aspectRatio\": \"5:4\",
              \"imageSize\": \"2K\"
            }
          }
        }"

![AI-generated office group photo](https://ai.google.dev/static/gemini-api/docs/images/office-group-photo.jpeg) AI-generated office group photo

### Grounding with Google Search

Use the [Google Search tool](https://ai.google.dev/gemini-api/docs/google-search) to generate images
based on real-time information, such as weather forecasts, stock charts, or
recent events.

Note that when using Grounding with Google Search with image generation,
image-based search results are not passed to the generation model and are
excluded from the response (see [Grounding with Google Search for images](https://ai.google.dev/gemini-api/docs/image-generation#image-search))

### Python

    from google import genai
    prompt = "Visualize the current weather forecast for the next 5 days in San Francisco as a clean, modern weather chart. Add a visual on what I should wear each day"
    aspect_ratio = "16:9" # "1:1","1:4","1:8","2:3","3:2","3:4","4:1","4:3","4:5","5:4","8:1","9:16","16:9","21:9"

    client = genai.Client()

    response = client.models.generate_content(
        model="gemini-3.1-flash-image-preview",
        contents=prompt,
        config=types.GenerateContentConfig(
            response_modalities=['Text', 'Image'],
            image_config=types.ImageConfig(
                aspect_ratio=aspect_ratio,
            ),
            tools=[{"google_search": {}}]
        )
    )

    for part in response.parts:
        if part.text is not None:
            print(part.text)
        elif image:= part.as_image():
            image.save("weather.png")

### Javascript

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

    async function main() {

      const ai = new GoogleGenAI({});

      const prompt = 'Visualize the current weather forecast for the next 5 days in San Francisco as a clean, modern weather chart. Add a visual on what I should wear each day';
      const aspectRatio = '16:9';
      const resolution = '2K';

    const response = await ai.models.generateContent({
        model: 'gemini-3.1-flash-image-preview',
        contents: prompt,
        config: {
          responseModalities: ['TEXT', 'IMAGE'],
          imageConfig: {
            aspectRatio: aspectRatio,
            imageSize: resolution,
          },
        tools: [{ googleSearch: {} }]
        },
      });

      for (const part of response.candidates[0].content.parts) {
        if (part.text) {
          console.log(part.text);
        } else if (part.inlineData) {
          const imageData = part.inlineData.data;
          const buffer = Buffer.from(imageData, "base64");
          fs.writeFileSync("image.png", buffer);
          console.log("Image saved as image.png");
        }
      }

    }

    main();

### Java

    import com.google.genai.Client;
    import com.google.genai.types.GenerateContentConfig;
    import com.google.genai.types.GenerateContentResponse;
    import com.google.genai.types.GoogleSearch;
    import com.google.genai.types.ImageConfig;
    import com.google.genai.types.Part;
    import com.google.genai.types.Tool;

    import java.io.IOException;
    import java.nio.file.Files;
    import java.nio.file.Paths;

    public class SearchGrounding {
      public static void main(String[] args) throws IOException {

        try (Client client = new Client()) {
          GenerateContentConfig config = GenerateContentConfig.builder()
              .responseModalities("TEXT", "IMAGE")
              .imageConfig(ImageConfig.builder()
                  .aspectRatio("16:9")
                  .build())
              .tools(Tool.builder()
                  .googleSearch(GoogleSearch.builder().build())
                  .build())
              .build();

          GenerateContentResponse response = client.models.generateContent(
              "gemini-3.1-flash-image-preview", """
                  Visualize the current weather forecast for the next 5 days
                  in San Francisco as a clean, modern weather chart.
                  Add a visual on what I should wear each day
                  """,
              config);

          for (Part part : response.parts()) {
            if (part.text().isPresent()) {
              System.out.println(part.text().get());
            } else if (part.inlineData().isPresent()) {
              var blob = part.inlineData().get();
              if (blob.data().isPresent()) {
                Files.write(Paths.get("weather.png"), blob.data().get());
              }
            }
          }
        }
      }
    }

### REST

    curl -s -X POST \
      "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent" \
      -H "x-goog-api-key: $GEMINI_API_KEY" \
      -H "Content-Type: application/json" \
      -d '{
        "contents": [{"parts": [{"text": "Visualize the current weather forecast for the next 5 days in San Francisco as a clean, modern weather chart. Add a visual on what I should wear each day"}]}],
        "tools": [{"google_search": {}}],
        "generationConfig": {
          "responseModalities": ["TEXT", "IMAGE"],
          "imageConfig": {"aspectRatio": "16:9"}
        }
      }'

![AI-generated five day weather chart for San Francisco](https://ai.google.dev/static/gemini-api/docs/images/weather-forecast.png) AI-generated five day weather chart for San Francisco

The response includes `groundingMetadata` which contains the following required
fields:

- **`searchEntryPoint`**: Contains the HTML and CSS to render the required search suggestions.
- **`groundingChunks`**: Returns the top 3 web sources used to ground the generated image

### Grounding with Google Search for Images (3.1 Flash)

> [!NOTE]
> **Note:** This feature is only available for the Gemini 3.1 Flash Image model.

Grounding with Google Search for images allows models to use web images retrieved via
Google Search as visual context for image generation. Image Search is a
new search type within the existing Grounding with Google Search tool,
functioning alongside standard [Web Search](https://ai.google.dev/gemini-api/docs/image-generation#use-with-grounding).

To enable Image Search, configure the `googleSearch` tool in your API request
and specify `imageSearch` within the `searchTypes` object. Image Search can be
used independently or together with Web Search.

Note that Grounding with Google Search for images can't be used to search for people.

### Python

    from google import genai
    prompt = "A detailed painting of a Timareta butterfly resting on a flower"

    client = genai.Client()

    response = client.models.generate_content(
        model="gemini-3.1-flash-image-preview",
        contents=prompt,
        config=types.GenerateContentConfig(
            response_modalities=["IMAGE"],
            tools=[
                types.Tool(google_search=types.GoogleSearch(
                    search_types=types.SearchTypes(
                        web_search=types.WebSearch(),
                        image_search=types.ImageSearch()
                    )
                ))
            ]
        )
    )

    # Display grounding sources if available
    if response.candidates and response.candidates[0].grounding_metadata and response.candidates[0].grounding_metadata.search_entry_point:
        display(HTML(response.candidates[0].grounding_metadata.search_entry_point.rendered_content))

### JavaScript

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

    async function main() {

      const ai = new GoogleGenAI({});

      const prompt = "A detailed painting of a Timareta butterfly resting on a flower";

      const response = await ai.models.generateContent({
        model: "gemini-3.1-flash-image-preview",
        contents: prompt,
        config: {
          responseModalities: ["IMAGE"],
          tools: [
            {
              googleSearch: {
                searchTypes: {
                  webSearch: {},
                  imageSearch: {}
                }
              }
            }
          ]
        }
      });

      // Display grounding sources if available
      if (response.candidates && response.candidates[0].groundingMetadata && response.candidates[0].groundingMetadata.searchEntryPoint) {
          console.log(response.candidates[0].groundingMetadata.searchEntryPoint.renderedContent);
      }
    }

    main();

### Go

    package main

    import (
      "context"
      "fmt"
      "log"

      "google.golang.org/genai"
      pb "google.golang.org/genai/schema"
    )

    func main() {
      ctx := context.Background()
      client, err := genai.NewClient(ctx, nil)
      if err != nil {
        log.Fatal(err)
      }
      defer client.Close()

      model := client.GenerativeModel("gemini-3.1-flash-image-preview")
      model.Tools = []*pb.Tool{
        {
          GoogleSearch: &pb.GoogleSearch{
            SearchTypes: &pb.SearchTypes{
              WebSearch:   &pb.WebSearch{},
              ImageSearch: &pb.ImageSearch{},
            },
          },
        },
      }
      model.GenerationConfig = &pb.GenerationConfig{
        ResponseModalities: []pb.ResponseModality{genai.Image},
      }

      prompt := "A detailed painting of a Timareta butterfly resting on a flower"
      resp, err := model.GenerateContent(ctx, genai.Text(prompt))
      if err != nil {
        log.Fatal(err)
      }

      if resp.Candidates[0].GroundingMetadata != nil && resp.Candidates[0].GroundingMetadata.SearchEntryPoint != nil {
        fmt.Println(resp.Candidates[0].GroundingMetadata.SearchEntryPoint.RenderedContent)
      }
    }

### REST

    curl -s -X POST \
      "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent" \
      -H "x-goog-api-key: $GEMINI_API_KEY" \
      -H "Content-Type: application/json" \
      -d '{
        "contents": [{"parts": [{"text": "A detailed painting of a Timareta butterfly resting on a flower"}]}],
        "tools": [{"google_search": {"searchTypes": {"webSearch": {}, "imageSearch": {}}}}],
        "generationConfig": {
          "responseModalities": ["IMAGE"]
        }
      }'

**Display requirements**

When you use Image Search within Grounding with Google Search, you must comply
with the following conditions:

- **Source attribution**: You must provide a link to the webpage containing the source image (the "containing page," not the image file itself) in a manner that the user will recognize as a link.
- **Direct navigation**: If you also choose to display the source images, you must provide a direct, single-click path from the source images to its containing source webpage. Any other implementation that delays or abstracts the end user's access to the source webpage, including but not limited to any multi-click path or the use of an intermediate image viewer, is not permitted.

**Response**

For grounded responses using image search, the API provides clear attribution
and metadata to link its output to verified sources. Key fields in the
`groundingMetadata` object include:

- **`imageSearchQueries`**: The specific queries used by the model for visual context (image search).
- **`groundingChunks`**: Contains source information for retrieved results.
  For image sources, these will be returned as redirect URLs using a new image
  chunk type. This chunk includes:

  - **`uri`**: The web page URL for attribution (the landing page).
  - **`image_uri`**: The direct image URL.
- **`groundingSupports`**: Provides specific mappings that link the generated
  content to its relevant citation source in the chunks.

- **`searchEntryPoint`**: Includes the "Google Search" chip containing
  compliant HTML and CSS to render Search Suggestions.

### Generate images up to 4K resolution

Gemini 3 image models generate 1K images by default but can also output 2K,
4K, and 512 (0.5K) (Gemini 3.1 Flash Image only) images. To generate higher
resolution assets, specify the `image_size` in the `generation_config`.

You must use an uppercase 'K' (e.g. 1K, 2K, 4K). The `512` value does not use a 'K' suffix. Lowercase
parameters (e.g., 1k) will be rejected.

### Python

    from google import genai
    from google.genai import types

    prompt = "Da Vinci style anatomical sketch of a dissected Monarch butterfly. Detailed drawings of the head, wings, and legs on textured parchment with notes in English."
    aspect_ratio = "1:1" # "1:1","1:4","1:8","2:3","3:2","3:4","4:1","4:3","4:5","5:4","8:1","9:16","16:9","21:9"
    resolution = "1K" # "512", "1K", "2K", "4K"

    client = genai.Client()

    response = client.models.generate_content(
        model="gemini-3.1-flash-image-preview",
        contents=prompt,
        config=types.GenerateContentConfig(
            response_modalities=['TEXT', 'IMAGE'],
            image_config=types.ImageConfig(
                aspect_ratio=aspect_ratio,
                image_size=resolution
            ),
        )
    )

    for part in response.parts:
        if part.text is not None:
            print(part.text)
        elif image:= part.as_image():
            image.save("butterfly.png")

### Javascript

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

    async function main() {

      const ai = new GoogleGenAI({});

      const prompt =
          'Da Vinci style anatomical sketch of a dissected Monarch butterfly. Detailed drawings of the head, wings, and legs on textured parchment with notes in English.';
      const aspectRatio = '1:1';
      const resolution = '1K';

      const response = await ai.models.generateContent({
        model: 'gemini-3.1-flash-image-preview',
        contents: prompt,
        config: {
          responseModalities: ['TEXT', 'IMAGE'],
          imageConfig: {
            aspectRatio: aspectRatio,
            imageSize: resolution,
          },
        },
      });

      for (const part of response.candidates[0].content.parts) {
        if (part.text) {
          console.log(part.text);
        } else if (part.inlineData) {
          const imageData = part.inlineData.data;
          const buffer = Buffer.from(imageData, "base64");
          fs.writeFileSync("image.png", buffer);
          console.log("Image saved as image.png");
        }
      }

    }

    main();

### Go

    package main

    import (
        "context"
        "fmt"
        "log"
        "os"

        "google.golang.org/genai"
    )

    func main() {
        ctx := context.Background()
        client, err := genai.NewClient(ctx, nil)
        if err != nil {
            log.Fatal(err)
        }
        defer client.Close()

        model := client.GenerativeModel("gemini-3.1-flash-image-preview")
        model.GenerationConfig = &pb.GenerationConfig{
            ResponseModalities: []pb.ResponseModality{genai.Text, genai.Image},
            ImageConfig: &pb.ImageConfig{
                AspectRatio: "1:1",
                ImageSize:   "1K",
            },
        }

        prompt := "Da Vinci style anatomical sketch of a dissected Monarch butterfly. Detailed drawings of the head, wings, and legs on textured parchment with notes in English."
        resp, err := model.GenerateContent(ctx, genai.Text(prompt))
        if err != nil {
            log.Fatal(err)
        }

        for _, part := range resp.Candidates[0].Content.Parts {
            if txt, ok := part.(genai.Text); ok {
                fmt.Printf("%s", string(txt))
            } else if img, ok := part.(genai.ImageData); ok {
                err := os.WriteFile("butterfly.png", img.Data, 0644)
                if err != nil {
                    log.Fatal(err)
                }
            }
        }
    }

### Java

    import com.google.genai.Client;
    import com.google.genai.types.GenerateContentConfig;
    import com.google.genai.types.GenerateContentResponse;
    import com.google.genai.types.GoogleSearch;
    import com.google.genai.types.ImageConfig;
    import com.google.genai.types.Part;
    import com.google.genai.types.Tool;

    import java.io.IOException;
    import java.nio.file.Files;
    import java.nio.file.Paths;

    public class HiRes {
        public static void main(String[] args) throws IOException {

          try (Client client = new Client()) {
            GenerateContentConfig config = GenerateContentConfig.builder()
                .responseModalities("TEXT", "IMAGE")
                .imageConfig(ImageConfig.builder()
                    .aspectRatio("16:9")
                    .imageSize("4K")
                    .build())
                .build();

            GenerateContentResponse response = client.models.generateContent(
                "gemini-3.1-flash-image-preview", """
                  Da Vinci style anatomical sketch of a dissected Monarch butterfly.
                  Detailed drawings of the head, wings, and legs on textured
                  parchment with notes in English.
                  """,
                config);

            for (Part part : response.parts()) {
              if (part.text().isPresent()) {
                System.out.println(part.text().get());
              } else if (part.inlineData().isPresent()) {
                var blob = part.inlineData().get();
                if (blob.data().isPresent()) {
                  Files.write(Paths.get("butterfly.png"), blob.data().get());
                }
              }
            }
          }
        }
    }

### REST

    curl -s -X POST \
      "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent" \
      -H "x-goog-api-key: $GEMINI_API_KEY" \
      -H "Content-Type: application/json" \
      -d '{
        "contents": [{"parts": [{"text": "Da Vinci style anatomical sketch of a dissected Monarch butterfly. Detailed drawings of the head, wings, and legs on textured parchment with notes in English."}]}],
        "tools": [{"google_search": {}}],
        "generationConfig": {
          "responseModalities": ["TEXT", "IMAGE"],
          "imageConfig": {"aspectRatio": "1:1", "imageSize": "1K"}
        }
      }'

The following is an example image generated from this prompt:
![AI-generated Da Vinci style anatomical sketch of a dissected Monarch butterfly.](https://ai.google.dev/static/gemini-api/docs/images/gemini3-4k-image.png) AI-generated Da Vinci style anatomical sketch of a dissected Monarch butterfly.

### Thinking Process

Gemini 3 image models are thinking models that use a reasoning
process ("Thinking") for complex prompts. This feature is enabled by default and
cannot be disabled in the API. To learn more about the thinking process, see
the [Gemini Thinking](https://ai.google.dev/gemini-api/docs/thinking) guide.

The model generates up to two interim images to test composition and logic. The
last image within Thinking is also the final rendered image.

You can check the thoughts that lead to the final image being produced.

### Python

    for part in response.parts:
        if part.thought:
            if part.text:
                print(part.text)
            elif image:= part.as_image():
                image.show()

### Javascript

    for (const part of response.candidates[0].content.parts) {
      if (part.thought) {
        if (part.text) {
          console.log(part.text);
        } else if (part.inlineData) {
          const imageData = part.inlineData.data;
          const buffer = Buffer.from(imageData, 'base64');
          fs.writeFileSync('image.png', buffer);
          console.log('Image saved as image.png');
        }
      }
    }

#### Controlling thinking levels

With Gemini 3.1 Flash Image, you can control the amount of thinking the model
uses to balance quality and latency. The default `thinkingLevel` is `minimal`,
and the supported levels are `minimal` and `high`. Setting the
`thinkingLevel` to `minimal` provides the lowest latency responses. Note that
minimal thinking does not mean the model uses no thinking at all.

You can add the `includeThoughts` boolean to determine whether the model's
generated thoughts are returned in the response, or remain hidden.

### Python

    from google import genai

    response = client.models.generate_content(
        model="gemini-3.1-flash-image-preview",
        contents="A futuristic city built inside a giant glass bottle floating in space",
        config=types.GenerateContentConfig(
            response_modalities=["IMAGE"],
            thinking_config=types.ThinkingConfig(
                thinking_level="High",
                include_thoughts=True
            ),
        )
    )

    for part in response.parts:
        if part.thought: # Skip outputting thoughts
          continue
        if part.text:
          display(Markdown(part.text))
        elif image:= part.as_image():
          image.show()

### JavaScript

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

    async function main() {

      const ai = new GoogleGenAI({});

      const response = await ai.models.generateContent({
        model: "gemini-3.1-flash-image-preview",
        contents: "A futuristic city built inside a giant glass bottle floating in space",
        config: {
          responseModalities: ["IMAGE"],
          thinkingConfig: {
            thinkingLevel: "High",
            includeThoughts: true
          },
        },
      });

      for (const part of response.candidates[0].content.parts) {
        if (part.thought) { // Skip outputting thoughts
          continue;
        }
        if (part.text) {
          console.log(part.text);
        } else if (part.inlineData) {
          const imageData = part.inlineData.data;
          const buffer = Buffer.from(imageData, "base64");
          fs.writeFileSync("image.png", buffer);
          console.log("Image saved as image.png");
        }
      }
    }
    main();

### Go

    package main

    import (
        "context"
        "fmt"
        "log"
        "os"

        "google.golang.org/genai"
        pb "google.golang.org/genai/schema"
    )

    func main() {
        ctx := context.Background()
        client, err := genai.NewClient(ctx, nil)
        if err != nil {
            log.Fatal(err)
        }
        defer client.Close()

        model := client.GenerativeModel("gemini-3.1-flash-image-preview")
        model.GenerationConfig = &pb.GenerationConfig{
            ResponseModalities: []pb.ResponseModality{genai.Image},
            ThinkingConfig: &pb.ThinkingConfig{
                ThinkingLevel:   "High",
                IncludeThoughts: true,
            },
        }

        prompt := "A futuristic city built inside a giant glass bottle floating in space"
        resp, err := model.GenerateContent(ctx, genai.Text(prompt))
        if err != nil {
            log.Fatal(err)
        }

        for _, part := range resp.Candidates[0].Content.Parts {
            if part.Thought { // Skip outputting thoughts
                continue
            }
            if txt, ok := part.(genai.Text); ok {
                fmt.Printf("%s", string(txt))
            } else if img, ok := part.(genai.ImageData); ok {
                err := os.WriteFile("image.png", img.Data, 0644)
                if err != nil {
                    log.Fatal(err)
                }
            }
        }
    }

### REST

    curl -s -X POST \
      "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent" \
      -H "x-goog-api-key: $GEMINI_API_KEY" \
      -H "Content-Type: application/json" \
      -d '{
        "contents": [{"parts": [{"text": "A futuristic city built inside a giant glass bottle floating in space"}]}],
        "generationConfig": {
          "responseModalities": ["IMAGE"],
          "thinkingConfig": {
            "thinkingLevel": "High",
            "includeThoughts": true
          }
        }
      }'

Note that thinking tokens are billed regardless of whether `includeThoughts` is
set to `true` or `false`, as the [thinking process](https://ai.google.dev/gemini-api/docs/image-generation#thinking-process) always
happens by default whether you view the process or not.

#### Thought Signatures

Thought signatures are encrypted representations of the
model's internal thought process and are used to preserve reasoning context
across multi-turn interactions. All responses include a `thought_signature`
field. As a general rule, if you receive a thought signature in a model
response, you should pass it back exactly as received when sending the
conversation history in the next turn. Failure to circulate thought signatures
may cause the response to fail. Check the [thought signature](https://ai.google.dev/gemini-api/docs/thought-signatures)
documentation for more explanations of signatures overall.

> [!NOTE]
> **Note:** If you use the official [Google Gen AI SDKs](https://ai.google.dev/gemini-api/docs/libraries) and use the chat feature (or append the full model response object directly to history), **thought signatures are handled automatically**. You do not need to manually extract or manage them, or change your code.

Here is how thought signatures work:

- All `inline_data` parts with image `mimetype` which are part of the response should have signature.
- If there are some text parts at the beginning (before any image) right after the thoughts, the first text part should also have a signature.
- If `inline_data` parts with image `mimetype` are part of thoughts, they won't have signatures.

The following code shows an example of where thought signatures are included:

    [
      {
        "inline_data": {
          "data": "<base64_image_data_0>",
          "mime_type": "image/png"
        },
        "thought": true // Thoughts don't have signatures
      },
      {
        "inline_data": {
          "data": "<base64_image_data_1>",
          "mime_type": "image/png"
        },
        "thought": true // Thoughts don't have signatures
      },
      {
        "inline_data": {
          "data": "<base64_image_data_2>",
          "mime_type": "image/png"
        },
        "thought": true // Thoughts don't have signatures
      },
      {
        "text": "Here is a step-by-step guide to baking macarons, presented in three separate images.\n\n### Step 1: Piping the Batter\n\nThe first step after making your macaron batter is to pipe it onto a baking sheet. This requires a steady hand to create uniform circles.\n\n",
        "thought_signature": "<Signature_A>" // The first non-thought part always has a signature
      },
      {
        "inline_data": {
          "data": "<base64_image_data_3>",
          "mime_type": "image/png"
        },
        "thought_signature": "<Signature_B>" // All image parts have a signatures
      },
      {
        "text": "\n\n### Step 2: Baking and Developing Feet\n\nOnce piped, the macarons are baked in the oven. A key sign of a successful bake is the development of \"feet\"---the ruffled edge at the base of each macaron shell.\n\n"
        // Follow-up text parts don't have signatures
      },
      {
        "inline_data": {
          "data": "<base64_image_data_4>",
          "mime_type": "image/png"
        },
        "thought_signature": "<Signature_C>" // All image parts have a signatures
      },
      {
        "text": "\n\n### Step 3: Assembling the Macaron\n\nThe final step is to pair the cooled macaron shells by size and sandwich them together with your desired filling, creating the classic macaron dessert.\n\n"
      },
      {
        "inline_data": {
          "data": "<base64_image_data_5>",
          "mime_type": "image/png"
        },
        "thought_signature": "<Signature_D>" // All image parts have a signatures
      }
    ]

## Other image generation modes

Gemini supports other image interaction modes based on prompt structure and
context, including:

- **Text to image(s) and text (interleaved):** Outputs images with related text.
  - Example prompt: "Generate an illustrated recipe for a paella."
- **Image(s) and text to image(s) and text (interleaved)** : Uses input images and text to create new related images and text.
  - Example prompt: (With an image of a furnished room) "What other color sofas would work in my space? can you update the image?"

## Generate images in batch

If you need to generate a lot of images, you can use the
[Batch API](https://ai.google.dev/gemini-api/docs/batch-api). You get higher
[rate limits](https://ai.google.dev/gemini-api/docs/rate-limits) in exchange for a turnaround of up
to 24 hours.

Check the [Batch API image generation documentation](https://ai.google.dev/gemini-api/docs/batch-api#image-generation) and the [cookbook](https://colab.research.google.com/github/google-gemini/cookbook/blob/main/quickstarts/Batch_mode.ipynb)
for Batch API image examples and code.

## Prompting guide and strategies

Mastering image generation starts with one fundamental principle:
> **Describe the scene, don't just list keywords.**
> The model's core strength is its deep language understanding. A narrative,
> descriptive paragraph will almost always produce a better, more coherent image
> than a list of disconnected words.

### Prompts for generating images

The following strategies will help you create effective prompts to
generate exactly the images you're looking for.

#### Photography

For realistic images, use photography terms. Mention camera angles, lens types,
lighting, and fine details to guide the model toward a realistic result.

| **Prompt** | **Generated output** |
|---|---|
| A photo of a close-up portrait of an elderly Japanese ceramicist with deep, sun-etched wrinkles and a warm, knowing smile. He is carefully inspecting a freshly glazed tea bowl. The setting is his rustic, sun-drenched workshop. The scene is illuminated by soft, golden hour light streaming through a window, highlighting the fine texture of the clay. Captured with an 85mm portrait lens, resulting in a soft, blurred background (bokeh). The overall mood is serene and masterful. Vertical portrait orientation. | ![Elderly Japanese ceramicist](https://ai.google.dev/static/gemini-api/docs/images/photorealistic_example.png) |

#### Stylized illustrations and stickers

To create stickers, icons, or assets, be explicit about the style and request a
white background.

> [!NOTE]
> **Note:** The model does not support generating a transparent background.

| **Prompt** | **Generated output** |
|---|---|
| A kawaii-style sticker of a happy red panda wearing a tiny bamboo hat. It's munching on a green bamboo leaf. The design features bold, clean outlines, simple cel-shading, and a vibrant color palette. The background must be white. | ![Kawaii red panda sticker](https://ai.google.dev/static/gemini-api/docs/images/red_panda_sticker.png) |

#### Accurate text in images

Gemini excels at rendering text. Be clear about the text, the font style
(descriptively), and the overall design. Use Gemini 3 Pro Image Preview for
professional asset production.

| **Prompt** | **Generated output** |
|---|---|
| Create a modern, minimalist logo for a coffee shop called 'The Daily Grind'. The text should be in a clean, bold, sans-serif font. The color scheme is black and white. Put the logo in a circle. Use a coffee bean in a clever way. | ![Coffee shop logo](https://ai.google.dev/static/gemini-api/docs/images/logo_example.jpg) |

#### Product mockups and commercial photography

Perfect for creating clean, professional product shots for ecommerce,
advertising, or branding.

| **Prompt** | **Generated output** |
|---|---|
| A high-resolution, studio-lit product photograph of a minimalist ceramic coffee mug in matte black, presented on a polished concrete surface. The lighting is a three-point softbox setup designed to create soft, diffused highlights and eliminate harsh shadows. The camera angle is a slightly elevated 45-degree shot to showcase its clean lines. Ultra-realistic, with sharp focus on the steam rising from the coffee. Square image. | ![Ceramic coffee mug product shot](https://ai.google.dev/static/gemini-api/docs/images/product_mockup.png) |

#### Minimalist and negative space design

Excellent for creating backgrounds for websites, presentations, or marketing
materials where text will be overlaid.

| **Prompt** | **Generated output** |
|---|---|
| A minimalist composition featuring a single, delicate red maple leaf positioned in the bottom-right of the frame. The background is a vast, empty off-white canvas, creating significant negative space for text. Soft, diffused lighting from the top left. Square image. | ![Minimalist design with red maple leaf](https://ai.google.dev/static/gemini-api/docs/images/minimalist_design.png) |

#### Sequential art (Comic panel / Storyboard)

Builds on character consistency and scene description to create panels for
visual storytelling. For accuracy with text and storytelling ability, these
prompts work best with Gemini 3 Pro and Gemini 3.1 Flash Image Preview.

| **Prompt** | **Generated output** |
|---|---|
| **Input image:** ![Man in white glasses](https://ai.google.dev/static/gemini-api/docs/images/man_in_white_glasses.jpg) Input image **Prompt:** Make a 3 panel comic in a gritty, noir art style with high-contrast black and white inks. Put the character in a humurous scene. | ![Gritty noir comic panel](https://ai.google.dev/static/gemini-api/docs/images/comic_panel.jpg) |

#### Grounding with Google Search

Use Google Search to generate images based on recent or real-time information.
This is useful for news, weather, and other time-sensitive topics.

| **Prompt** | **Generated output** |
|---|---|
| Make a simple but stylish graphic of last night's Arsenal game in the Champion's League | ![Arsenal football score graphic](https://ai.google.dev/static/gemini-api/docs/images/football-score.jpg) |

### Prompts for editing images

These examples show how to provide images alongside your text prompts for
editing, composition, and style transfer.

#### Adding and removing elements

Provide an image and describe your change. The model will match the original
image's style, lighting, and perspective.

| **Prompt** | **Generated output** |
|---|---|
| **Input image:** :cat: Input image **Prompt:** Using the provided image of my cat, please add a small, knitted wizard hat on its head. Make it look like it's sitting comfortably and matches the soft lighting of the photo. | ![Cat with wizard hat](https://ai.google.dev/static/gemini-api/docs/images/cat_with_hat.png) |

#### Inpainting (Semantic masking)

Conversationally define a "mask" to edit a specific part of an image while
leaving the rest untouched.

| **Prompt** | **Generated output** |
|---|---|
| **Input image:** ![A wide shot of a modern, well-lit living room...](https://ai.google.dev/static/gemini-api/docs/images/living_room.png) Input image **Prompt:** Using the provided image of a living room, change only the blue sofa to be a vintage, brown leather chesterfield sofa. Keep the rest of the room, including the pillows on the sofa and the lighting, unchanged. | ![Living room with brown leather sofa](https://ai.google.dev/static/gemini-api/docs/images/living_room_edited.png) |

#### Style transfer

Provide an image and ask the model to recreate its content in a different
artistic style.

| **Prompt** | **Generated output** |
|---|---|
| **Input image:** ![A photorealistic, high-resolution photograph of a busy city street...](https://ai.google.dev/static/gemini-api/docs/images/city.png) Input image **Prompt:** Transform the provided photograph of a modern city street at night into the artistic style of Vincent van Gogh's 'Starry Night'. Preserve the original composition of buildings and cars, but render all elements with swirling, impasto brushstrokes and a dramatic palette of deep blues and bright yellows. | ![City street in Starry Night style](https://ai.google.dev/static/gemini-api/docs/images/city_style_transfer.png) |

#### Advanced composition: Combining multiple images

Provide multiple images as context to create a new, composite scene. This is
perfect for product mockups or creative collages.

| **Prompt** | **Generated output** |
|---|---|
| **Input images:** :dress: Input 1: Dress ![Full-body shot of a woman with her hair in a bun...](https://ai.google.dev/static/gemini-api/docs/images/model.png) Input 2: Model **Prompt:** Create a professional e-commerce fashion photo. Take the blue floral dress from the first image and let the woman from the second image wear it. Generate a realistic, full-body shot of the woman wearing the dress, with the lighting and shadows adjusted to match the outdoor environment. | ![Fashion e-commerce shot](https://ai.google.dev/static/gemini-api/docs/images/fashion_ecommerce_shot.png) |

#### High-fidelity detail preservation

To ensure critical details (like a face or logo) are preserved during an edit,
describe them in great detail along with your edit request.

| **Prompt** | **Generated output** |
|---|---|
| **Input images:** :woman: Input 1: Woman ![A simple, modern logo with the letters 'G' and 'A'...](https://ai.google.dev/static/gemini-api/docs/images/logo.png) Input 2: Logo **Prompt:** Take the first image of the woman with brown hair, blue eyes, and a neutral expression. Add the logo from the second image onto her black t-shirt. Ensure the woman's face and features remain completely unchanged. The logo should look like it's naturally printed on the fabric, following the folds of the shirt. | ![Woman with logo on t-shirt](https://ai.google.dev/static/gemini-api/docs/images/woman_with_logo.png) |

#### Bring something to life

Upload a rough sketch or drawing and ask the model to refine it into a finished image.

| **Prompt** | **Generated output** |
|---|---|
| **Input image:** ![Sketch of a car](https://ai.google.dev/static/gemini-api/docs/images/car-sketch.jpg) Rough sketch of a car **Prompt:** Turn this rough pencil sketch of a futuristic car into a polished photo of the finished concept car in a showroom. Keep the sleek lines and low profile from the sketch but add metallic blue paint and neon rim lighting. | ![Polished photo of concept car](https://ai.google.dev/static/gemini-api/docs/images/car-photo.jpg) |

#### Character consistency: 360 view

You can generate 360-degree views of a character by iteratively prompting for
different angles. For best results, include previously generated images in
subsequent prompts to maintain consistency. For complex poses, include a
reference image of the desired pose.

| **Prompt** | **Generated output** |
|---|---|
| **Input image:** ![Original input of a man in white glasses](https://ai.google.dev/static/gemini-api/docs/images/man_in_white_glasses.jpg) Original image **Prompt:** A studio portrait of this man against white, in profile looking right | ![Output of a man in white glasses looking right](https://ai.google.dev/static/gemini-api/docs/images/man_in_white_glasses_looking_right.jpg) Man in white glasses looking right ![Output of a man in white glasses looking forward](https://ai.google.dev/static/gemini-api/docs/images/man_in_white_glasses_looking_forward.jpg) Man in white glasses looking forward |

### Best Practices

To elevate your results from good to great, incorporate these professional
strategies into your workflow.

- **Be Hyper-Specific:** The more detail you provide, the more control you have. Instead of "fantasy armor," describe it: "ornate elven plate armor, etched with silver leaf patterns, with a high collar and pauldrons shaped like falcon wings."
- **Provide Context and Intent:** Explain the *purpose* of the image. The model's understanding of context will influence the final output. For example, "Create a logo for a high-end, minimalist skincare brand" will yield better results than just "Create a logo."
- **Iterate and Refine:** Don't expect a perfect image on the first try. Use the conversational nature of the model to make small changes. Follow up with prompts like, "That's great, but can you make the lighting a bit warmer?" or "Keep everything the same, but change the character's expression to be more serious."
- **Use Step-by-Step Instructions:** For complex scenes with many elements, break your prompt into steps. "First, create a background of a serene, misty forest at dawn. Then, in the foreground, add a moss-covered ancient stone altar. Finally, place a single, glowing sword on top of the altar."
- **Use "Semantic Negative Prompts":** Instead of saying "no cars," describe the desired scene positively: "an empty, deserted street with no signs of traffic."
- **Control the Camera:** Use photographic and cinematic language to control the composition. Terms like `wide-angle shot`, `macro shot`, `low-angle
  perspective`.

## Limitations

- For best performance, use the following languages: EN, ar-EG, de-DE, es-MX, fr-FR, hi-IN, id-ID, it-IT, ja-JP, ko-KR, pt-BR, ru-RU, ua-UA, vi-VN, zh-CN.
- Image generation does not support audio or video inputs.
- The model won't always follow the exact number of image outputs that the user explicitly asks for.
- `gemini-2.5-flash-image` works best with up to 3 images as input, while `gemini-3-pro-image-preview` supports 5 images with high fidelity, and up to 14 images in total. `gemini-3.1-flash-image-preview` supports character resemblance of up to 4 characters and the fidelity of up to 10 objects in a single workflow.
- When generating text for an image, Gemini works best if you first generate the text and then ask for an image with the text.
- `gemini-3.1-flash-image-preview` Grounding with Google Search does not support using real-world images of people from web search at this time.
- All generated images include a [SynthID watermark](https://ai.google.dev/responsible/docs/safeguards/synthid).

## Optional configurations

You can optionally configure the response modalities and aspect ratio of the
model's output in the `config` field of `generate_content` calls.

### Output types

The model defaults to returning text and image responses
(i.e. `response_modalities=['Text', 'Image']`).
You can configure the response to return only images without text using
`response_modalities=['Image']`.

### Python

    response = client.models.generate_content(
        model="gemini-3.1-flash-image-preview",
        contents=[prompt],
        config=types.GenerateContentConfig(
            response_modalities=['Image']
        )
    )

### JavaScript

    const response = await ai.models.generateContent({
        model: "gemini-3.1-flash-image-preview",
        contents: prompt,
        config: {
            responseModalities: ['Image']
        }
      });

### Go

    result, _ := client.Models.GenerateContent(
        ctx,
        "gemini-3.1-flash-image-preview",
        genai.Text("Create a picture of a nano banana dish in a " +
                    " fancy restaurant with a Gemini theme"),
        &genai.GenerateContentConfig{
            ResponseModalities: "Image",
        },
      )

### Java

    response = client.models.generateContent(
        "gemini-3.1-flash-image-preview",
        prompt,
        GenerateContentConfig.builder()
            .responseModalities("IMAGE")
            .build());

### REST

    curl -s -X POST \
      "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent" \
      -H "x-goog-api-key: $GEMINI_API_KEY" \
      -H "Content-Type: application/json" \
      -d '{
        "contents": [{
          "parts": [
            {"text": "Create a picture of a nano banana dish in a fancy restaurant with a Gemini theme"}
          ]
        }],
        "generationConfig": {
          "responseModalities": ["Image"]
        }
      }'

### Aspect ratios and image size

The model defaults to matching the output image size to that of your input
image, or otherwise generates 1:1 squares.
You can control the aspect ratio of the output image using the `aspect_ratio`
field under `image_config` in the response request, shown here:

### Python

    # For gemini-2.5-flash-image
    response = client.models.generate_content(
        model="gemini-2.5-flash-image",
        contents=[prompt],
        config=types.GenerateContentConfig(
            image_config=types.ImageConfig(
                aspect_ratio="16:9",
            )
        )
    )

    # For gemini-3.1-flash-image-preview and gemini-3-pro-image-preview
    response = client.models.generate_content(
        model="gemini-3.1-flash-image-preview",
        contents=[prompt],
        config=types.GenerateContentConfig(
            image_config=types.ImageConfig(
                aspect_ratio="16:9",
                image_size="2K",
            )
        )
    )

### JavaScript

    // For gemini-2.5-flash-image
    const response = await ai.models.generateContent({
        model: "gemini-2.5-flash-image",
        contents: prompt,
        config: {
          imageConfig: {
            aspectRatio: "16:9",
          },
        }
      });

    // For gemini-3.1-flash-image-preview and gemini-3-pro-image-preview
    const response_gemini3 = await ai.models.generateContent({
        model: "gemini-3.1-flash-image-preview",
        contents: prompt,
        config: {
          imageConfig: {
            aspectRatio: "16:9",
            imageSize: "2K",
          },
        }
      });

### Go

    // For gemini-2.5-flash-image
    result, _ := client.Models.GenerateContent(
        ctx,
        "gemini-2.5-flash-image",
        genai.Text("Create a picture of a nano banana dish in a " +
                    " fancy restaurant with a Gemini theme"),
        &genai.GenerateContentConfig{
            ImageConfig: &genai.ImageConfig{
              AspectRatio: "16:9",
            },
        }
      )

    // For gemini-3.1-flash-image-preview and gemini-3-pro-image-preview
    result_gemini3, _ := client.Models.GenerateContent(
        ctx,
        "gemini-3.1-flash-image-preview",
        genai.Text("Create a picture of a nano banana dish in a " +
                    " fancy restaurant with a Gemini theme"),
        &genai.GenerateContentConfig{
            ImageConfig: &genai.ImageConfig{
              AspectRatio: "16:9",
              ImageSize: "2K",
            },
        }
      )

### Java

    // For gemini-2.5-flash-image
    response = client.models.generateContent(
        "gemini-2.5-flash-image",
        prompt,
        GenerateContentConfig.builder()
            .imageConfig(ImageConfig.builder()
                .aspectRatio("16:9")
                .build())
            .build());

    // For gemini-3.1-flash-image-preview and gemini-3-pro-image-preview
    response_gemini3 = client.models.generateContent(
        "gemini-3.1-flash-image-preview",
        prompt,
        GenerateContentConfig.builder()
            .imageConfig(ImageConfig.builder()
                .aspectRatio("16:9")
                .imageSize("2K")
                .build())
            .build());

### REST

    # For gemini-2.5-flash-image
    curl -s -X POST \
      "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-image:generateContent" \
      -H "x-goog-api-key: $GEMINI_API_KEY" \
      -H 'Content-Type: application/json' \
      -d '{
        "contents": [{
          "parts": [
            {"text": "Create a picture of a nano banana dish in a fancy restaurant with a Gemini theme"}
          ]
        }],
        "generationConfig": {
          "imageConfig": {
            "aspectRatio": "16:9"
          }
        }
      }'

    # For gemini-3-pro-image-preview
    curl -s -X POST \
      "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent" \
      -H "x-goog-api-key: $GEMINI_API_KEY" \
      -H 'Content-Type: application/json' \
      -d '{
        "contents": [{
          "parts": [
            {"text": "Create a picture of a nano banana dish in a fancy restaurant with a Gemini theme"}
          ]
        }],
        "generationConfig": {
          "imageConfig": {
            "aspectRatio": "16:9",
            "imageSize": "2K"
          }
        }
      }'

The different ratios available and the size of the image generated are listed in
the following tables:

### 3.1 Flash Image Preview

| Aspect ratio | 512 resolution | 0.5K tokens | 1K resolution | 1K tokens | 2K resolution | 2K tokens | 4K resolution | 4K tokens |
|---|---|---|---|---|---|---|---|---|
| **1:1** | 512x512 | 747 | 1024x1024 | 1120 | 2048x2048 | 1680 | 4096x4096 | 2520 |
| **1:4** | 256x1024 | 747 | 512x2048 | 1120 | 1024x4096 | 1680 | 2048x8192 | 2520 |
| **1:8** | 192x1536 | 747 | 384x3072 | 1120 | 768x6144 | 1680 | 1536x12288 | 2520 |
| **2:3** | 424x632 | 747 | 848x1264 | 1120 | 1696x2528 | 1680 | 3392x5056 | 2520 |
| **3:2** | 632x424 | 747 | 1264x848 | 1120 | 2528x1696 | 1680 | 5056x3392 | 2520 |
| **3:4** | 448x600 | 747 | 896x1200 | 1120 | 1792x2400 | 1680 | 3584x4800 | 2520 |
| **4:1** | 1024x256 | 747 | 2048x512 | 1120 | 4096x1024 | 1680 | 8192x2048 | 2520 |
| **4:3** | 600x448 | 747 | 1200x896 | 1120 | 2400x1792 | 1680 | 4800x3584 | 2520 |
| **4:5** | 464x576 | 747 | 928x1152 | 1120 | 1856x2304 | 1680 | 3712x4608 | 2520 |
| **5:4** | 576x464 | 747 | 1152x928 | 1120 | 2304x1856 | 1680 | 4608x3712 | 2520 |
| **8:1** | 1536x192 | 747 | 3072x384 | 1120 | 6144x768 | 1680 | 12288x1536 | 2520 |
| **9:16** | 384x688 | 747 | 768x1376 | 1120 | 1536x2752 | 1680 | 3072x5504 | 2520 |
| **16:9** | 688x384 | 747 | 1376x768 | 1120 | 2752x1536 | 1680 | 5504x3072 | 2520 |
| **21:9** | 792x168 | 747 | 1584x672 | 1120 | 3168x1344 | 1680 | 6336x2688 | 2520 |

### 3 Pro Image Preview

| Aspect ratio | 1K resolution | 1K tokens | 2K resolution | 2K tokens | 4K resolution | 4K tokens |
|---|---|---|---|---|---|---|
| **1:1** | 1024x1024 | 1120 | 2048x2048 | 1120 | 4096x4096 | 2000 |
| **2:3** | 848x1264 | 1120 | 1696x2528 | 1120 | 3392x5056 | 2000 |
| **3:2** | 1264x848 | 1120 | 2528x1696 | 1120 | 5056x3392 | 2000 |
| **3:4** | 896x1200 | 1120 | 1792x2400 | 1120 | 3584x4800 | 2000 |
| **4:3** | 1200x896 | 1120 | 2400x1792 | 1120 | 4800x3584 | 2000 |
| **4:5** | 928x1152 | 1120 | 1856x2304 | 1120 | 3712x4608 | 2000 |
| **5:4** | 1152x928 | 1120 | 2304x1856 | 1120 | 4608x3712 | 2000 |
| **9:16** | 768x1376 | 1120 | 1536x2752 | 1120 | 3072x5504 | 2000 |
| **16:9** | 1376x768 | 1120 | 2752x1536 | 1120 | 5504x3072 | 2000 |
| **21:9** | 1584x672 | 1120 | 3168x1344 | 1120 | 6336x2688 | 2000 |

### Gemini 2.5 Flash Image

| Aspect ratio | Resolution | Tokens |
|---|---|---|
| 1:1 | 1024x1024 | 1290 |
| 2:3 | 832x1248 | 1290 |
| 3:2 | 1248x832 | 1290 |
| 3:4 | 864x1184 | 1290 |
| 4:3 | 1184x864 | 1290 |
| 4:5 | 896x1152 | 1290 |
| 5:4 | 1152x896 | 1290 |
| 9:16 | 768x1344 | 1290 |
| 16:9 | 1344x768 | 1290 |
| 21:9 | 1536x672 | 1290 |

## Model selection

Choose the model best suited for your specific use case.

- **Gemini 3.1 Flash Image Preview (Nano Banana 2 Preview)** should be your
  go-to image generation model, as the best all around performance and
  intelligence to cost and latency balance. Check the model [pricing](https://ai.google.dev/gemini-api/docs/pricing#gemini-3.1-flash-image-preview) and [capabilities](https://ai.google.dev/gemini-api/docs/models/gemini-3.1-flash-image-preview) page for more
  details.

- **Gemini 3 Pro Image Preview (Nano Banana Pro Preview)** is designed for
  professional asset production and complex instructions. This model features
  real-world grounding using Google Search, a default "Thinking" process that
  refines composition prior to generation, and can generate images of up to 4K
  resolutions. Check the model [pricing](https://ai.google.dev/gemini-api/docs/pricing#gemini-3-pro-image-preview) and [capabilities](https://ai.google.dev/gemini-api/docs/models/gemini-3-pro-image-preview) page for more
  details.

- **Gemini 2.5 Flash Image (Nano Banana)** is designed for speed and
  efficiency. This model is optimized for high-volume, low-latency tasks and
  generates images at 1024px resolution. Check the model [pricing](https://ai.google.dev/gemini-api/docs/pricing#gemini-2.5-flash-image) and
  [capabilities](https://ai.google.dev/gemini-api/docs/models/gemini-2.5-flash-image) page for more
  details.

### When to use Imagen

In addition to using Gemini's built-in image generation capabilities, you can
also access [Imagen](https://ai.google.dev/gemini-api/docs/imagen), our specialized image generation
model, through the Gemini API.

Imagen 4 should be your go-to model when starting to generate images
with Imagen. Choose Imagen 4 Ultra for advanced
use-cases or when you need the best image quality (note that can only generate
one image at a time).

## What's next

- Find more examples and code samples in the [cookbook guide](https://colab.research.google.com/github/google-gemini/cookbook/blob/main/quickstarts/Get_Started_Nano_Banana.ipynb).
- Check out the [Veo guide](https://ai.google.dev/gemini-api/docs/video) to learn how to generate videos with the Gemini API.
- To learn more about Gemini models, see [Gemini models](https://ai.google.dev/gemini-api/docs/models/gemini).
