MODEL PROMPTS

imagen-4 Prompts — 6 curated examples

Examples for using imagen-4 through RunAPI from agent tools or API calls. Copy a prompt, then use it in Claude Code, Codex, Cursor, Windsurf, or your backend.

MODELS

imagen-4

Modality
Image
Provider
Google
Endpoint
Text To Image
View model details and pricing →
1. claude mcp add runapi -s user -- npx -y @runapi.ai/mcp
2. Restart Claude Code
3. Paste this prompt: Generate an image: "A fluffy white Persian cat with bright blue eyes sitting gracefully on a sunlit windowsill, soft morning light streaming through lace curtains creating gentle dappled shadows, warm golden tones, shallow depth of field with sharp focus on the cat's face, photorealistic, shot on medium format camera"
1. codex plugin install runapi-mcp@agents
2. Restart Codex
3. Paste this prompt: Generate an image: "A fluffy white Persian cat with bright blue eyes sitting gracefully on a sunlit windowsill, soft morning light streaming through lace curtains creating gentle dappled shadows, warm golden tones, shallow depth of field with sharp focus on the cat's face, photorealistic, shot on medium format camera"
1. npx @runapi.ai/mcp init cursor
2. Restart Cursor
3. Paste this prompt: Generate an image: "A fluffy white Persian cat with bright blue eyes sitting gracefully on a sunlit windowsill, soft morning light streaming through lace curtains creating gentle dappled shadows, warm golden tones, shallow depth of field with sharp focus on the cat's face, photorealistic, shot on medium format camera"
1. npx @runapi.ai/mcp init windsurf
2. Restart Windsurf
3. Paste this prompt: Generate an image: "A fluffy white Persian cat with bright blue eyes sitting gracefully on a sunlit windowsill, soft morning light streaming through lace curtains creating gentle dappled shadows, warm golden tones, shallow depth of field with sharp focus on the cat's face, photorealistic, shot on medium format camera"
curl -X POST https://runapi.ai/api/v1/imagen_4/text_to_image \
  -H "Authorization: Bearer $RUNAPI_KEY" \
  -H "Content-Type: application/json" \
  --data-binary @- <<'JSON'
{
  "model": "imagen-4",
  "prompt": "A fluffy white Persian cat with bright blue eyes sitting gracefully on a sunlit windowsill, soft morning light streaming through lace curtains creating gentle dappled shadows, warm golden tones, shallow depth of field with sharp focus on the cat's face, photorealistic, shot on medium format camera"
}
JSON
import { Imagen4Client } from "@runapi.ai/imagen-4";

const client = new Imagen4Client({
  apiKey: process.env.RUNAPI_API_KEY,
});

const result = await client.textToImage.run({
  "model": "imagen-4",
  "prompt": "A fluffy white Persian cat with bright blue eyes sitting gracefully on a sunlit windowsill, soft morning light streaming through lace curtains creating gentle dappled shadows, warm golden tones, shallow depth of field with sharp focus on the cat's face, photorealistic, shot on medium format camera"
});
console.log(result.id);
require "runapi/imagen_4"

client = RunApi::Imagen4::Client.new
result = client.text_to_image.run(
  model: "imagen-4",
  prompt: "A fluffy white Persian cat with bright blue eyes sitting gracefully on a sunlit windowsill, soft morning light streaming through lace curtains creating gentle dappled shadows, warm golden tones, shallow depth of field with sharp focus on the cat's face, photorealistic, shot on medium format camera"
)
puts result.id
package main

import (
  "context"
  "fmt"
  "log"
  "net/http"
  "os"
  "strings"
)

func main() {
  body := strings.NewReader("{\"model\":\"imagen-4\",\"prompt\":\"A fluffy white Persian cat with bright blue eyes sitting gracefully on a sunlit windowsill, soft morning light streaming through lace curtains creating gentle dappled shadows, warm golden tones, shallow depth of field with sharp focus on the cat's face, photorealistic, shot on medium format camera\"}")
  req, err := http.NewRequestWithContext(context.Background(), http.MethodPost, "https://runapi.ai/api/v1/imagen_4/text_to_image", body)
  if err != nil {
    log.Fatal(err)
  }

  req.Header.Set("Authorization", "Bearer "+os.Getenv("RUNAPI_API_KEY"))
  req.Header.Set("Content-Type", "application/json")

  resp, err := http.DefaultClient.Do(req)
  if err != nil {
    log.Fatal(err)
  }
  defer resp.Body.Close()

  fmt.Println(resp.Status)
}
imagen-4 /api/v1/imagen_4/text_to_image Get API Key
IM
Image
Portrait Photography imagen-4

A fluffy white Persian cat with bright blue eyes sitting gra

A fluffy white Persian cat with bright blue eyes sitting gracefully on a sunlit windowsill, soft morning light streaming through lace curtains creating gentle dappled shadows, warm golden tones, shallow depth of field with sharp focus on the cat's face, photorealistic, shot on medium format camera

View API Code
curl -X POST https://runapi.ai/api/v1/imagen_4/text_to_image \
  -H "Authorization: Bearer $RUNAPI_KEY" \
  -H "Content-Type: application/json" \
  --data-binary @- <<'JSON'
{
  "model": "imagen-4",
  "prompt": "A fluffy white Persian cat with bright blue eyes sitting gracefully on a sunlit windowsill, soft morning light streaming through lace curtains creating gentle dappled shadows, warm golden tones, shallow depth of field with sharp focus on the cat's face, photorealistic, shot on medium format camera"
}
JSON
FAQ

Using imagen-4 prompts

What is %{model}?

%{model} is available through RunAPI as part of the unified model catalog. These prompts show practical input patterns that agents and backend services can reuse.

How do I use these prompts?

Copy any prompt and paste it into Claude Code, Codex, Cursor, or Windsurf after installing the RunAPI MCP Server. Developers can also copy the API example and send the prompt directly.

Do these prompts cost money to browse?

Browsing and copying prompt examples is free. Generation requests only cost money when you call a RunAPI model with your API key.

Can I adapt the prompts for production?

Yes. Treat each prompt as a starting point, then add your brand rules, output dimensions, safety constraints, and application-specific context before using it in production.