Examples for using gpt-4o-image through RunAPI from agent tools or API calls. Copy a prompt, then use it in Claude Code, Codex, Cursor, Windsurf, or your backend.
1. claude mcp add runapi -s user -- npx -y @runapi.ai/mcp
2. Restart Claude Code
3. Paste this prompt: Generate an image: "Overhead food photography of a freshly baked round sourdough boule on a rustic wooden cutting board. The bread has been sliced to reveal the open, airy crumb structure with large irregular holes. A bread knife with a dark wood handle rests beside it. Scattered flour dusts the board and surrounding dark slate countertop. A small ceramic bowl of salted butter sits in the upper right corner. Natural side lighting from a kitchen window creates long soft shadows. Warm tones, high detail on the crust texture showing deep caramelization and ear scoring patterns."
1. codex plugin install runapi-mcp@agents
2. Restart Codex
3. Paste this prompt: Generate an image: "Overhead food photography of a freshly baked round sourdough boule on a rustic wooden cutting board. The bread has been sliced to reveal the open, airy crumb structure with large irregular holes. A bread knife with a dark wood handle rests beside it. Scattered flour dusts the board and surrounding dark slate countertop. A small ceramic bowl of salted butter sits in the upper right corner. Natural side lighting from a kitchen window creates long soft shadows. Warm tones, high detail on the crust texture showing deep caramelization and ear scoring patterns."
1. npx @runapi.ai/mcp init cursor
2. Restart Cursor
3. Paste this prompt: Generate an image: "Overhead food photography of a freshly baked round sourdough boule on a rustic wooden cutting board. The bread has been sliced to reveal the open, airy crumb structure with large irregular holes. A bread knife with a dark wood handle rests beside it. Scattered flour dusts the board and surrounding dark slate countertop. A small ceramic bowl of salted butter sits in the upper right corner. Natural side lighting from a kitchen window creates long soft shadows. Warm tones, high detail on the crust texture showing deep caramelization and ear scoring patterns."
1. npx @runapi.ai/mcp init windsurf
2. Restart Windsurf
3. Paste this prompt: Generate an image: "Overhead food photography of a freshly baked round sourdough boule on a rustic wooden cutting board. The bread has been sliced to reveal the open, airy crumb structure with large irregular holes. A bread knife with a dark wood handle rests beside it. Scattered flour dusts the board and surrounding dark slate countertop. A small ceramic bowl of salted butter sits in the upper right corner. Natural side lighting from a kitchen window creates long soft shadows. Warm tones, high detail on the crust texture showing deep caramelization and ear scoring patterns."
curl -X POST https://runapi.ai/api/v1/gpt_4o_image/text_to_image \
-H "Authorization: Bearer $RUNAPI_KEY" \
-H "Content-Type: application/json" \
--data-binary @- <<'JSON'
{
"model": "gpt-4o-image",
"prompt": "Overhead food photography of a freshly baked round sourdough boule on a rustic wooden cutting board. The bread has been sliced to reveal the open, airy crumb structure with large irregular holes. A bread knife with a dark wood handle rests beside it. Scattered flour dusts the board and surrounding dark slate countertop. A small ceramic bowl of salted butter sits in the upper right corner. Natural side lighting from a kitchen window creates long soft shadows. Warm tones, high detail on the crust texture showing deep caramelization and ear scoring patterns."
}
JSON
import { Gpt4oImageClient } from "@runapi.ai/gpt-4o-image";
const client = new Gpt4oImageClient({
apiKey: process.env.RUNAPI_API_KEY,
});
const result = await client.textToImage.run({
"model": "gpt-4o-image",
"prompt": "Overhead food photography of a freshly baked round sourdough boule on a rustic wooden cutting board. The bread has been sliced to reveal the open, airy crumb structure with large irregular holes. A bread knife with a dark wood handle rests beside it. Scattered flour dusts the board and surrounding dark slate countertop. A small ceramic bowl of salted butter sits in the upper right corner. Natural side lighting from a kitchen window creates long soft shadows. Warm tones, high detail on the crust texture showing deep caramelization and ear scoring patterns."
});
console.log(result.id);
require "runapi/gpt_4o_image"
client = RunApi::Gpt4oImage::Client.new
result = client.text_to_image.run(
model: "gpt-4o-image",
prompt: "Overhead food photography of a freshly baked round sourdough boule on a rustic wooden cutting board. The bread has been sliced to reveal the open, airy crumb structure with large irregular holes. A bread knife with a dark wood handle rests beside it. Scattered flour dusts the board and surrounding dark slate countertop. A small ceramic bowl of salted butter sits in the upper right corner. Natural side lighting from a kitchen window creates long soft shadows. Warm tones, high detail on the crust texture showing deep caramelization and ear scoring patterns."
)
puts result.id
package main
import (
"context"
"fmt"
"log"
"net/http"
"os"
"strings"
)
func main() {
body := strings.NewReader("{\"model\":\"gpt-4o-image\",\"prompt\":\"Overhead food photography of a freshly baked round sourdough boule on a rustic wooden cutting board. The bread has been sliced to reveal the open, airy crumb structure with large irregular holes. A bread knife with a dark wood handle rests beside it. Scattered flour dusts the board and surrounding dark slate countertop. A small ceramic bowl of salted butter sits in the upper right corner. Natural side lighting from a kitchen window creates long soft shadows. Warm tones, high detail on the crust texture showing deep caramelization and ear scoring patterns.\"}")
req, err := http.NewRequestWithContext(context.Background(), http.MethodPost, "https://runapi.ai/api/v1/gpt_4o_image/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)
}
gpt-4o-image/api/v1/gpt_4o_image/text_to_imageGet API Key
Overhead food photography of a freshly baked round sourdough boule on a rustic wooden cutting board. The bread has been sliced to reveal the open, airy crumb structure with large irregular holes. A bread knife with a dark wood handle rests beside it. Scattered flour dusts the board and surrounding dark slate countertop. A small ceramic bowl of salted butter sits in the upper right corner. Natural side lighting from a kitchen window creates long soft shadows. Warm tones, high detail on the crust texture showing deep caramelization and ear scoring patterns.
curl -X POST https://runapi.ai/api/v1/gpt_4o_image/text_to_image \
-H "Authorization: Bearer $RUNAPI_KEY" \
-H "Content-Type: application/json" \
--data-binary @- <<'JSON'
{
"model": "gpt-4o-image",
"prompt": "Overhead food photography of a freshly baked round sourdough boule on a rustic wooden cutting board. The bread has been sliced to reveal the open, airy crumb structure with large irregular holes. A bread knife with a dark wood handle rests beside it. Scattered flour dusts the board and surrounding dark slate countertop. A small ceramic bowl of salted butter sits in the upper right corner. Natural side lighting from a kitchen window creates long soft shadows. Warm tones, high detail on the crust texture showing deep caramelization and ear scoring patterns."
}
JSON
FAQ
Using gpt-4o-image 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.