## Basic model info

- Model name: google/google gemini-3.1-flash-image
- Model description: via GCP
- Endpoint name: image-editing


## Model schema

The model schema is defined in the OpenAPI schema: [OpenAPI Schema](https://oapi.sunra.ai/main/google/gemini-3.1-flash-image/latest.json)

### Model input schema

The model input schema is:



```json
{
  "description": "Input model for image editing via Google Vertex AI.",
  "properties": {
    "prompt": {
      "description": "The prompt for the image",
      "title": "Prompt",
      "type": "string",
      "x-sr-order": 200
    },
    "aspect_ratio": {
      "default": "1:1",
      "description": "The aspect ratio of the generated image",
      "enum": [
        "21:9",
        "16:9",
        "3:2",
        "4:3",
        "5:4",
        "1:1",
        "4:5",
        "3:4",
        "2:3",
        "9:16",
        "4:1",
        "1:4",
        "8:1",
        "1:8"
      ],
      "title": "Aspect Ratio",
      "type": "string",
      "x-sr-order": 401
    },
    "enable_web_search": {
      "default": false,
      "description": "Enable web search for the image generation task",
      "title": "Enable Web Search",
      "type": "boolean",
      "x-sr-order": 404
    },
    "images": {
      "description": "The images to edit",
      "items": {
        "anyOf": [
          {
            "format": "uri",
            "maxLength": 2083,
            "minLength": 1,
            "type": "string"
          },
          {
            "type": "string"
          }
        ]
      },
      "maxItems": 10,
      "minItems": 1,
      "title": "Images",
      "type": "array",
      "x-sr-order": 301
    },
    "output_format": {
      "default": "png",
      "description": "The output format of the generated image",
      "enum": [
        "jpeg",
        "png",
        "webp"
      ],
      "title": "Output Format",
      "type": "string",
      "x-sr-order": 402
    },
    "resolution": {
      "default": "1K",
      "description": "The resolution of the generated image",
      "enum": [
        "0.5K",
        "1K",
        "2K",
        "4K"
      ],
      "title": "Resolution",
      "type": "string",
      "x-sr-order": 403
    },
    "thinking_level": {
      "anyOf": [
        {
          "enum": [
            "minimal",
            "high"
          ],
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "description": "When set, enables model thinking with the given level",
      "title": "Thinking Level",
      "x-sr-order": 405
    }
  },
  "required": [
    "prompt",
    "images"
  ],
  "title": "ImageEditingInput",
  "type": "object"
}
```



### Model output schema

The model output schema is:



```json
{
  "properties": {
    "images": {
      "items": {
        "properties": {
          "content_type": {
            "description": "The mime type of the file.",
            "title": "Content Type",
            "type": "string"
          },
          "file_name": {
            "description": "The name of the file. It will be auto-generated if not provided.",
            "title": "File Name",
            "type": "string"
          },
          "file_size": {
            "description": "The size of the file in bytes.",
            "title": "File Size",
            "type": "integer"
          },
          "url": {
            "description": "The URL where the file can be downloaded from.",
            "title": "Url",
            "type": "string"
          }
        },
        "required": [
          "content_type",
          "file_name",
          "file_size",
          "url"
        ],
        "title": "SunraFile",
        "type": "object"
      },
      "title": "Images",
      "type": "array"
    }
  },
  "required": [
    "images"
  ],
  "title": "ImagesOutput",
  "type": "object"
}
```



## Example inputs and outputs

Use the following example inputs and outputs to understand the model.

### Input example



```json
{
  "prompt": "",
  "aspect_ratio": "1:1",
  "enable_web_search": false,
  "images": [
  ],
  "output_format": "png",
  "resolution": "1K",
  "thinking_level": null
}
```



### Output example



```json
{
}
```



## Model code examples

### JavaScript



```javascript
import { sunra } from "@sunra/client";

const result = await sunra.subscribe("google/gemini-3.1-flash-image/image-editing", {
  input: {
  	prompt: '',
  	images: [],
  	aspect_ratio: '1:1',
  	output_format: 'png',
  	resolution: '1K',
  	enable_web_search: false,
  	thinking_level: null
  },
  logs: true,
  onQueueUpdate: (update) => {
    console.log(`Status Update: ${update.status}, Request ID: ${update.request_id}`);
  },
});
console.log(result.data);
console.log(result.requestId);
```



### Python



```python
import sunra_client

result = sunra_client.subscribe(
    "google/gemini-3.1-flash-image/image-editing",
    arguments={
        "prompt": "",
        "images": [],
        "aspect_ratio": "1:1",
        "output_format": "png",
        "resolution": "1K",
        "enable_web_search": False,
        "thinking_level": None
    },
    with_logs=True,
    on_enqueue=print,
    on_queue_update=print,
)
print(result)
```



### Java



```java
import ai.sunra.client.*;
import java.util.Map;
import com.google.gson.JsonObject;

var client = SunraClient.withEnvCredentials();

var response = client.subscribe(
    "google/gemini-3.1-flash-image/image-editing",
    SubscribeOptions.<JsonObject>builder()
        .input(Map.of(
            "prompt", "",
            "images", ,
            "aspect_ratio", "1:1",
            "output_format", "png",
            "resolution", "1K",
            "enable_web_search", false,
            "thinking_level", null))
        .resultType(JsonObject.class)
        .onQueueUpdate(update -> System.out.printf(
            "\nStatus Update: %s, Request ID: %s%n",
            update.getStatus(),
            update.getRequestId()
        ))
        .logs(true)
        .build()
);

System.out.println("Completed!");
System.out.println(response.getData());
  
```



### Kotlin



```kotlin
import ai.sunra.client.kt.*
import com.google.gson.JsonObject

val client = createSunraClient()

val response = client.subscribe<JsonObject>(
    endpointId = "google/gemini-3.1-flash-image/image-editing",
    input = mapOf(
            "prompt" to "",
            "images" to ,
            "aspect_ratio" to "1:1",
            "output_format" to "png",
            "resolution" to "1K",
            "enable_web_search" to false,
            "thinking_level" to null),
    options = ai.sunra.client.kt.SubscribeOptions(logs = true),
    onUpdate = { update ->
        println("\nStatus Update: ${update.status}, Request ID: ${update.requestId}")
    }
)

println("Completed!")
println(response.data)
  
```



### Curl



```bash

curl --request POST \
  --url https://api.sunra.ai/v1/queue/google/gemini-3.1-flash-image/image-editing \
  --header "Authorization: Key $SUNRA_KEY" \
  --header "Content-Type: application/json" \
  --data '{"prompt":"","images":[],"aspect_ratio":"1:1","output_format":"png","resolution":"1K","enable_web_search":false,"thinking_level":null}'
    
```



## Model readme

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