# Recraft V4

> Generate a polished raster image from a text prompt — the standard tier of Recraft V4, with professional composition, lighting, and materials at an economical per-image price.

## Overview

- **Endpoint**: `https://queue.modelrunner.run/recraft/v4/text-to-image`
- **Model ID**: `recraft/v4/text-to-image`
- **Category**: text-to-image
- **Kind**: inference
- **Tags**: recraft, recraft-v4, text-to-image, image, design, photorealistic, illustration

## Pricing

- **Price**: $0.04 per output

## Request Lifecycle

This model runs on the ModelRunner **asynchronous queue API** — a single POST does not return the output. Every call requires an `Authorization: Key $MODEL_RUNNER_KEY` header. Run three steps:

1. **Submit** — `POST https://queue.modelrunner.run/recraft/v4/text-to-image` with a JSON body holding the input fields at the top level. The body may also include a reserved top-level `metadata` object — a flat string map (max 16 keys, key ≤64 / value ≤512 chars) stored on the request for your own tagging. It is never sent to the model; filter your request history with `GET https://queue.modelrunner.run/requests?metadata=<url-encoded JSON>` (exact key=value matches, AND-ed). The response carries request handles only (no output yet):

```json
{
  "status": "IN_QUEUE",
  "request_id": "<21-char id>",
  "status_url": "https://queue.modelrunner.run/recraft/v4/text-to-image/requests/<request_id>/status",
  "response_url": "https://queue.modelrunner.run/recraft/v4/text-to-image/requests/<request_id>",
  "cancel_url": "https://queue.modelrunner.run/recraft/v4/text-to-image/requests/<request_id>/cancel"
}
```

2. **Poll status** — `GET <status_url>` until `status` is `COMPLETED`. Possible values are `IN_QUEUE`, `IN_PROGRESS`, `COMPLETED`, `FAILED`, `CANCELLED`. A `FAILED` request responds with HTTP 400 and an `error` field.

3. **Read result** — `GET <response_url>`. Returns the finished request, including the generated `output`:

```json
{ "id": "<request_id>", "status": "COMPLETED", "output": ..., "input": ... }
```

The JavaScript and Python SDKs below perform steps 2–3 for you. In any language without an SDK (Swift, Go, Kotlin, etc.) you must implement the polling loop and the final result fetch yourself — see the cURL example for the full flow.

### Input Schema

- **`prompt`** (`string`, _required_):
  The text description of the image to generate (subject, scene, lighting, mood). Output is a polished raster image.

- **`image_size`** (`ImageSize | ImageSizeEnum`, _optional_):
  The size/aspect of the generated image. Pick a preset or supply an explicit width/height.
  - Default: `"square_hd"`
  - Options: `"square_hd"`, `"square"`, `"portrait_4_3"`, `"portrait_16_9"`, `"landscape_4_3"`, `"landscape_16_9"`

### Output Schema

_No `Output` schema properties are available._

## Default Example

**Input**

```json
{
  "prompt": "a minimalist matte-ceramic espresso set on a marble countertop, morning window light, soft shadows, editorial product photography composition",
  "image_size": "square_hd"
}
```

**Output**

```json
[
  "https://media.modelrunner.ai/EKgTNICAfVw51TsABdfPp.webp"
]
```
## Usage Examples

### cURL

The queue API is asynchronous: submit the request, poll `status_url` until it is `COMPLETED`, then read the result from `response_url`. Requires `jq`.

```bash
# 1. Submit the request (returns request handles, not the output)
SUBMIT=$(curl --silent --request POST \
  --url https://queue.modelrunner.run/recraft/v4/text-to-image \
  --header "Authorization: Key $MODEL_RUNNER_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "prompt": "a minimalist matte-ceramic espresso set on a marble countertop, morning window light, soft shadows, editorial product photography composition",
  "image_size": "square_hd"
}')

STATUS_URL=$(echo "$SUBMIT" | jq -r '.status_url')
RESPONSE_URL=$(echo "$SUBMIT" | jq -r '.response_url')

# 2. Poll until the request leaves the queue / in-progress state
while true; do
  STATUS=$(curl --silent --url "$STATUS_URL" \
    --header "Authorization: Key $MODEL_RUNNER_KEY" | jq -r '.status')
  echo "Status: $STATUS"
  case "$STATUS" in
    COMPLETED) break ;;
    FAILED|CANCELLED) echo "Request $STATUS"; exit 1 ;;
  esac
  sleep 1
done

# 3. Read the finished request, including the generated output
curl --silent --url "$RESPONSE_URL" \
  --header "Authorization: Key $MODEL_RUNNER_KEY"
```

### JavaScript

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

const result = await modelrunner.subscribe("recraft/v4/text-to-image", {
  input: {
  "prompt": "a minimalist matte-ceramic espresso set on a marble countertop, morning window light, soft shadows, editorial product photography composition",
  "image_size": "square_hd"
}
});

console.log(result.data);
```

### Python

```python
import asyncio
import modelrunner_ai

async def main():
    response = await modelrunner_ai.submit_async(
        "recraft/v4/text-to-image",
        arguments={
  "prompt": "a minimalist matte-ceramic espresso set on a marble countertop, morning window light, soft shadows, editorial product photography composition",
  "image_size": "square_hd"
}
    )
    result = await response.get()
    print(result["output"])

asyncio.run(main())
```

## Additional Resources

- [Playground](https://modelrunner.ai/models/recraft/v4/text-to-image)
- [OpenAPI Schema](https://modelrunner.ai/models/recraft/v4/text-to-image/openapi.json)
- [LLM Instructions](https://modelrunner.ai/models/recraft/v4/text-to-image/llms.txt)