Name: GPT-4.1 API: OpenAI Text and Vision Chat Model
Brand: magicapi
SKU: api-magicapi-complex-task-ai-api-openai-gpt-4-1
Availability: InStock
Author: API Market

Shashank Agarwal

# 💬 Text and Vision Chat API (OpenAI GPT-4.1)

## Overview
GPT-4.1 is a language model from OpenAI that reads text and images and answers in text. Its developer describes it as its flagship GPT model for complex tasks.

Send a `prompt` with an optional `system_prompt`, or a whole conversation as `messages`, and attach images with `image_input`. Each request returns the model's answer.

## Features
- **Prompt or conversation**: send a single `prompt`, or a multi-turn conversation as a JSON string in `messages` (then `prompt` and `system_prompt` are ignored).
- **Image input**: attach one or more image URLs in `image_input` and ask about them.
- **System prompt**: set a role, tone or output format with `system_prompt`.
- **Sampling control**: `temperature` (0 to 2), `top_p` (0 to 1), `frequency_penalty` and `presence_penalty` (-2 to 2).
- **Answer length**: `max_completion_tokens` caps the answer (default 4096).

## Use Cases
1. **Questions about images**: explain what a photo, chart or screenshot shows.
2. **Writing and editing**: drafts, rewrites and summaries that follow a system prompt.
3. **Assistants and chatbots**: multi-turn conversations sent as `messages`.
4. **Analysis tasks**: extract facts, compare options or reason through a problem described in the prompt.

## Visual Examples
### Example Input
System prompt: "You are a helpful assistant."

Prompt: "What is happening in this image?"

Image:

![A hand spreading butter on toast from a glue-stick style container labeled BUTTER STICK TYPE](https://blog.api.market/wp-content/uploads/2026/10/gpt-4-1-input-api-market.jpg)

Settings: `temperature` 1; all other fields at their defaults.

### Example Output
The joined `output` text:

> In the image, someone is spreading butter on a piece of toast using a container labeled "BUTTER STICK TYPE." The butter is packaged in a stick form, similar to a glue stick or lip balm, allowing the user to easily apply and spread the butter onto the bread by rubbing it directly onto the surface. This design is practical and helps control the amount of butter applied without making a mess.

This request used 789 input tokens and 80 output tokens.

## Input Parameters
| Field | Type | Required | Default | Allowed values | Description |
| --- | --- | --- | --- | --- | --- |
| `prompt` | string | No | none |  | The prompt to send to the model. Do not use if using messages. |
| `system_prompt` | string | No | none |  | System prompt to set the assistant's behavior |
| `messages` | string | No | `""` |  | A JSON string representing a list of messages, for example [{"role": "user", "content": "Hello, how are you?"}]. If provided, prompt and system_prompt are ignored. |
| `image_input` | array of URLs | No | `[]` |  | List of image URLs to send to the model |
| `temperature` | number | No | `1` | `0` to `2` | Sampling temperature between 0 and 2 |
| `max_completion_tokens` | integer | No | `4096` |  | Maximum number of completion tokens to generate |
| `top_p` | number | No | `1` | `0` to `1` | Nucleus sampling parameter: the model considers the tokens within the top_p probability mass (0.1 means only the tokens in the top 10% probability mass are considered) |
| `frequency_penalty` | number | No | `0` | `-2` to `2` | Frequency penalty: positive values penalize the repetition of tokens |
| `presence_penalty` | number | No | `0` | `-2` to `2` | Presence penalty: positive values penalize tokens that already appear in the text, making the model more likely to talk about new topics |

## Pricing
- **Pay As You Go (PAYG) pricing**: you only pay for what you use.
- **Price**: $0.001 per API unit.
- **1 API unit = 100 weighted tokens; an output token counts 1, an input token counts 0.25.**
- That is **$2.50 per million input tokens** and **$10 per million output tokens**. Each request is rounded up to a whole API unit.
- Token counts are the input and output token counts the model reports for each request; images add input tokens.
- **Example cost**: the example request used 789 input tokens and 80 output tokens. 789 x 0.25 + 80 = 277.25 weighted tokens, which rounds up to 3 API units: **$0.003**.
- **Failed or cancelled predictions are not billed.**
- **GET requests to check prediction status** are free and don't count toward API unit usage.
- **No monthly subscription fee.**

## API Documentation

### Base URL
All API requests go to: `https://prod.api.market/api/v1/magicapi/complex-task-ai-api-openai-gpt-4-1`

### Authentication
Send your api.market API key in the `x-api-market-key` header:
```
x-api-market-key: YOUR_API_MARKET_KEY
```

### Asynchronous Processing
**IMPORTANT**: This API is asynchronous. The flow is:
1. Send a POST request to `/predictions`. A successful request returns **201 Created** with a prediction `id` and a `status` of `starting` or `processing`.
2. Poll GET `/predictions/{id}` with that ID.
3. Keep polling while `status` is `starting` or `processing`.
4. Stop when `status` is `succeeded` (read `output`), `failed` or `canceled` (read `error`).

A polling interval of 1 second and an overall timeout of 600 seconds (10 minutes) work well. The example request finished in about 13 seconds.

### Endpoints

#### Create a Prediction
```bash
curl -X POST "https://prod.api.market/api/v1/magicapi/complex-task-ai-api-openai-gpt-4-1/predictions" \
  -H "Content-Type: application/json" \
  -H "x-api-market-key: YOUR_API_MARKET_KEY" \
  -d '{
    "version": "openai/gpt-4.1",
    "input": {
      "prompt": "What is happening in this image?",
      "image_input": [
        "https://blog.api.market/wp-content/uploads/2026/10/gpt-4-1-input-api-market.jpg"
      ],
      "temperature": 1,
      "system_prompt": "You are a helpful assistant."
    }
  }'
```

Response (201 Created):
```json
{
  "id": "7b2cpbpwrhrmr0d1c6ssjcdsjr",
  "version": "openai/gpt-4.1",
  "status": "starting",
  "created_at": "2026-10-22T03:42:20.034Z"
}
```

#### Check Prediction Status
```bash
curl -X GET "https://prod.api.market/api/v1/magicapi/complex-task-ai-api-openai-gpt-4-1/predictions/{id}" \
  -H "x-api-market-key: YOUR_API_MARKET_KEY"
```

Response when the prediction has finished (200 OK):
```json
{
  "id": "7b2cpbpwrhrmr0d1c6ssjcdsjr",
  "status": "succeeded",
  "output": [
    "",
    "In",
    " the",
    " image",
    ",",
    " someone",
    " is",
    " spreading",
    " butter",
    " on",
    " a",
    " piece"
  ],
  "error": null,
  "created_at": "2026-10-22T03:42:20.034Z",
  "started_at": "2026-10-22T03:42:20.534Z",
  "completed_at": "2026-10-22T03:42:33.380Z"
}
```

`output` is a list of text chunks. Join them in order to get the full answer. The example above shows only the first 12 of 81 chunks.

### Status Codes
- **201 Created**: prediction created (POST `/predictions`). This is the normal success response.
- **200 OK**: prediction details returned (GET `/predictions/{id}`).
- **400 Bad Request**: invalid input, for example a `temperature` above 2 or a `top_p` above 1.
- **401 Unauthorized**: missing or invalid API key.
- **404 Not Found**: no prediction with that ID.
- **429 Too Many Requests**: rate limit exceeded.
- **500 Internal Server Error** or **503 Service Unavailable**: temporary server problem; retry later.

## Rate Limits
All plans allow 10 requests per second. Requests above that return `429 Too Many Requests`; back off before retrying and honour any `Retry-After` header. Polling a prediction with GET is free but still counts toward the per-second limit, so poll every 1 to 5 seconds rather than in a tight loop.

## Error Handling
- Check the HTTP status code of every request and read the response body for the error message.
- A prediction can also end with `status` set to `failed`; the `error` field then explains why. Failed predictions are not billed.
- Retry 429, 500 and 503 responses with exponential backoff. Fix the input before retrying a 400.

## Implementation Guide
### Example Code

#### JavaScript
```javascript
const apiKey = "YOUR_API_MARKET_KEY";
const baseUrl = "https://prod.api.market/api/v1/magicapi/complex-task-ai-api-openai-gpt-4-1";

async function createAndWaitForPrediction(input) {
  const response = await fetch(`${baseUrl}/predictions`, {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "x-api-market-key": apiKey
    },
    body: JSON.stringify({ version: "openai/gpt-4.1", input })
  });

  if (response.status !== 201) {
    throw new Error(`API error: ${response.status} ${await response.text()}`);
  }

  const prediction = await response.json();
  return pollForResults(prediction.id);
}

const delay = (ms) => new Promise((resolve) => setTimeout(resolve, ms));

async function pollForResults(predictionId) {
  const maxTimeout = 600000;
  const pollInterval = 1000;
  const startTime = Date.now();

  while (Date.now() - startTime < maxTimeout) {
    const response = await fetch(`${baseUrl}/predictions/${predictionId}`, {
      headers: { "x-api-market-key": apiKey }
    });

    if (!response.ok) {
      throw new Error(`API error: ${response.status} ${await response.text()}`);
    }

    const prediction = await response.json();

    if (prediction.status === "succeeded") {
      return prediction.output;
    }

    if (prediction.status === "failed" || prediction.status === "canceled") {
      throw new Error(`Prediction ${prediction.status}: ${prediction.error || "Unknown error"}`);
    }

    await delay(pollInterval);
  }

  throw new Error("Prediction timed out after 10 minutes");
}

createAndWaitForPrediction({
    "prompt": "What is happening in this image?",
    "image_input": [
      "https://blog.api.market/wp-content/uploads/2026/10/gpt-4-1-input-api-market.jpg"
    ],
    "temperature": 1,
    "system_prompt": "You are a helpful assistant."
  }).then((output) => console.log(output.join("")));
```

#### Python
```python
import time
import requests

API_KEY = "YOUR_API_MARKET_KEY"
BASE_URL = "https://prod.api.market/api/v1/magicapi/complex-task-ai-api-openai-gpt-4-1"


def create_and_wait_for_prediction(input_data):
    headers = {"Content-Type": "application/json", "x-api-market-key": API_KEY}
    response = requests.post(
        f"{BASE_URL}/predictions",
        headers=headers,
        json={"version": "openai/gpt-4.1", "input": input_data},
    )

    if response.status_code != 201:
        raise Exception(f"API error: {response.status_code} {response.text}")

    return poll_for_results(response.json()["id"])


def poll_for_results(prediction_id):
    max_timeout = 600
    poll_interval = 1
    headers = {"x-api-market-key": API_KEY}
    start_time = time.time()

    while time.time() - start_time < max_timeout:
        response = requests.get(f"{BASE_URL}/predictions/{prediction_id}", headers=headers)

        if response.status_code != 200:
            raise Exception(f"API error: {response.status_code} {response.text}")

        prediction = response.json()

        if prediction["status"] == "succeeded":
            return prediction["output"]

        if prediction["status"] in ("failed", "canceled"):
            raise Exception(f"Prediction {prediction['status']}: {prediction.get('error') or 'Unknown error'}")

        time.sleep(poll_interval)

    raise Exception("Prediction timed out after 10 minutes")


print("".join(create_and_wait_for_prediction({
    "prompt": "What is happening in this image?",
    "image_input": [
        "https://blog.api.market/wp-content/uploads/2026/10/gpt-4-1-input-api-market.jpg"
    ],
    "temperature": 1,
    "system_prompt": "You are a helpful assistant."
})))
```

## FAQ
- **How do I get the full answer?**
  - `output` is a list of text chunks. Join them in order with no separator.
- **How do I send a multi-turn conversation?**
  - Pass the conversation as a JSON string in `messages`, for example `[{"role": "user", "content": "Hello, how are you?"}]`. When `messages` is set, `prompt` and `system_prompt` are ignored.
- **Can I send images?**
  - Yes. Put one or more image URLs in `image_input`. Images count as input tokens.
- **What does a request cost?**
  - $2.50 per million input tokens and $10 per million output tokens, rounded up to a whole API unit ($0.001) per request. The example request cost $0.003.
- **Is the 201 response from POST an error?**
  - No. 201 Created means the prediction was accepted. Use the returned `id` to poll GET `/predictions/{id}` until `status` is `succeeded`, `failed` or `canceled`.
- **What if my prediction fails?**
  - Failed predictions are not billed. Read the `error` field, adjust the input and try again.

## Contact Us
If you have any questions, reach out to us through the chat on the bottom right of the page. You can also email our support team at hello@magicapi.com.


Use OpenAI's flagship GPT-4.1 for complex text tasks and questions about images, with system prompts and multi-turn messages. Pay as you go, no monthly fee.