Name: GPT-5.2 API: OpenAI Reasoning and Coding Model
Brand: magicapi
SKU: api-magicapi-coding-and-agentic-tasks-ai-api-openai-gpt-5-2
Availability: InStock
Author: API Market

Shashank Agarwal

# 🧠 Reasoning and Coding Chat API (OpenAI GPT-5.2)

## Overview
GPT-5.2 is a language model from OpenAI that reads text and images and answers in text. Its developer describes it as the best model for coding and agentic tasks across industries.

Send a `prompt` with an optional `system_prompt`, or a conversation as `messages`, and attach images with `image_input`. Choose how hard the model reasons with `reasoning_effort` and how long it answers with `verbosity`.

## Features
- **Reasoning effort**: `reasoning_effort` is `none`, `low` (default), `medium`, `high` or `xhigh`.
- **Verbosity**: `verbosity` is `low`, `medium` (default) or `high`, from short and to the point to long and comprehensive.
- **Prompt or conversation**: send a single `prompt`, or a list of message objects in `messages` (then `prompt` and `system_prompt` are ignored).
- **Image input**: attach one or more image URLs in `image_input`.
- **Answer length**: `max_completion_tokens` caps the completion. At high reasoning effort, raise it so reasoning does not use up the whole budget and leave an empty answer.

## Use Cases
1. **Coding**: write, explain and fix code, or plan changes across a codebase described in the prompt.
2. **Agentic workflows**: multi-step tasks driven by a conversation in `messages`.
3. **Math and proofs**: step-by-step explanations at `high` or `xhigh` reasoning effort.
4. **Questions about images**: explain diagrams, screenshots and figures sent in `image_input`.

## Visual Examples
### Example Input
Prompt: "Explain this paradox through a mathematical proof"

Image:

![Input image illustrating the Banach-Tarski paradox](https://blog.api.market/wp-content/uploads/2026/10/gpt-5-2-input-api-market.jpg)

Settings: `reasoning_effort` `medium`, `verbosity` `medium`, `messages` empty.

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

```text
The image is commonly used to illustrate the **Banach–Tarski paradox**:

> A solid 3‑dimensional ball $B\subset \mathbb{R}^3$ can be partitioned into finitely many disjoint pieces and, using only rotations and translations, those pieces can be reassembled into **two** balls each congruent to the original $B$.

This sounds impossible because it seems to “double volume,” but the resolution is: **the pieces are not Lebesgue measurable**, so “volume” is not defined for them in the usual sense.
```

The full answer continues with a six-step proof sketch: paradoxical decompositions, the free group on two generators, free groups inside the rotations of 3D space, the role of the Axiom of Choice, the extension from the sphere to the solid ball, and why the result does not contradict volume. This request used 705 input tokens and 2,217 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` | array of objects | No | `[]` |  | A list of message objects, 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 |
| `reasoning_effort` | string | No | `low` | `none`, `low`, `medium`, `high`, `xhigh` | Constrains effort on reasoning. Supported values are none, low, medium, high and xhigh. |
| `verbosity` | string | No | `medium` | `low`, `medium`, `high` | Constrains the verbosity of the response. Lower values give more concise answers, higher values more comprehensive ones. |
| `max_completion_tokens` | integer | No | none |  | Maximum number of completion tokens to generate. For higher reasoning efforts you may need to increase it to avoid empty responses (where all the tokens are used on reasoning). |

## Pricing
- **Pay As You Go (PAYG) pricing**: you only pay for what you use.
- **Price**: $0.0018 per API unit.
- **1 API unit = 100 weighted tokens; an output token counts 1, an input token counts 0.125.**
- That is **$2.25 per million input tokens** and **$18 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. Reasoning uses completion tokens, so higher `reasoning_effort` usually costs more.
- **Example cost**: the example request used 705 input tokens and 2,217 output tokens. 705 x 0.125 + 2,217 = 2,305.125 weighted tokens, which rounds up to 24 API units: **$0.0432**.
- **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/coding-and-agentic-tasks-ai-api-openai-gpt-5-2`

### 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 (medium reasoning effort) finished in about 34 seconds.

### Endpoints

#### Create a Prediction
```bash
curl -X POST "https://prod.api.market/api/v1/magicapi/coding-and-agentic-tasks-ai-api-openai-gpt-5-2/predictions" \
  -H "Content-Type: application/json" \
  -H "x-api-market-key: YOUR_API_MARKET_KEY" \
  -d '{
    "version": "openai/gpt-5.2",
    "input": {
      "prompt": "Explain this paradox through a mathematical proof",
      "messages": [],
      "verbosity": "medium",
      "image_input": [
        "https://blog.api.market/wp-content/uploads/2026/10/gpt-5-2-input-api-market.jpg"
      ],
      "reasoning_effort": "medium"
    }
  }'
```

Response (201 Created):
```json
{
  "id": "rpnvdbhq4srmt0d1c6t9918q64",
  "version": "openai/gpt-5.2",
  "status": "starting",
  "created_at": "2026-10-22T03:42:43.159Z"
}
```

#### Check Prediction Status
```bash
curl -X GET "https://prod.api.market/api/v1/magicapi/coding-and-agentic-tasks-ai-api-openai-gpt-5-2/predictions/{id}" \
  -H "x-api-market-key: YOUR_API_MARKET_KEY"
```

Response when the prediction has finished (200 OK):
```json
{
  "id": "rpnvdbhq4srmt0d1c6t9918q64",
  "status": "succeeded",
  "output": [
    "The",
    " image",
    " is",
    " commonly",
    " used",
    " to",
    " illustrate",
    " the",
    " **",
    "Ban",
    "ach",
    "–"
  ],
  "error": null,
  "created_at": "2026-10-22T03:42:43.159Z",
  "started_at": "2026-10-22T03:42:43.659Z",
  "completed_at": "2026-10-22T03:43:17.060Z"
}
```

`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 1,925 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 `reasoning_effort` or `verbosity` value that is not in the list.
- **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/coding-and-agentic-tasks-ai-api-openai-gpt-5-2";

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-5.2", 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": "Explain this paradox through a mathematical proof",
    "messages": [],
    "verbosity": "medium",
    "image_input": [
      "https://blog.api.market/wp-content/uploads/2026/10/gpt-5-2-input-api-market.jpg"
    ],
    "reasoning_effort": "medium"
  }).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/coding-and-agentic-tasks-ai-api-openai-gpt-5-2"


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-5.2", "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": "Explain this paradox through a mathematical proof",
    "messages": [],
    "verbosity": "medium",
    "image_input": [
        "https://blog.api.market/wp-content/uploads/2026/10/gpt-5-2-input-api-market.jpg"
    ],
    "reasoning_effort": "medium"
})))
```

## FAQ
- **How do I get the full answer?**
  - `output` is a list of text chunks. Join them in order with no separator.
- **Why did I get an empty answer?**
  - At higher `reasoning_effort` the model can spend all of `max_completion_tokens` on reasoning. Raise `max_completion_tokens` or lower the effort.
- **How do I send a multi-turn conversation?**
  - Pass a list of message objects in `messages`, for example `[{"role": "user", "content": "Hello, how are you?"}]`. When `messages` is set, `prompt` and `system_prompt` are ignored.
- **What does a request cost?**
  - $2.25 per million input tokens and $18 per million output tokens, rounded up to a whole API unit ($0.0018) per request. The example request cost $0.0432.
- **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 GPT-5.2 for coding, agentic and reasoning tasks with text and images, with five reasoning effort levels. Pay as you go, no monthly fee.