Name: GPT-5 API: OpenAI Coding and Reasoning Model
Brand: magicapi
SKU: api-magicapi-coding-writing-reasoning-ai-openai-gpt-5
Availability: InStock
Author: API Market

Shashank Agarwal

# 🤖 Coding and Reasoning LLM API (OpenAI GPT-5)

## Overview
GPT-5 is a language model from OpenAI. Its developer describes it as excelling at coding, writing and reasoning.

Send either a `prompt` (with an optional `system_prompt`) or a full `messages` list, plus optional image URLs in `image_input`. `reasoning_effort` sets how much the model reasons before answering and `verbosity` sets how long the answer is. The model returns text as a list of chunks; join them for the full response.

## Features
- **Prompt or messages**: send a single `prompt`, or a `messages` list of `{"role", "content"}` objects for multi-turn input. When `messages` is set, `prompt` and `system_prompt` are ignored.
- **Image input**: pass one or more image URLs in `image_input`.
- **Reasoning effort**: `minimal` (default, fastest), `low`, `medium` or `high`.
- **Verbosity**: `low`, `medium` (default) or `high` answer length.
- **Output cap**: `max_completion_tokens` limits the tokens generated. At higher reasoning effort, raise it to avoid empty responses.

## Use Cases
1. **Coding**: write, debug and explain code.
2. **Writing and editing**: drafts, rewrites and summaries with the length set by `verbosity`.
3. **Reasoning tasks**: analysis, planning and problem solving with a higher `reasoning_effort`.
4. **Questions about images**: send screenshots, charts or photos in `image_input` and ask about them.

## Visual Examples
### Example Input
A one-line question with `reasoning_effort` set to `minimal` and `verbosity` to `medium`, with no images and no messages.

```json
{
  "prompt": "Are you AGI?",
  "messages": [],
  "verbosity": "medium",
  "image_input": [],
  "reasoning_effort": "minimal"
}
```

### Example Output
Joined output (excerpt, the answer continues with one more sentence):

> No. I’m a narrow AI language model: I can understand and generate text (and interpret images) based on patterns in data, but I don’t have general intelligence, self-awareness, goals, or broad, human-like understanding across all domains. [...]

## 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 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 |
| `reasoning_effort` | string | No | `minimal` | `minimal`, `low`, `medium`, `high` | Constrains effort on reasoning. Supported values are minimal, low, medium and high. The minimal value gets answers back faster without extensive reasoning first. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning. For higher reasoning efforts you may need to increase max_completion_tokens to avoid empty responses (where all the tokens are used on reasoning). |
| `verbosity` | string | No | `medium` | `low`, `medium`, `high` | Constrains the verbosity of the response. Lower values give more concise responses, higher values more comprehensive ones. Supported values are low, medium and high. |
| `max_completion_tokens` | integer | No | none |  | Maximum number of completion tokens to generate. For higher reasoning efforts you may need to increase your max_completion_tokens 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**: $0.0013 per API unit.
- **1 API unit = 100 weighted tokens**; an output token counts 1, an input token counts 0.125.
- That is **$1.625 per million input tokens** and **$13 per million output tokens**.
- Each request's weighted token total is rounded up to a whole unit.
- **Example cost**: the example request used 11 input tokens and 87 output tokens: 11 x 0.125 + 87 = 88.375 weighted tokens, rounded up to 1 unit = **$0.0013**.
- Higher `reasoning_effort` usually spends more tokens on reasoning, which raises the token count of a request.
- **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-writing-reasoning-ai-openai-gpt-5`

### 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.

### Endpoints

#### Create a Prediction
```bash
curl -X POST "https://prod.api.market/api/v1/magicapi/coding-writing-reasoning-ai-openai-gpt-5/predictions" \
  -H "Content-Type: application/json" \
  -H "x-api-market-key: YOUR_API_MARKET_KEY" \
  -d '{
    "version": "openai/gpt-5",
    "input": {
      "prompt": "Are you AGI?",
      "messages": [],
      "verbosity": "medium",
      "image_input": [],
      "reasoning_effort": "minimal"
    }
  }'
```

Response (201 Created):
```json
{
  "id": "bsccqx8y5drmw0d1c6t9qkwvb0",
  "version": "openai/gpt-5",
  "status": "starting",
  "created_at": "2026-10-22T03:42:36.753Z"
}
```

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

Response when the prediction has finished (200 OK):
```json
{
  "id": "bsccqx8y5drmw0d1c6t9qkwvb0",
  "status": "succeeded",
  "output": [
    "No",
    ".",
    " I",
    "’m",
    " a",
    " narrow",
    " AI",
    " language",
    " model",
    ":",
    " I",
    " can",
    " understand",
    " and",
    " generate",
    " text",
    " (",
    "and",
    " interpret",
    " images",
    ")",
    " based",
    " on",
    " patterns",
    " in",
    " data",
    ",",
    " but",
    " I",
    " don",
    "’t",
    " have",
    " general",
    " intelligence",
    ",",
    " self",
    "-awareness",
    ",",
    " goals",
    ",",
    " or",
    " broad",
    ",",
    " human",
    "-like",
    " understanding",
    " across",
    " all",
    " domains",
    "."
  ],
  "error": null,
  "created_at": "2026-10-22T03:42:36.753Z",
  "started_at": "2026-10-22T03:42:37.753Z",
  "completed_at": "2026-10-22T03:42:40.753Z"
}
```

`output` is a list of text chunks. Join them in order to get the full response. The example above is cut to its first 50 chunks; the full response had 69.

### 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-writing-reasoning-ai-openai-gpt-5";

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", 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": "Are you AGI?",
  "messages": [],
  "verbosity": "medium",
  "image_input": [],
  "reasoning_effort": "minimal"
}).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-writing-reasoning-ai-openai-gpt-5"


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", "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": "Are you AGI?",
    "messages": [],
    "verbosity": "medium",
    "image_input": [],
    "reasoning_effort": "minimal"
})))
```

## FAQ
- **Should I use `prompt` or `messages`?**
  - Use `prompt` (and `system_prompt`) for single questions. Use `messages` to send a whole conversation; when it is set, `prompt` and `system_prompt` are ignored.
- **Why did I get an empty answer?**
  - At higher `reasoning_effort` the model can use all of `max_completion_tokens` on reasoning. Raise `max_completion_tokens` or lower `reasoning_effort`.
- **How do I get the full answer text?**
  - Join the strings in `output` in order: `output.join("")` in JavaScript or `"".join(output)` in Python.
- **What did the example request cost?**
  - It used 11 input and 87 output tokens, which is 1 unit ($0.0013).
- **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`.

## 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 for coding, writing and reasoning with one API key, with image input and reasoning effort control. Pay as you go, no monthly fee.