Name: GPT-5.6 Luna API: Fast, High-Volume Text Generation
Brand: magicapi
SKU: api-magicapi-high-volume-ai-text-generation-openai-gpt-5-6-luna
Availability: InStock
Author: API Market

Shashank Agarwal

# ⚡ Text Generation API (OpenAI GPT-5.6 Luna)

## Overview
GPT-5.6 Luna is the cost-optimized tier of OpenAI's GPT-5.6. Its developer describes it as built for fast, high-volume, latency-sensitive workloads.

Send a `prompt` with an optional `system_prompt`, or a full conversation as `messages`, and optionally a list of images as `image_input`. The model returns its answer as a list of text chunks. `reasoning_effort` sets how much the model reasons before answering and `verbosity` sets how long the answer is.

## Features
- **Prompt or conversation**: send a single `prompt` (with optional `system_prompt`), or a list of `messages` such as `[{"role": "user", "content": "Hello, how are you?"}]`. When `messages` is set, `prompt` and `system_prompt` are ignored.
- **Image input**: send one or more image URLs in `image_input`.
- **Reasoning effort**: `reasoning_effort` is `none`, `low`, `medium`, `high` or `xhigh`. The default `none` gives the fastest, lowest-latency answers; higher levels think longer and use more tokens.
- **Verbosity**: `verbosity` is `low`, `medium` (default) or `high`, from short and to the point to long and comprehensive.
- **Output limit**: `max_completion_tokens` caps the generated tokens, reasoning included. There is no default in the input schema.
- **Chunked output**: the answer is returned as a list of text chunks that you join in order.

## Use Cases
1. **High-volume text tasks**: classification, extraction, rewriting and short answers at a low price per token.
2. **Chat and assistants**: fast replies in a role set by `system_prompt`, or a conversation sent as `messages`.
3. **Explanations**: short answers to factual and technical questions, as in the example.
4. **Questions about images**: send screenshots or photos in `image_input` and ask about them.

## Examples
### Example Input
A short math question with the default `reasoning_effort` `none` and `verbosity` `medium`.

```json
{
  "prompt": "What is euler's formula?",
  "messages": [],
  "verbosity": "medium",
  "image_input": [],
  "reasoning_effort": "none"
}
```

### Example Output
The joined output text:

~~~text
“Euler’s formula” commonly refers to the complex-exponential identity

\[
e^{ix}=\cos x+i\sin x,
\]

where $i^2=-1$. Setting $x=\pi$ gives the famous Euler identity:

\[
e^{i\pi}+1=0.
\]

It connects five fundamental constants: $e$, $i$, $\pi$, $1$, and $0$.

“Euler’s formula” can also refer to other results by Euler, such as the polyhedron formula $V-E+F=2$, depending on context.
~~~

## Input Parameters
| Field | Type | Required | Default | Allowed values | Description |
| --- | --- | --- | --- | --- | --- |
| `prompt` | string | No | none |  | The prompt to send to the model. Do not use it together with `messages` |
| `system_prompt` | string | No | none |  | System prompt that sets 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 strings (URLs) | No | `[]` |  | List of image URLs to send to the model |
| `reasoning_effort` | string | No | `none` | `none`, `low`, `medium`, `high`, `xhigh` | How much the model reasons before answering: `none`, `low`, `medium`, `high` or `xhigh`. `none` gives fast, low-latency responses; higher levels give more thorough answers but use more tokens and take longer. For higher levels 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` | How long the response is. Lower values give more concise responses, higher values more detailed ones |
| `max_completion_tokens` | integer | No | none |  | Maximum number of completion tokens to generate, reasoning included. 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**: $0.001 per API unit.
- **1 API unit = 100 weighted tokens**; an output token counts 1, an input token counts 0.1667.
- That is **$1.667 per million input tokens** and **$10.00 per million output tokens**.
- Each request is rounded up to a whole API unit, so the smallest charge for a request is $0.001.
- **Example cost**: the example request used 13 input tokens and 131 output tokens: 13 x 0.1667 + 131 x 1 = 133.1671 weighted tokens, rounded up to 2 API units = **$0.002**.
- Reasoning tokens count as output tokens, so higher `reasoning_effort` levels cost more per 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/high-volume-ai-text-generation-openai-gpt-5-6-luna`

### 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/high-volume-ai-text-generation-openai-gpt-5-6-luna/predictions" \
  -H "Content-Type: application/json" \
  -H "x-api-market-key: YOUR_API_MARKET_KEY" \
  -d '{
    "version": "openai/gpt-5.6-luna",
    "input": {
      "prompt": "What is euler'\''s formula?",
      "messages": [],
      "verbosity": "medium",
      "image_input": [],
      "reasoning_effort": "none"
    }
  }'
```

Response (201 Created):
```json
{
  "id": "t7bbwkqk69rmt0d1d17v66ps7g",
  "version": "openai/gpt-5.6-luna",
  "status": "starting",
  "created_at": "2026-10-23T10:30:32.644Z"
}
```

#### Check Prediction Status
```bash
curl -X GET "https://prod.api.market/api/v1/magicapi/high-volume-ai-text-generation-openai-gpt-5-6-luna/predictions/{id}" \
  -H "x-api-market-key: YOUR_API_MARKET_KEY"
```

Response when the prediction has finished (200 OK):
```json
{
  "id": "t7bbwkqk69rmt0d1d17v66ps7g",
  "status": "succeeded",
  "output": ["“", "Euler", "’s", " formula", "”", " commonly", " refers", " to", " the", " complex", "-ex", "ponential", " identity", "\n\n", "\\", "[\n", "e", "^{", "ix", "}", "=\\", "cos", " x", "+i", "\\", "sin", " x", ",\n", "\\", "]\n\n", "where", " \\(", "i", "^", "2", "=-", "1", "\\", ").", " Setting", " \\(", "x", "=\\", "pi", "\\", ")", " gives", " the", " famous", " Euler", " identity", ":\n\n", "\\", "[\n", "e", "^{", "i", "\\", "pi", "}+", "1", "=", "0", ".\n", "\\", "]\n\n", "It", " connects", " five", " fundamental", " constants", ":", " \\(", "e", "\\", "),", " \\(", "i", "\\", "),", " \\", "(\\", "pi", "\\", "),", " \\(", "1", "\\", "),", " and", " \\(", "0", "\\", ").\n\n", "“", "Euler", "’s", " formula", "”", " can", " also", " refer", " to", " other", " results", " by", " Euler", ",", " such", " as", " the", " poly", "hedron", " formula", " \\(", "V", "-E", "+", "F", "=", "2", "\\", "),", " depending", " on", " context", "."],
  "error": null,
  "created_at": "2026-10-23T10:30:32.644Z",
  "started_at": "2026-10-23T10:30:33.144Z",
  "completed_at": "2026-10-23T10:30:35.115Z"
}
```

`output` is a list of text chunks in the order the model produced them. Join them with no separator to get the full response.

### 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` 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/high-volume-ai-text-generation-openai-gpt-5-6-luna";

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-5.6-luna", 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 euler's formula?",
  "messages": [],
  "verbosity": "medium",
  "image_input": [],
  "reasoning_effort": "none"
}).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/high-volume-ai-text-generation-openai-gpt-5-6-luna"


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-5.6-luna", "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")


output = create_and_wait_for_prediction({
    "prompt": "What is euler's formula?",
    "messages": [],
    "verbosity": "medium",
    "image_input": [],
    "reasoning_effort": "none"
})
print("".join(output))
```

## FAQ
- **How do I get the full answer as one string?**
  - `output` is a list of text chunks. Join them in order with no separator: `output.join("")` in JavaScript or `"".join(output)` in Python.
- **Should I use `prompt` or `messages`?**
  - Use `prompt` (plus `system_prompt`) for a single question. Use `messages` to send a whole conversation; when `messages` is set, `prompt` and `system_prompt` are ignored.
- **Which `reasoning_effort` should I pick?**
  - Keep the default `none` for high-volume and latency-sensitive work. Use `low` to `high` for questions that need more thought, and `xhigh` for the hardest ones; each step up takes longer and uses more tokens.
- **Why did I get an empty answer?**
  - With a higher `reasoning_effort`, all of `max_completion_tokens` can be spent on reasoning. Raise `max_completion_tokens` or lower `reasoning_effort`.
- **How much does a request cost?**
  - $1.667 per million input tokens and $10.00 per million output tokens, billed in units of $0.001 and rounded up per request. The example cost $0.002.
- **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.


Call OpenAI's GPT-5.6 Luna, the cost-optimized GPT-5.6 tier, for fast high-volume chat, extraction and image questions. Pay as you go, no monthly fee.