Name: GPT-5.6 Terra API: OpenAI Text Generation LLM
Brand: magicapi
SKU: api-magicapi-text-generation-llm-openai-gpt-5-6-terra
Availability: InStock
Author: API Market

Shashank Agarwal

# 💬 Text Generation LLM API (OpenAI GPT-5.6 Terra)

## Overview
GPT-5.6 Terra is a language model from OpenAI. Its developer describes it as the balanced tier of GPT-5.6, tuned for everyday production work at roughly half the cost of the flagship tier.

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` (default), `low`, `medium`, `high` or `xhigh`. `none` gives fast, low-latency answers; higher levels give more thorough answers 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. Answers often use Markdown (headings, tables, code blocks).

## Use Cases
1. **Technical explanations**: compare languages, tools or approaches, as in the example.
2. **Production text tasks**: summaries, drafts, replies and classification prompts at everyday volume.
3. **Multi-turn assistants**: send the full conversation history as `messages`.
4. **Questions about images**: send screenshots, photos or charts in `image_input` and ask about them.

## Examples
### Example Input
```json
{
  "prompt": "What is Objective C vs C?",
  "messages": [],
  "verbosity": "medium",
  "image_input": [],
  "reasoning_effort": "none"
}
```

### Example Output
The model answered in Markdown with sections on C, on Objective-C, a comparison table and a closing note (591 output tokens). The start of the joined output text:

```text
**C** and **Objective-C** are related programming languages, but Objective-C adds object-oriented features on top of C.

## C

C is a procedural, low-level/general-purpose language.
```

The answer continues with a C code sample, an Objective-C section with a class example, and a table of key differences.

## 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. The default `none` gives fast, low-latency responses. Higher efforts give more thorough answers but use more tokens and take longer; 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. 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**: $0.0019 per API unit.
- **1 API unit = 100 weighted tokens.** An output token counts 1, an input token counts 0.1667.
- That works out to **$3.1673 per million input tokens** and **$19 per million output tokens**.
- Each request is rounded up to a whole API unit, so the smallest charge for a request is $0.0019.
- **Example cost**: the request below used 13 input tokens and 591 output tokens: 13 x 0.1667 + 591 = 593.1671 weighted tokens, rounded up to 6 API units = **$0.0114**.
- Reasoning tokens count as output tokens, so higher `reasoning_effort` levels cost more per request. Long answers cost more than short ones; lower `verbosity` or set `max_completion_tokens` to keep answers short.
- **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/text-generation-llm-openai-gpt-5-6-terra`

### 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/text-generation-llm-openai-gpt-5-6-terra/predictions" \
  -H "Content-Type: application/json" \
  -H "x-api-market-key: YOUR_API_MARKET_KEY" \
  -d '{
    "version": "openai/gpt-5.6-terra",
    "input": {
      "prompt": "What is Objective C vs C?",
      "messages": [],
      "verbosity": "medium",
      "image_input": [],
      "reasoning_effort": "none"
    }
  }'
```

Response (201 Created):
```json
{
  "id": "8a61g695gdrmr0d1d189rn27eg",
  "version": "openai/gpt-5.6-terra",
  "status": "starting",
  "created_at": "2026-10-23T10:30:45.523Z"
}
```

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

Response when the prediction has finished (200 OK):
```json
{
  "id": "8a61g695gdrmr0d1d189rn27eg",
  "status": "succeeded",
  "output": ["**", "C", "**", " and", " **", "Objective", "-C", "**", " are", " related", " programming", " languages", ",", " but", " Objective", "-C", " adds", " object", "-oriented", " features", " on", " top", " of", " C", ".\n\n"],
  "error": null,
  "created_at": "2026-10-23T10:30:45.523Z",
  "started_at": "2026-10-23T10:30:46.023Z",
  "completed_at": "2026-10-23T10:30:52.320Z"
}
```

`output` is a list of text chunks in the order the model produced them (shortened above to the first sentence; the full example answer has several hundred chunks). 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/text-generation-llm-openai-gpt-5-6-terra";

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-terra", 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 Objective C vs C?",
  "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/text-generation-llm-openai-gpt-5-6-terra"


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-terra", "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 Objective C vs C?",
    "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.
- **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 do I keep costs down?**
  - Output tokens make up most of the price. Set `verbosity` to `low`, keep `reasoning_effort` at `none` and set `max_completion_tokens` for an upper bound per request.
- **Can I send images?**
  - Yes. Put one or more image URLs in `image_input` and ask about them in `prompt`.
- **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 Terra for chat, writing, explanations and image questions with selectable reasoning effort and verbosity. Pay as you go, no monthly fee.