Name: IBM Granite 3.1 8B Instruct API: Open Source LLM
Brand: magicapi
SKU: api-magicapi-instruction-following-ai-api-ibm-granite-granite-3-1-8b-instruct
Availability: InStock
Author: API Market

Shashank Agarwal

# 💬 Instruction-Following LLM API (IBM Granite 3.1 8B Instruct)

## Overview
Granite 3.1 8B Instruct is an open-source 8B-parameter language model from IBM. Its developer describes it as lightweight and designed for instruction-following tasks such as summarization, problem-solving, text translation, reasoning, code tasks and function calling.

Send a text `prompt` and an optional `system_prompt`; the model returns generated text. `max_tokens` (default 512) and `min_tokens` set the length of the answer, and `temperature`, `top_p`, `top_k`, `presence_penalty`, `frequency_penalty` and `stop_sequences` control sampling.

## Features
- **System prompt**: `system_prompt` (default `You are a helpful assistant.`) is prepended to the prompt to guide behavior.
- **Length controls**: `max_tokens` (default 512) and `min_tokens` (default 0).
- **Sampling controls**: `temperature` (default 0.6), `top_p` (default 0.9) and `top_k` (default 50).
- **Repetition controls**: `presence_penalty` and `frequency_penalty` (both default 0).
- **Stop sequences**: `stop_sequences` ends generation at any of a comma-separated list of strings.

## Use Cases
1. **Summarization**: condense reports, emails and articles pasted into the prompt.
2. **Translation**: translate short texts between languages.
3. **Question answering**: factual and explanatory answers in a persona set by `system_prompt`.
4. **Code tasks**: explain or write short code snippets.

## Visual Examples
### Example Input
System prompt: "You are an expert in tech history and can answer questions eloquently about the milestones of software organizations."

Prompt: "Please list an IBM Research laboratory located in the United States."

Settings: `max_tokens` 512, `min_tokens` 0, `temperature` 0.6, `top_p` 0.9, `top_k` 50, `presence_penalty` 0, `frequency_penalty` 0.

### Example Output
The chunks of this run joined into one answer:

````markdown
One of IBM Research's laboratories located in the United States is the IBM Thomas J. Watson Research Center. This facility is situated in Yorktown Heights, New York, approximately 38 miles north of New York City. Established in 1956, it's one of the oldest corporate-backed research centers in the world. The center has played a pivotal role in numerous technological advancements, including the development of the relational database, the Scanning Tunneling Microscope, high-capacity data storage, and the Java programming language, among others.
````

## Input Parameters
| Field | Type | Required | Default | Allowed values | Description |
| --- | --- | --- | --- | --- | --- |
| `prompt` | string | No | `""` |  | Prompt |
| `system_prompt` | string | No | `You are a helpful assistant.` |  | System prompt to send to the model. It is prepended to the prompt and helps guide the model's behavior. |
| `min_tokens` | integer | No | `0` |  | The minimum number of tokens the model should generate as output. |
| `max_tokens` | integer | No | `512` |  | The maximum number of tokens the model should generate as output. |
| `temperature` | number | No | `0.6` |  | The value used to modulate the next token probabilities. |
| `top_p` | number | No | `0.9` |  | A probability threshold for generating the output. If < 1.0, only keep the top tokens with cumulative probability >= top_p (nucleus filtering). |
| `top_k` | integer | No | `50` |  | The number of highest probability tokens to consider for generating the output. If > 0, only keep the top k tokens with highest probability (top-k filtering). |
| `presence_penalty` | number | No | `0` |  | Presence penalty |
| `frequency_penalty` | number | No | `0` |  | Frequency penalty |
| `stop_sequences` | string | No | none |  | A comma-separated list of sequences to stop generation at. For example, `<end>,<stop>` stops generation at the first instance of `<end>` or `<stop>`. |

## Pricing
- **Pay As You Go (PAYG) pricing**: you only pay for what you use.
- **Price**: $0.0032 per API unit.
- **1 API unit = 10,000 weighted tokens**; an output token counts 1, an input token counts 0.12.
- That is **$0.0384 per million input tokens** and **$0.32 per million output tokens**.
- Each request is rounded up to a whole API unit, so the smallest charge per request is $0.0032.
- **Example cost**: the example request used 39 input tokens and 130 output tokens. 39 x 0.12 + 130 = 134.68 weighted tokens, rounded up to 1 API unit = **$0.0032**.
- **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/instruction-following-ai-api-ibm-granite-granite-3-1-8b-instruct`

### 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/instruction-following-ai-api-ibm-granite-granite-3-1-8b-instruct/predictions" \
  -H "Content-Type: application/json" \
  -H "x-api-market-key: YOUR_API_MARKET_KEY" \
  -d '{
    "version": "ibm-granite/granite-3.1-8b-instruct",
    "input": {
      "top_k": 50,
      "top_p": 0.9,
      "prompt": "Please list an IBM Research laboratory located in the United States.",
      "max_tokens": 512,
      "min_tokens": 0,
      "temperature": 0.6,
      "system_prompt": "You are an expert in tech history and can answer questions eloquently about the milestones of software organizations.",
      "presence_penalty": 0,
      "frequency_penalty": 0
    }
  }'
```

Response (201 Created):
```json
{
  "id": "rttg7w8va9rmt0d1d0z85r4v78",
  "version": "ibm-granite/granite-3.1-8b-instruct",
  "status": "starting",
  "created_at": "2026-10-23T10:11:03.278Z"
}
```

#### Check Prediction Status
```bash
curl -X GET "https://prod.api.market/api/v1/magicapi/instruction-following-ai-api-ibm-granite-granite-3-1-8b-instruct/predictions/{id}" \
  -H "x-api-market-key: YOUR_API_MARKET_KEY"
```

Response when the prediction has finished (200 OK):
```json
{
  "id": "rttg7w8va9rmt0d1d0z85r4v78",
  "status": "succeeded",
  "output": [
    "One of IBM Research's laboratories located in the United States is the IBM Thomas J. Watson Research Center. This facility is situated in Yorktown Heights, New York, approximately 38 miles north of New York City. Established in 1956, it's one of the oldest corporate-backed research centers in the world. The center has played a pivotal role in numerous technological advancements, including the development of the relational database, the Scanning Tunneling Microscope, high-capacity data storage, and the Java programming language, among others."
  ],
  "error": null,
  "created_at": "2026-10-23T10:11:03.278Z",
  "started_at": "2026-10-23T10:13:57.635Z",
  "completed_at": "2026-10-23T10:14:00.641Z"
}
```

`output` is an array of text chunks. Join them in order to get the full answer. The example above shows the chunks from this run already joined into one string.

### 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 `max_tokens` value that is not an integer.
- **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/instruction-following-ai-api-ibm-granite-granite-3-1-8b-instruct";

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: "ibm-granite/granite-3.1-8b-instruct", 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.join("");
    }

    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({
    "top_k": 50,
    "top_p": 0.9,
    "prompt": "Please list an IBM Research laboratory located in the United States.",
    "max_tokens": 512,
    "min_tokens": 0,
    "temperature": 0.6,
    "system_prompt": "You are an expert in tech history and can answer questions eloquently about the milestones of software organizations.",
    "presence_penalty": 0,
    "frequency_penalty": 0
  }).then((output) => console.log(output));
```

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

API_KEY = "YOUR_API_MARKET_KEY"
BASE_URL = "https://prod.api.market/api/v1/magicapi/instruction-following-ai-api-ibm-granite-granite-3-1-8b-instruct"


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": "ibm-granite/granite-3.1-8b-instruct", "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 "".join(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({
    "top_k": 50,
    "top_p": 0.9,
    "prompt": "Please list an IBM Research laboratory located in the United States.",
    "max_tokens": 512,
    "min_tokens": 0,
    "temperature": 0.6,
    "system_prompt": "You are an expert in tech history and can answer questions eloquently about the milestones of software organizations.",
    "presence_penalty": 0,
    "frequency_penalty": 0
})
print(output)
```

## FAQ
- **Why does `output` come back as a list?**
  - The answer is returned as an array of text chunks. Join them in order (`output.join("")` in JavaScript, `"".join(output)` in Python).
- **How do I set the model's role or tone?**
  - Use `system_prompt`. It is prepended to your prompt; the default is `You are a helpful assistant.`
- **How do I stop the answer at a marker?**
  - Pass `stop_sequences` as a comma-separated string, for example `<end>,<stop>`.
- **How is a request billed?**
  - By weighted tokens at $0.0032 per 10,000: input tokens count 0.12 and output tokens count 1, rounded up to a whole unit per request. Most short requests cost $0.0032.
- **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.


Run IBM Granite 3.1 8B Instruct for summarization, translation, Q&A and code tasks with a system prompt and sampling controls. Pay as you go, no monthly fee.