Name: Llama 4 Scout Instruct API: Chat LLM by Meta
Brand: magicapi
SKU: api-magicapi-large-language-model-inference-meta-llama-4-scout-instruct
Availability: InStock
Author: API Market

Shashank Agarwal

# 🦙 Chat Language Model API (Meta Llama 4 Scout Instruct)

## Overview
Llama 4 Scout Instruct is a chat language model from Meta. Meta describes it as a 17 billion parameter model with 16 experts.

Send a text `prompt`, optionally with a `system_prompt` that sets the assistant's behavior. Control the answer length with `max_tokens` and `min_tokens`, and sampling with `temperature`, `top_p` and `top_k`. The answer comes back as a list of text chunks; join them in order to get the full text.

## Features
- **System prompt**: `system_prompt` (default "You are a helpful assistant.") is prepended to your prompt to guide the model's behavior.
- **Answer length**: `max_tokens` (0 to 131072, default 4096) caps the output; `min_tokens` (default 0) sets a minimum.
- **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**: end generation early at any string in a comma-separated `stop_sequences` list.
- **Prompt template**: pass your own `prompt_template` to format the prompt; leave it empty to use the default template.
- **Text in, text out**: this API takes text only; there is no image input.

## Use Cases
1. **Chat assistants**: answer customer or user questions with a fixed persona set in `system_prompt`.
2. **Writing help**: drafts, rewrites, summaries and replies.
3. **Text processing**: classify, extract or reformat text, with low `temperature` for consistent results.
4. **Prototyping**: try prompts on an open-weight Meta model before building on it.

## Visual Examples
### Example Input
Prompt: "Hello, Llama!", with `max_tokens` `1024` and `temperature` `0.6`. `system_prompt` was left at its default.

```json
{
  "top_p": 1,
  "prompt": "Hello, Llama!",
  "max_tokens": 1024,
  "temperature": 0.6,
  "presence_penalty": 0,
  "frequency_penalty": 0
}
```

### Example Output
The model returned 33 text chunks. Joined, they read:

~~~text
Hello! It's nice to meet you. I'm here to help with any questions or tasks you have, so feel free to ask me anything. How can I assist you today?
~~~

## Input Parameters
| Field | Type | Required | Default | Allowed values | Description |
| --- | --- | --- | --- | --- | --- |
| `prompt` | string | No | `""` |  | The text prompt to send to the model |
| `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 | `4096` | `0` to `131072` | 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 below 1.0, only the top tokens with cumulative probability >= top_p are kept (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 | `""` |  | A comma-separated list of sequences to stop generation at. For example, '<end>,<stop>' stops generation at the first instance of '<end>' or '<stop>'. |
| `prompt_template` | string | No | `""` |  | A template to format the prompt with. If not provided, the default prompt template is used. |

## Pricing
- **Pay As You Go (PAYG) pricing**: you only pay for what you use.
- **Price**: $0.001 per API unit.
- **1 API unit = 1,000 weighted tokens**; an output token counts 1, an input token counts 0.2616.
- That is **$0.2616 per million input tokens** and **$1 per million output tokens**.
- Each request is rounded up to a whole API unit, so the minimum charge is 1 unit ($0.001).
- The input token count includes the `system_prompt` and the prompt formatting, so it is higher than the length of your prompt alone (the two-word example prompt counted as 26 input tokens).
- **Example cost**: the example request used 26 input tokens and 36 output tokens: 26 x 0.2616 + 36 = 42.8016 weighted tokens, rounded up to 1 API unit = **$0.001**.
- **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/large-language-model-inference-meta-llama-4-scout-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/large-language-model-inference-meta-llama-4-scout-instruct/predictions" \
  -H "Content-Type: application/json" \
  -H "x-api-market-key: YOUR_API_MARKET_KEY" \
  -d '{
      "version": "meta/llama-4-scout-instruct",
      "input": {
        "top_p": 1,
        "prompt": "Hello, Llama!",
        "max_tokens": 1024,
        "temperature": 0.6,
        "presence_penalty": 0,
        "frequency_penalty": 0
      }
    }'
```

Response (201 Created):
```json
{
  "id": "5sqzqn33zdrmw0d1c6wvyt766r",
  "version": "meta/llama-4-scout-instruct",
  "status": "starting",
  "created_at": "2026-10-22T03:48:22.386Z"
}
```

#### Check Prediction Status
```bash
curl -X GET "https://prod.api.market/api/v1/magicapi/large-language-model-inference-meta-llama-4-scout-instruct/predictions/{id}" \
  -H "x-api-market-key: YOUR_API_MARKET_KEY"
```

Response when the prediction has finished (200 OK):
```json
{
  "id": "5sqzqn33zdrmw0d1c6wvyt766r",
  "status": "succeeded",
  "output": [
    "He",
    "llo",
    "! It'",
    "s ni",
    "c",
    "e to",
    " meet",
    " yo",
    "u. I'",
    "m her"
  ],
  "error": null,
  "created_at": "2026-10-22T03:48:22.386Z",
  "started_at": "2026-10-22T03:48:22.400Z",
  "completed_at": "2026-10-22T03:48:24.160Z"
}
```

`output` is a list of text chunks. Join them in order to get the full answer. The response above is shortened to the first 10 of the 33 chunks; the joined text is shown under Example Output.

### 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` above 131072 or a value of the wrong type, such as text in `temperature`.
- **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/large-language-model-inference-meta-llama-4-scout-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: "meta/llama-4-scout-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;
    }

    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_p": 1,
    "prompt": "Hello, Llama!",
    "max_tokens": 1024,
    "temperature": 0.6,
    "presence_penalty": 0,
    "frequency_penalty": 0
  }).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/large-language-model-inference-meta-llama-4-scout-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": "meta/llama-4-scout-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 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_p": 1,
    "prompt": "Hello, Llama!",
    "max_tokens": 1024,
    "temperature": 0.6,
    "presence_penalty": 0,
    "frequency_penalty": 0
})
print("".join(output))
```

## FAQ
- **How do I get the full answer text?**
  - Join the chunks in `output` in order, for example `output.join("")` in JavaScript or `"".join(output)` in Python.
- **How do I change the assistant's behavior?**
  - Set `system_prompt`. It is prepended to your prompt; the default is "You are a helpful assistant."
- **Can I send images?**
  - No. This API takes text only: `prompt`, `system_prompt` and the generation settings.
- **How do I stop the answer at a marker?**
  - Put one or more strings in `stop_sequences`, separated by commas, for example `<end>,<stop>`. Generation stops at the first match.
- **How much does a request cost?**
  - Input tokens cost $0.2616 per million and output tokens $1 per million, billed in units of 1,000 weighted tokens at $0.001 each and rounded up per request. The example above cost $0.001.
- **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.


Run Meta Llama 4 Scout Instruct, a 17B-parameter mixture-of-experts chat model, with system prompts, stop sequences and sampling controls.