Llama-4-maverick

This documentation is valid for the following list of our models: meta-llama/llama-4-maverick	Try in Playground

Model Overview

A 17 billion active parameter model with 128 experts, is the best multimodal model in its class, beating GPT-4o and Gemini 2.0 Flash on a wide range of common benchmarks, while achieving comparable results to the new DeepSeek v3 on reasoning and coding—with less than half the number of active parameters.

How to Make a Call

Step-by-Step Instructions

1️ Setup You Can’t Skip

▪️ Create an Account: Visit the AI/ML API website and create an account (if you don’t have one yet). ▪️ Generate an API Key: After logging in, navigate to your account dashboard and generate your API key. Ensure that key is enabled on UI.

2️ Copy the code example

At the bottom of this page, you'll find a code example that shows how to structure the request. Choose the code snippet in your preferred programming language and copy it into your development environment.

3️ Modify the code example

▪️ Replace <YOUR_AIMLAPI_KEY> with your actual AI/ML API key from your account. ▪️ Insert your question or request into the content field—this is what the model will respond to.

4️ ^_(Optional) Adjust other optional parameters if needed

Only model and messages are required parameters for this model (and we’ve already filled them in for you in the example), but you can include optional parameters if needed to adjust the model’s behavior. Below, you can find the corresponding API schema, which lists all available parameters along with notes on how to use them.

5️ Run your modified code

Run your modified code in your development environment. Response time depends on various factors, but for simple prompts it rarely exceeds a few seconds.

If you need a more detailed walkthrough for setting up your development environment and making a request step by step — feel free to use our Quickstart guide.

API Schema

Code Example

Python

import requests
import json  # for getting a structured output with indentation 

response = requests.post(
    "https://api.aimlapi.com/v1/chat/completions",
    headers={
        # Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>:
        "Authorization":"Bearer <YOUR_AIMLAPI_KEY>",
        "Content-Type":"application/json"
    },
    json={
        "model":"meta-llama/llama-4-maverick",
        "messages":[
            {
                "role":"user",
                "content":"Hello"  # insert your prompt here, instead of Hello
            }
        ],
    }
)

data = response.json()
print(json.dumps(data, indent=2, ensure_ascii=False))

JavaScript

async function main() {
  const response = await fetch('https://api.aimlapi.com/v1/chat/completions', {
    method: 'POST',
    headers: {
      // Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>
      'Authorization': 'Bearer <YOUR_AIMLAPI_KEY>',
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({
      model: 'meta-llama/llama-4-maverick',
      messages:[
          {
              role:'user',
              content: 'Hello'   // insert your prompt here, instead of Hello
          }
      ],
    }),
  });

  const data = await response.json();
  console.log(JSON.stringify(data, null, 2));
}

main();

Response

{'id': 'npXgTRD-28Eivz-92e226847aa70d87', 'object': 'chat.completion', 'choices': [{'index': 0, 'finish_reason': 'stop', 'logprobs': None, 'message': {'role': 'assistant', 'content': 'Hello! How are you today? Is there something I can help you with or would you like to chat?', 'tool_calls': []}}], 'created': 1744287125, 'model': 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8', 'usage': {'prompt_tokens': 6, 'completion_tokens': 41, 'total_tokens': 47}}