How to Access Meta’s Llama 4 Models via API 

Meta’s Llama 4 is a major leap in open-source AI, offering multimodal support, a Mixture-of-Experts architecture, and massive context windows. But what really sets it apart is accessibility. Whether you’re building apps, running experiments, or scaling AI systems, there are multiple ways to access Llama 4 via API. In this guide, I will show you how to access Llama 4 Scout and Maverick models on some of the best API platforms, like OpenRouter, Hugging Face, GroqCloud, etc.

Key Features and Capabilities of Llama 4

• Native Multimodality & Early Fusion: Processes text and images together from the start using early fusion. Supports up to 5 images per prompt—ideal for image captioning, visual Q&A, and more.
• Mixture of Experts (MoE) Architecture: Routes each input to a small subset of expert networks, improving efficiency.
  • Scout: 17B active / 109B total, 16 experts
  • Maverick: 17B active / 400B total, 128 experts
  • Behemoth: 288B active / ~2T total (in training)
• Extended Context Window: Handles long inputs with ease.
  • Scout: up to 10 million tokens
  • Maverick: up to 1 million tokens
• Multilingual Support: Natively supports 12 languages and was trained on data from 200+. Performs best in English for image-text tasks.
• Expert Image Grounding: Links text to specific image regions for precise visual reasoning and high-quality image-based answers.

Click here to more about the training and benchmarks of Meta’s Llama 4.

Llama 4 at #2 overall in the LMSYS Chatbot Arena

Meta’s Llama 4 Maverick ranks #2 overall in the LMSYS Chatbot Arena with an impressive Arena Score of 1417, outperforming GPT-4o and Gemini 2.0 Flash in key tasks like image reasoning (MMMU: 73.4%), code generation (LiveCodeBench: 43.4%), and multilingual understanding (84.6% on Multilingual MMLU).

It’s also efficient running on a single H100 with lower costs and fast deployment. These results highlight Llama 4’s balance of power, versatility, and affordability, making it a strong choice for production AI workloads.

How to Access Meta’s Llama 4 Models?

Meta has made Llama 4 accessible through various platforms and methods, catering to different user needs and technical expertise.

Accessing Llama 4 Models via Meta AI Platform

The simplest way to try Llama 4 is through Meta’s AI platform at meta.ai. You can start chatting with the assistant instantly, no sign-up required. It runs on Llama 4, which you can confirm by asking, “Which model are you? Llama 3 or Llama 4?” The assistant will respond, “I am built on Llama 4.” However, this platform has its limitations: there’s no API access, and customization options are minimal.

Downloading Model Weights from Llama.com

You can download the model weights from llama.com. You need to fill out a request form first. After approval, you can get Llama 4 Scout and Maverick. Llama 4 Behemoth may come later. This method gives full control. You can run it locally or in the cloud. But it is best for developers. There is no chat interface.

Accessing Llama 4 Models through API Providers

Several platforms offer API access to Llama 4, providing developers with the tools to integrate the model into their own applications.

OpenRouter

OpenRouter.ai provides free API access to both Llama 4 models, Maverick and Scout. After signing up, you can explore available models, generate API keys, and start making requests. OpenRouter also includes a built-in chat interface, which makes it easy to test responses before integrating them into your application.

Hugging Face

To access Llama 4 via Hugging Face, follow these steps:

1. Create a Hugging Face Account
Visit https://huggingface.co and sign up for a free account if you haven’t already.

2. Find the Llama 4 Model Repository
After logging in, search for the official Meta Llama organization or a specific Llama 4 model like meta-llama/Llama-4-Scout-17B-16E-Instruct. You can also find links to official repositories on the Llama website or Hugging Face’s blog.

3. Request Access to the Model
Navigate to the model page and click the “Request Access” button. You’ll need to fill out a form with the following details like Full Legal Name, Date of Birth, Full Organization Name (no acronyms or special characters), Country, Affiliation (e.g., Student, Researcher, Company), and Job Title.

You’ll also need to carefully review and accept the Llama 4 Community License Agreement. Once all fields are completed, click “Submit” to request access. Make sure the information is accurate, as it may not be editable after submission.

4. Wait for Approval
Once submitted, your request will be reviewed by Meta. If access is granted automatically, you’ll get access immediately. Otherwise, the process may take a few hours to several days. You’ll be notified via email when your access is approved.

5. Access the Model Programmatically
To use the model in your code, first install the required library:

pip install transformers

Then, authenticate using your Hugging Face token:

from huggingface_hub import login

login(token="YOUR_HUGGING_FACE_ACCESS_TOKEN")

(You can generate a "read" token from your Hugging Face account settings under Access Tokens.)

Now, load and use the model as shown below:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "meta-llama/Llama-4-Scout-17B-16E-Instruct"  # Replace with your chosen model

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForCausalLM.from_pretrained(model_name)

# Inference

input_text = "What is the capital of India?"

input_ids = tokenizer.encode(input_text, return_tensors="pt")

output = model.generate(input_ids, max_length=50, num_return_sequences=1)

print(tokenizer.decode(output[0], skip_special_tokens=True))

Alternative Access Options:

• Hugging Face Inference API: Some Llama 4 models may offer API access, but availability and cost depend on Meta’s policy.
• Download Model Weights: Once access is approved, you can download the weights from the model repository for local usage.

By completing these steps and meeting the approval criteria, you can successfully access and use Llama 4 models on the Hugging Face platform.

Cloudflare Workers AI

Cloudflare offers Llama 4 Scout as a serverless API through its Workers AI platform. It allows you to invoke the model via API calls with minimal setup. A built-in AI playground is available for testing, and no account is required to get started with basic access, making it ideal for lightweight or experimental use.

Snowflake Cortex AI

For Snowflake users, Scout and Maverick can be accessed inside the Cortex AI environment. These models can be used through SQL or REST APIs, enabling seamless integration into existing data pipelines and analytical workflows. It’s especially useful for teams already leveraging Snowflake’s platform.

Amazon SageMaker JumpStart and Bedrock

Llama 4 is integrated into Amazon SageMaker JumpStart, with additional availability planned for Bedrock. Through the SageMaker console, you can deploy and manage the model easily. This method is particularly useful if you’re already building on AWS and want to embed LLMs into your cloud-native solutions.

GroqCloud

GroqCloud gives early access to both Scout and Maverick. You can use them via GroqChat or API calls. Signing up provides free access, while paid tiers offer higher limits, making this suitable for both exploration and scaling into production.

Together AI

Together AI offers API access to Scout and Maverick after a simple registration process. Developers receive free credits upon sign-up and can immediately start using the API with an issued key. It’s developer-friendly and offers high-performance inference.

Replicate

Replicate hosts Llama 4 Maverick Instruct, which can be run using their API. Pricing is based on token usage, so you pay only for what you use. It’s a good choice for developers looking to experiment or build lightweight applications without upfront infrastructure costs.

Fireworks AI

Fireworks AI also provides Llama 4 Maverick Instruct through a serverless API. Developers can follow Fireworks’ documentation to set up and begin generating responses quickly. It’s a clean solution for those looking to run LLMs at scale without managing servers.

Platforms and Methods for Accessing Llama 4 Models

The wide array of platforms and access methods highlights the accessibility of Llama 4 to a diverse audience, ranging from individuals wanting to explore its capabilities to developers seeking to integrate it into their applications.

Let’s Try Llama 4 Scout and Maverick via API

In this comparison, we evaluate Meta’s Llama 4 Scout and Maverick models across various task categories such as summarization, code generation, and multimodal image understanding. All experiments were conducted on Google Colab. For simplicity, we access our API key using userdata, which has a shortened reference to the key.

Here’s a quick peek at how we tested each model via Python using Groq:

Prerequisites

Before we dive into the code, make sure you have the following set up:

1. A GroqCloud account
2. Your Groq API Key set as an environment variable (GROQ_API_KEY)
3. The Groq Python SDK installed:

pip install groq

Setup: Initializing the Groq Client

Now, initialize the Groq client in your notebook:

import os

from groq import Groq

# Set your API key

os.environ["GROQ_API_KEY"] = userdata.get('Groq_Api')

# Initialize the client

client = Groq(api_key=os.environ.get("GROQ_API_KEY"))

Task 1: Summarizing a Long Document

We provided both models with a long passage about AI’s evolution and asked for a concise summary.

Llama 4 Scout

long_document_text = """<your long document goes here>"""

prompt_summary = f"Please provide a concise summary of the following document:\n\n{long_document_text}"

# Scout

summary_scout = client.chat.completions.create(

    model="meta-llama/llama-4-scout-17b-16e-instruct",

    messages=[{"role": "user", "content": prompt_summary}],

    max_tokens=500

).choices[0].message.content

print("Summary (Scout):\n", summary_scout)

Output:

Llama 4 Maverick

# Maverick

summary_maverick = client.chat.completions.create(

    model="meta-llama/llama-4-maverick-17b-128e-instruct",

    messages=[{"role": "user", "content": prompt_summary}],

    max_tokens=500

).choices[0].message.content

print("\nSummary (Maverick):\n", summary_maverick)

Output:

Task 2: Code Generation from Description

We asked both models to write a Python function based on a simple functional prompt.

Llama 4 Scout

code_description = "Write a Python function that takes a list of numbers as input and returns the average of those numbers."

prompt_code = f"Please write the Python code for the following description:\n\n{code_description}"

# Scout

code_scout = client.chat.completions.create(

    model="meta-llama/llama-4-scout-17b-16e-instruct",

    messages=[{"role": "user", "content": prompt_code}],

    max_tokens=200

).choices[0].message.content

print("Generated Code (Scout):\n", code_scout)

Output:

Llama 4 Maverick

# Maverick

code_maverick = client.chat.completions.create(

    model="meta-llama/llama-4-maverick-17b-128e-instruct",

    messages=[{"role": "user", "content": prompt_code}],

    max_tokens=200

).choices[0].message.content

print("\nGenerated Code (Maverick):\n", code_maverick)

Output:

Task 3: Image Understanding (Multimodal)

We provided both models with the same image URL and asked for a detailed description of its content.

image_url = "https://cdn.analyticsvidhya.com/wp-content/uploads/2025/04/Screenshot-2025-04-06-at-3.09.43%E2%80%AFAM.webp"

prompt_image = "Describe the contents of this image in detail. Make sure it’s not incomplete."

# Scout

description_scout = client.chat.completions.create(

    model="meta-llama/llama-4-scout-17b-16e-instruct",

    messages=[

        {

            "role": "user",

            "content": [

                {"type": "text", "text": prompt_image},

                {"type": "image_url", "image_url": {"url": image_url}}

            ]

        }

    ],

    max_tokens=150

).choices[0].message.content

print("Image Description (Scout):\n", description_scout)

Output:

Llama 4 Maverick

# Maverick

description_maverick = client.chat.completions.create(

    model="meta-llama/llama-4-maverick-17b-128e-instruct",

    messages=[

        {

            "role": "user",

            "content": [

                {"type": "text", "text": prompt_image},

                {"type": "image_url", "image_url": {"url": image_url}}

            ]

        }

    ],

    max_tokens=150

).choices[0].message.content

print("\nImage Description (Maverick):\n", description_maverick)

Output:

Task Analysis

Both Llama 4 Scout and Llama 4 Maverick offer impressive capabilities, but they shine in different domains. Scout excels in handling long-form content thanks to its extended context window, making it ideal for summarization and quick interactions.

On the other hand, Maverick stands out in technical tasks and multimodal reasoning, delivering higher precision in code generation and image interpretation. Choosing between them ultimately depends on your specific use case – you get breadth & speed with Scout, and depth & accuracy with Maverick.

Conclusion

Llama 4 is a major step in AI progress. It is a top multimodal model with strong features. It handles text and images natively. Its mixture-of-experts setup is efficient. It also supports long context windows. This makes it powerful and flexible. Llama 4 is open-source and widely accessible. This helps innovation and broad adoption. Bigger versions like Behemoth are in development. That shows continued growth in the Llama ecosystem.

Frequently Asked Questions

Janvi Kumari

Hi, I am Janvi, a passionate data science enthusiast currently working at Analytics Vidhya. My journey into the world of data began with a deep curiosity about how we can extract meaningful insights from complex datasets.