Build With
LLama 4
Access new generation Scout and Maverick models that seamlessly blend text, images, and video processing.
Meta LLama 4 Features: New Multimodal Experience
Llama 4 benchmarks showcase Meta's impressive new model family with Scout, Maverick, and Behemoth variants utilizing MoE architecture and a 10M token context window. Performance metrics include Maverick's 85.5 on MMLU, 61.2 on MATH, 77.6 on MBPP (coding), 85.3 on ChartQA, 91.6 on DocVQA, and 84.6 on Multilingual MMLU—often outperforming proprietary models like Gemini 2.5 Pro and GPT-4o in long-context and coding tasks.
Extended Context Windows
Llama 4 supports a dramatically increased context length. For example, Llama 4 Scout can handle up to 10 million tokens.
Multimodal Fusion
Handles text, images, and video as a single sequence for seamless multimodal reasoning.
Mixture of Experts Architecture
Each input token is routed to a subset of specialized expert networks, improving efficiency, scalability, and quality.
Multilingual Support
Llama pre-trained on data from over 200 languages, and have a billion tokens use in more than 100 languages.
Expert Image Grounding
Llama 4 improved vision encoders and can refer to specific image regions, and ground its reasoning in visual content.
Efficient Training and Deployment
Llama 4 introduces innovations like FP8 precision training and the MetaP hyperparameter tuning technique.
3 Ways LLama 4 Transforms Business
Discover how Meta's new models change operations across industries.
Supply Chain Optimization
Llama 4 can analyze inventory, sales, feedback, and market data to provide insights and automate inventory management. Retailers can use it to monitor stock levels, predict demand, and automatically generate purchase orders when needed.
Enterprise Document Intelligence
Llama 4 Scout's 10M token window analyzes vast document collections, helping businesses extract insights from SharePoint libraries, technical manuals, and financial reports. Legal firms can use it to summarize cases and identify key clauses across thousands of pages.
Multilingual Customer Support
Llama 4 Maverick offers 12-language support, enabling AI assistants that analyze both text and uploaded images for 24/7 technical support. These systems identify product issues from images and provide multilingual troubleshooting guidance.
New Llama 4 Models
Llama 4 Scout and Maverick are Meta's latest AI models, both featuring innovative mixture-of-experts (MoE) architecture but with different capabilities and use cases.
Llama 4 Scout
17B parameters (16 experts) outperforms all previous Llama models and competitors like Gemma 3 and Gemini 2.0 Flash on standard benchmarks. Runs on a single H100 GPU with exceptional 10M context window and Mixture of Experts (MoE), making it the leading multimodal model in its class.
Llama 4 Maverick
17B active parameters with 128 experts surpasses GPT-4o and Gemini 2.0 Flash on key benchmarks, matching DeepSeek v3's reasoning and coding capabilities with less than half the parameters. Delivers unmatched performance-to-cost efficiency in its class.
Get API KeyLlama 4 Behemoth
288B parameter model (16 experts) powers the latest releases through distillation. Already surpassing GPT-4.5 and Claude Sonnet 3.7 on multiple STEM metrics despite ongoing training. This represents the most advanced LLM to date, ranking among the world's most intelligent language models.
Why Choose AI/ML API solution?
AI/ML API provides scalability, faster deployment, and access to 200+ advanced machine learning models without the need for extensive in-house expertise or infrastructure.
Easy To Use
Our API allows seamless integration of powerful AI capabilities into your applications, regardless of your coding experience. Simply swap your API key to begin using the AI/ML API.
Scalable
AI/ML API provides flexibility for business growth since you can scale resources by purchasing more tokens as needed, ensuring optimal performance and cost efficiency
Affordable
We offer flat, predictable pricing, payable by card or cryptocurrency, keeping it the lowest on the market and affordable for everyone.
import os
from openai import OpenAI
client = OpenAI(
base_url="<https://api.aimlapi.com/v1>",
api_key="<YOUR_API_KEY>",
)
response = client.chat.completions.create(
model="meta-llama/llama-4-maverick",
messages=[
{
"role": "system",
"content": "You are an AI assistant who knows everything.",
},
{
"role": "user",
"content": "Tell me, why is the sky blue?"
},
],
)
message = response.choices[0].message.content
print(f"Assistant: {message}")
Getting started with
Llama 4 API
Visit AI Playground to quickly try Llama 4 Scout and Maverick APIs.
For more information about technical features, please refer to the Llama 4 model cards:
• Llama 4 Scout
• Llama 4 Maverick