Meta Unveils Llama 4: A Natively Multimodal Open LLM Family

April 5, 2025

The Llama 4 herd

The landscape of artificial intelligence is evolving rapidly! Today, Meta unveils Llama 4, its groundbreaking next-generation family of open large language models (LLMs). Designed for developers, researchers, and AI enthusiasts, Llama 4 represents a major leap forward with its native multimodality, enabling AI applications that understand and process information more like humans do – by integrating different data types seamlessly. Get ready to explore a new frontier in AI development!

Llama 4 Explained: What is Native Multimodality?

A key innovation in Llama 4 is native multimodality. But what does that mean?

• Multimodality: This refers to an AI model's ability to understand, interpret, and generate content across multiple types of data, such as text and images.

• Native Integration & Early Fusion: Unlike models where different data types might be handled separately and combined later, Llama 4 uses early fusion. This means text tokens and vision tokens (the basic units of information the model processes) are integrated and processed together deep within the model's core architecture right from the start. It's built natively for multimodal understanding, trained on datasets including numerous images.

This approach allows Llama 4 to have a more holistic and contextual grasp when dealing with combined text and visual information.

Meet the Llama 4 Models: Scout, Maverick, and Behemoth

The Llama 4 release includes a family, or "herd," of models, featuring different strengths:

• Llama 4 Scout: 10M Token Context & MoE Efficiency 

  • Parameters: 17 billion active parameters.

  • Architecture: Uses a Mixture-of-Experts (MoE) approach with 16 experts. MoE allows the model to intelligently route tasks to specialized internal networks, enhancing efficiency.

  • Standout Feature: 10 Million Token Context Window! A token is a piece of text or code (roughly a word or sub-word). A context window is the amount of information the model can consider at once. Llama 4 Scout's massive 10M token window is industry-leading, enabling applications to process and retain context from incredibly long inputs – think entire codebases, extensive research papers, or long-running dialogues.

    
    

• Llama 4 Maverick: High Specialization with 128 Experts 

  • Parameters: Also 17 billion active parameters.

  • Architecture: Significantly increases the expert count to 128 within its MoE framework. This suggests Maverick is optimized for tasks requiring highly nuanced understanding and fine-grained specialization.

    
    

• Learning from Llama 4 Behemoth: The Power of Distillation 

  • Scout and Maverick achieve their impressive capabilities partly through distillation. They were trained to capture the knowledge and performance characteristics of Llama 4 Behemoth, a much larger 288 billion parameter "teacher" model (also with 16 experts). This allows developers to leverage state-of-the-art power in more accessible model sizes.

    
    

Benefits of Llama 4 for Developers and AI Projects

This new generation of open AI models unlocks significant advantages:

• Building Advanced Multimodal Applications: Seamlessly combine text and image inputs/outputs. Potential use cases include: analysing UI screenshots with bug reports, generating product descriptions from images, creating data visualizations from text prompts, and more interactive educational tools.

• Handling Large-Scale Data & Complex Tasks: The 10 million token context window in Llama 4 Scout directly addresses challenges with long-form content. This is crucial for analysing legacy code, understanding complex documentation, performing in-depth literature reviews, or building chatbots that remember detailed conversation history.

• Efficient AI Power: Leverage the performance benefits learned from the giant Behemoth model via distillation, combined with the potential efficiency gains of the MoE architecture in Scout and Maverick.

  
  

How to Get Started with Llama 4 Models

Ready to dive in and explore the capabilities of Llama 4?

• Download Models: Meta has made Llama 4 Scout and Llama 4 Maverick available for download for research and commercial use (check license details).

• Find Resources: Access model weights and further technical information via the official Meta AI announcement.

• Stay Tuned: Look out for upcoming integrations across major cloud platforms, edge silicon providers, and AI service integrators.

  
  

The Future is Multimodal with Llama 4

Llama 4 represents a significant step towards more versatile, contextually aware, and powerful open AI models. By embracing native multimodality and pushing the boundaries of context length, Meta is empowering the developer community to build the next wave of innovative AI applications.

Further Resources:

• Official Meta AI Announcement: https://ai.meta.com/blog/llama-4-multimodal-intelligence/

• Llama 4: https://www.llama.com/docs/model-cards-and-prompt-formats/llama4_omni/

• Hugging Face: https://huggingface.co/blog/llama4-release