Overview
The Llama 4 family, released by Meta in April 2025, represents a significant leap forward in open-source AI. Instead of a single model, Llama 4 is a suite of specialized, multimodal models designed to excel at different tasks, from efficient, long-context understanding to state-of-the-art reasoning and coding. The family is built on a Mixture-of-Experts (MoE) architecture, allowing for massive scale while maintaining computational efficiency.
The two primary models released are Llama 4 Scout and Llama 4 Maverick, with a third, much larger model named Llama 4 Behemoth still in training.
Llama 4 Scout
Scout is the highly efficient, multimodal model in the family. With 17 billion active parameters, it is designed to be a nimble yet powerful tool for tasks requiring long-context understanding, reasoning, and image analysis.
- Context Window: 10 million tokens
- Performance: Outperforms comparable models like Gemma 3, Gemini 2.0 Flash-Lite, and Mistral 3.1.
- Best for: Long-document analysis, RAG systems, and efficient multimodal tasks.
Llama 4 Maverick
Maverick is the powerhouse of the currently available models. While it also has 17 billion active parameters, it utilizes 128 experts for a total of 400 billion parameters, making it a formidable competitor to top proprietary models.
- Performance: Aims to outperform models like GPT-4o, Gemini 2.0 Flash, and DeepSeek v3.1 in reasoning and coding.
- Best for: Complex problem-solving, advanced code generation, and high-fidelity multimodal understanding.
Llama 4 Behemoth
Behemoth is Meta's frontier model, with 288 billion active parameters and a total size of nearly 2 trillion. While its public release was delayed for further training, it is positioned to compete with the most powerful models ever built, like GPT-4.5.
Capabilities
- State-of-the-Art Reasoning: Maverick, in particular, is designed to be a leader in complex logical and mathematical reasoning.
- Advanced Multimodality: All models can natively process and understand both text and images.
- Massive Context Windows: Scout's 10 million token context window opens up new possibilities for long-form content analysis.
- Open Source: The models are freely available for both research and commercial applications, driving innovation across the AI community.
Use Cases
- Complex Coding & Development: Maverick serves as a powerful pair programmer for developers.
- Large-Scale Data Analysis: Scout can analyze entire codebases or vast quantities of documents in a single prompt.
- Creative Multimodal Content: All models can be used to understand and interact with visual information.
- Academic & Commercial Research: The open nature of the models makes them a go-to choice for researchers pushing the boundaries of AI.
Pricing & Access
- Open Source: The Llama 4 Scout and Maverick models are free to download and use. They are available on the official Llama website and Hugging Face.
- Hosting: Pricing depends on the cloud provider or service used for hosting the models.
Ecosystem & Tools
- Official Llama Website: The primary source for downloading the models and reading the documentation.
- Hugging Face: The main community hub for accessing and fine-tuning Llama models.
- API Access: Available through major cloud providers and dedicated AI model hosting platforms.