Meta’s LLaMA: A Comprehensive Guide to Meta’s Open Generative AI Model

Meta, like other tech giants, has developed its own flagship generative AI system, LLaMA. Unlike many other AI models, LLaMA is open, meaning developers can download and utilize it freely within certain restrictions. This contrasts with models such as Anthropic’s Claude, Google’s Gemini, xAI’s Grok, and most versions of OpenAI’s ChatGPT, which are primarily accessible through APIs.

To provide developers with flexibility, Meta has partnered with major cloud providers, including AWS, Google Cloud, and Microsoft Azure, offering hosted versions of LLaMA. In addition, Meta publishes a suite of tools, libraries, and guides in its LLaMA Cookbook to assist developers in fine-tuning, evaluating, and adapting the models. With the latest generations — LLaMA 3 and LLaMA 4 — these capabilities now include native multimodal support and expanded cloud availability.

This article delves into everything about Meta’s LLaMA, from its features and editions to deployment options, while keeping track of Meta’s evolving developer support.

What is LLaMA?

LLaMA is a family of models rather than a single system. The latest iteration, LLaMA 4, was released in April 2025 and currently includes three models:

• Scout: 17 billion active parameters, 109 billion total parameters, and a context window of 10 million tokens.

• Maverick: 17 billion active parameters, 400 billion total parameters, and a context window of 1 million tokens.

• Behemoth: Upcoming, expected to have 288 billion active parameters and 2 trillion total parameters.

Note: In AI, tokens are units of raw data, like the syllables “fan,” “tas,” and “tic” in the word “fantastic.” A model’s context window refers to how much input data it can consider before generating output. Longer context helps models maintain coherence across large datasets but may also weaken some safety guardrails, occasionally leading to flawed reasoning.

For perspective, LLaMA 4 Scout’s 10 million token window roughly equals 80 average novels, while Maverick’s 1 million token window is about eight novels. All LLaMA 4 models were trained on “large amounts of unlabeled text, image, and video data” and 200 languages, giving them broad visual and linguistic understanding.

Scout and Maverick are Meta’s first open-weight natively multimodal models, built using a mixture-of-experts (MoE) architecture, which optimizes computational efficiency. Scout contains 16 experts, Maverick 128, and Behemoth will have 16 experts serving as a teacher model for the smaller versions.

LLaMA 4 builds on LLaMA 3, which included the 3.1 and 3.2 models widely used for instruction-tuned applications and cloud deployment.

Capabilities of LLaMA

LLaMA supports a variety of tasks including coding, math problem-solving, and document summarization in at least 12 languages: Arabic, English, German, French, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese.

• Scout: Optimized for long workflows and large-scale data analysis.

• Maverick: A generalist model balancing speed and reasoning, suitable for coding, chatbots, and technical assistance.

• Behemoth: Geared toward advanced research, model distillation, and STEM applications.

LLaMA models can integrate with third-party tools such as Brave Search for up-to-date queries, Wolfram Alpha for science and math, and a Python interpreter for code validation. However, these require explicit configuration and are not automatically enabled.

Deployment and Access

For casual use, LLaMA powers Meta AI chatbots on Facebook Messenger, WhatsApp, Instagram, Oculus, and Meta.ai across 40 countries. Fine-tuned versions are used in Meta AI services in over 200 countries and territories.

LLaMA 4 Scout and Maverick are available via LLaMA.com and developer platforms such as Hugging Face. Behemoth is still in development. Meta’s models can be used and fine-tuned across major cloud platforms, with more than 25 partners, including Nvidia, Databricks, Groq, Dell, and Snowflake.

Revenue is generated through revenue-sharing agreements with model hosts, rather than direct sales. Some partners provide additional services, enabling models to reference proprietary data or reduce latency.

Licensing restrictions apply: developers with apps exceeding 700 million monthly users must request a special license from Meta.

In May 2025, Meta launched LLaMA for Startups, offering startup support, access to funding, and guidance from Meta’s LLaMA team.

Meta’s Developer Tools

Meta provides several tools to improve safety and security:

• LLaMA Guard: Moderation framework for potentially harmful content.

• Prompt Guard: Protects against prompt-injection attacks.

• CyberSecEval: Security risk assessment benchmarks.

• LLaMA Firewall: Detects insecure code and risky tool interactions.

• Code Shield: Filters insecure code in seven programming languages.

LLaMA Guard addresses content including criminal activity, child exploitation, copyright violations, hate, self-harm, and sexual abuse. Developers can customize blocked categories for all supported languages.

Prompt Guard focuses on preventing malicious prompts designed to bypass safety filters, while LLaMA Firewall and Code Shield help safeguard coding and AI tool usage. CyberSecEval provides a set of security benchmarks evaluating risks such as automated social engineering and scaling offensive cyber operations.

Limitations and Risks

Despite its capabilities, LLaMA has limitations:

• Multimodal features are currently mostly English-based.

• Training data included pirated e-books and articles; a federal judge ruled this as fair use, but usage of copyrighted output may still incur liability.

• Meta’s models are also trained on Instagram and Facebook content, with limited opt-out options.

• Programming output may be buggy or insecure. On LiveCodeBench, LLaMA 4 Maverick scored 40%, compared to 85% for OpenAI GPT-5 High and 83% for xAI Grok 4 Fast.

All AI models, including LLaMA, may produce plausible but false information, whether in code, legal advice, or conversational scenarios.

Author: Din Kumar