Meta’s Llama is a family of open generative AI models designed to empower developers with flexible, accessible tools for building AI applications. Unlike closed models from competitors such as OpenAI, Google, or Anthropic, Llama is released with open weights, allowing developers to download, use, and fine-tune the models freely—subject to Meta’s licensing terms. This openness fosters innovation and choice, especially for startups and researchers. The latest iteration, Llama 4, launched in April 2025, includes three distinct models: Scout, Maverick, and Behemoth. Llama 4 Scout features a massive 10-million-token context window—equivalent to about 80 novels—making it ideal for analyzing long documents or complex workflows. Maverick offers a 1-million-token context and balances reasoning, speed, and efficiency, suitable for coding, chatbots, and technical assistance. Behemoth, with 16 experts in its mixture-of-experts (MoE) architecture, is designed for advanced research, model distillation, and STEM tasks. All Llama 4 models are natively multimodal, trained on vast amounts of text, image, and video data across 200 languages. Llama 4 builds on the success of Llama 3.1 and 3.2, widely adopted for instruction-tuned applications and cloud deployment. The models can perform tasks like coding, math problem-solving, multilingual summarization (12+ languages), and processing PDFs and spreadsheets. They also support integration with external tools—such as Brave Search for up-to-date information, Wolfram Alpha for scientific queries, and a Python interpreter for code validation—though these require manual setup. Llama is accessible through multiple channels. It powers Meta AI across Facebook, WhatsApp, Instagram, Oculus, and Meta.ai in 40 countries, with fine-tuned versions available in over 200 regions. Developers can access Llama 4 models via Llama.com, Hugging Face, and more than 25 cloud partners including AWS, Google Cloud, Microsoft Azure, Nvidia, and Snowflake. While Meta doesn’t sell direct access, it earns revenue through revenue-sharing agreements with hosting platforms. Additionally, Meta launched the Llama for Startups program in May 2025, offering technical support and potential funding to early-stage companies. To enhance safety, Meta provides a suite of tools: Llama Guard detects harmful content like hate speech, self-harm, or illegal activity; Prompt Guard defends against jailbreak attempts and malicious inputs; Llama Firewall blocks prompt injection and insecure tool use; and Code Shield helps prevent insecure code generation across seven programming languages. CyberSecEval serves as a benchmark to assess model risks related to cyber threats and social engineering. Despite its strengths, Llama has limitations. Multimodal capabilities remain primarily English-focused. The models were trained on large datasets, including pirated e-books—though a federal judge ruled this as fair use in a recent copyright case. Still, users risk liability if they reproduce copyrighted content generated by Llama. Meta also trains AI on user content from Facebook and Instagram, with limited opt-out options, raising privacy concerns. Code generation remains a challenge: Llama 4 Maverick scored only 40% on LiveCodeBench, far below GPT-5 (85%) and Grok 4 Fast (83%), indicating a higher risk of bugs or vulnerabilities. As with all generative AI, Llama can produce plausible but false information—especially in legal, medical, or emotional contexts—making human review essential. In summary, Llama represents a major step toward open, flexible AI development, combining powerful capabilities with broad accessibility, while still requiring careful oversight to manage risks.