Meta is breaking from the closed AI playbook with Llama, its open-source generative AI family that developers can download and customize freely. Unlike Google's Gemini or OpenAI's ChatGPT models locked behind APIs, Llama 4's three variants - Scout, Maverick, and the upcoming Behemoth - offer unprecedented flexibility for enterprise deployments and startup innovation.
Meta just redefined the AI battleground with a radical bet on openness. While every other tech giant locks their flagship models behind paywalls and APIs, Meta's Llama family lets developers download, modify, and deploy however they want - with some strategic strings attached.
The latest Llama 4 release in April 2025 crystallizes this strategy with three distinct models targeting different use cases. Scout packs 17 billion active parameters with a massive 10 million token context window - roughly equivalent to processing 80 novels simultaneously. That's enterprise-grade document analysis territory that makes Google's Gemini look constrained.
Maverick takes a different approach with the same 17 billion active parameters but trades context for efficiency with its 1 million token window. "This is our Swiss Army knife," one Meta engineer told developers during the launch. It's designed for the bread-and-butter AI tasks: coding assistants, chatbots, and technical support systems where speed matters more than massive context.
The third model, Behemoth, remains in training but promises to be the heavyweight with 288 billion active parameters across 2 trillion total parameters. Meta's positioning it as the "teacher" for the smaller models - think advanced research and STEM applications where raw computational power trumps efficiency.
What sets Llama apart isn't just the open licensing - it's Meta's infrastructure play. The company has signed up over 25 cloud partners including Nvidia, Databricks, and Snowflake to host Llama instances. This creates a revenue-sharing ecosystem that internal documents reveal generates meaningful income for Meta without direct model sales.
"We're seeing early-stage companies choose Llama specifically because they can modify the training for their domain," says one startup founder who requested anonymity. That flexibility comes with guardrails - companies with over 700 million monthly users need special licensing from Meta, effectively giving the company veto power over major deployments.
The multimodal capabilities represent Meta's first serious challenge to OpenAI's vision leadership. All Llama 4 models handle text, image, and video input natively, trained on what Meta describes as "large amounts of unlabeled text, image, and video data" across 200 languages. The mixture-of-experts architecture - 16 experts for Scout, 128 for Maverick - keeps computational costs manageable while maintaining performance.
