Meta releases Llama 4, a new crop of flagship AI models

Summary

Meta has released a new collection of AI models, Llama 4, in its Llama family — on a Saturday, no less. There are four new models in total: Llama 4 Scout, Llama 4 Maverick, and Llama 4 Behemoth. All were trained on “large amounts of unlabeled text, image, and video data” to give them “broad visual understanding,” Meta says. The success of open models from Chinese AI lab DeepSeek, which perform on par or better than Meta’s previous flagship Llama models, reportedly kicked Llama development into overdrive. Meta is said to have scrambled war rooms to decipher how DeepSeek lowered the cost of running and deploying models like R1 and V3. Scout and Maverick are openly available on Llama.com and from Meta’s partners, including the AI dev platform Hugging Face, while Behemoth is still in training. Meta says that Meta AI, its AI-powered assistant across apps including WhatsApp, Messenger, and Instagram, has been updated to use Llama 4 in 40 countries. Multimodal features are limited to the U.S. in English for now. Some developers may take issue with the Llama 4 license. Users and companies “domiciled” or with a “principal place of business” in the EU are prohibited from using or distributing the models, likely the result of governance requirements imposed by the region’s AI and data privacy laws. (In the past, Meta has decried these laws as overly burdensome.) In addition, as with previous Llama releases, companies with more than 700 million monthly active users must request a special license from Meta, which Meta can grant or deny at its sole discretion. “These Llama 4 models mark the beginning of a new era for the Llama ecosystem,” Meta wrote in a blog post. “This is just the beginning for the Llama 4 collection.” Image Credits:Meta Meta says that Llama 4 is its first cohort of models to use a mixture of experts (MoE) architecture, which is more computationally efficient for training and answering queries. MoE architectures basically break down data processing tasks into s...