10M Tokens and Native Multimodality: How Llama 4 Breaks the Mold
The launch of Llama 4 marks a defining moment for Meta.
For the first time, Meta has delivered an open-weight model family—Scout, Maverick, and the massive Behemoth—built from the ground up for true multimodal intelligence.
Unlike traditional models that add multimodal features after training, Llama 4 is designed for native multimodality from the start, using a single architecture to process text, images, and video together.
Powered by a mixture-of-experts (MoE) architecture and a record 10 million token context window, Llama 4 holds longer conversations, processes more information at once, and achieves impressive results in coding, reasoning, multilingual tasks, and STEM benchmarks.
Llama 4 models were trained on over 30 trillion tokens, more than double the training corpus used for Llama 3.
In this newsletter, we’ll explore:
What makes Llama 4 unique
A closer look at the Llama 4 model family: Scout, Maverick, and Behemoth
3 use cases that showcase Llama 4’s capabilities
The innovations behind Llama 4’s massive training run and deployment-ready performance
Let's get started.
