Meta Llama 4 Ushers in Native Multimodal AI Era with 10M Token Context

Native Multimodal Innovation

Meta has set a new milestone in the AI industry with the announcement of the Llama 4 series. Llama 4 Scout and Llama 4 Maverick are the first open-weight natively multimodal models, designed from the ground up to process text, images, and video in an integrated manner.

Llama 4 Maverick: 17B Parameter Powerhouse

Llama 4 Maverick is Meta's first model using a Mixture-of-Experts (MoE) architecture, with 17 billion active parameters and 128 experts.

It beats GPT-4o and Gemini 2.0 Flash across a broad range of widely reported benchmarks, proving itself as the best multimodal model in its class.

Llama 4 Scout: 10 Million Token Context

Llama 4 Scout dramatically increases the supported context length from 128K tokens in Llama 3 to an industry-leading 10 million tokens. This means it can process hundreds of pages of documents, hours of video content, or massive codebases in a single context.

Significance of Open-Weight Strategy

Meta has released Llama 4 as an open-weight model, allowing researchers and developers to freely use and improve it. This represents a major differentiator in terms of transparency and accessibility compared to commercial closed models (GPT, Claude, Gemini).

Impact on AI Ecosystem

The arrival of Llama 4 signifies the democratization of multimodal AI. Multimodal capabilities previously available only from major tech companies like OpenAI, Google, and Anthropic are now accessible to anyone for use and customization.

The introduction of MoE architecture is also significant for efficiency. It reduces computational costs by activating only necessary experts while maintaining performance.