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# Meta Unveils Llama 4 Scout and Maverick: Open-Weight Multimodal Models Built for Performance and Scale
- URL: https://www.theleftshift.com/meta-unveils-llama-4-scout-and-maverick-open-weight-multimodal-models-built-for-performance-and-scale/
- Published: 2025-04-06T05:55:16.000Z
- Updated: 2025-04-06T05:55:16.000Z
- Description: Both models were distilled from Llama 4 Behemoth, a larger unreleased model with 288 billion active parameters.
- Author: The Left Shift Bureau
- Tags: Big Tech, #a42

Meta has released two new open-weight multimodal AI models: Llama 4 Scout and Llama 4 Maverick. These models are available for download on llama.com and Hugging Face, and can be accessed through Meta AI on WhatsApp, Messenger, Instagram Direct, and the Meta AI website.

Llama 4 Scout is a 17 billion active parameter model built with 16 experts using a mixture-of-experts (MoE) architecture. It fits on a single H100 GPU and supports a 10 million token context window, enabling tasks like multi-document summarization and reasoning over large codebases. Meta says Scout outperforms models like Gemma 3 and Gemini 2.0 Flash-Lite, while remaining more efficient and scalable than previous Llama versions.

Llama 4 Maverick, also with 17 billion active parameters, includes 128 experts and 400 billion total parameters. It's intended for higher-end applications and rivals much larger models such as DeepSeek V3, with strong performance on reasoning and coding tasks. Meta says Maverick outperforms GPT-4o and Gemini Flash 2 on several benchmarks.

"Whether you’re a developer building on top of our models, an enterprise integrating them into your workflows, or simply curious about the potential uses and benefits of AI, Llama 4 Scout and Llama 4 Maverick are the best choices for adding next-generation intelligence to your products," Meta said in a[ blog post](https://ai.meta.com/blog/llama-4-multimodal-intelligence/?ref=theleftshift.com). 

Both models were distilled from Llama 4 Behemoth, a larger unreleased model with 288 billion active parameters. Behemoth has already shown leading results on STEM tasks and influenced Scout and Maverick through a process called codistillation.

The models introduce architectural changes such as interleaved attention layers and training on diverse multimodal data. Meta used a streamlined fine-tuning process, reinforcement learning, and preference optimization to improve performance.

More announcements are expected at LlamaCon on April 29, including details on an upcoming Llama 4 reasoning model.