Meta Unveils Llama 4: A New Era of Flagship AI Models
Meta has launched a new collection of AI models known as Llama 4, building on its previous efforts in AI development. This release features three new models: Llama 4 Scout, Llama 4 Maverick, and Llama 4 Behemoth. Each model has been trained on extensive amounts of unlabeled text, images, and videos to enhance their visual understanding capabilities.
The development of Llama 4 was reportedly accelerated by the impressive performance of open models from the Chinese AI lab DeepSeek. This success led Meta to intensify efforts to adapt and compete, especially as DeepSeek managed to reduce costs associated with running and deploying AI models.
The Scout and Maverick models are available for public use on platforms like Llama.com and through partners such as Hugging Face. In contrast, the Behemoth model is still undergoing training. Meta’s AI-powered assistant, utilized across applications such as WhatsApp and Instagram, has been updated to incorporate Llama 4 in 40 different countries, although its multimodal features are currently restricted to the U.S. in English.
However, the licensing conditions for Llama 4 could raise concerns among developers. Users or businesses located in the European Union are prohibited from using or distributing the models, likely due to strict AI and data privacy regulations within the region. Additionally, companies with more than 700 million monthly active users must seek a special license from Meta to use the models.
According to Meta, the Llama 4 models represent a significant evolution in their AI offerings. They now incorporate a mixture of experts (MoE) architecture, allowing for more efficient computation and task management. For instance, Llama 4 Maverick has an expansive architecture with 400 billion total parameters but activates only 17 billion parameters across 128 expert models.
Internal testing by Meta suggests that Maverick excels in various usage scenarios, outperforming previous flagship models from competitors such as OpenAI and Google in certain coding, reasoning, and creative tasks. Nonetheless, other recent models from competitors still maintain an edge in specific applications.
Scout is particularly noted for its ability to handle document summarization and analyze large codebases, with an impressive context capability of processing up to 10 million tokens. This allows it to manage extremely lengthy documents effectively. The models are capable of running on powerful systems, with specific hardware requirements tailored to their performance levels.
Interestingly, Meta has adjusted Llama 4’s programming to better address sensitive topics and offer a more balanced range of responses compared to previous iterations. This adjustment comes amid criticisms from some political circles about AI chatbots allegedly favoring certain viewpoints, an area that remains a persistent challenge in AI bias.
For more details on the models, visit the official Llama 4 launch page.
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