Meta launches Llama 4 AI models, marking a major milestone in the company’s rapidly evolving artificial intelligence strategy. The new collection—Llama 4 Scout, Llama 4 Maverick, and Llama 4 Behemoth—represents Meta’s most powerful open-source models to date. Built using a mixture of text, image, and video training data, these models are designed to push the boundaries of general intelligence, coding, reasoning, and multimodal AI tasks. And perhaps most surprisingly, Meta dropped this bombshell update on a Saturday—underscoring the urgency and intensity behind its latest AI rollout.

Meta’s timing wasn’t random. The tech giant is feeling the heat from competitors, particularly OpenAI, Google, Anthropic, and up-and-coming players like China’s DeepSeek. DeepSeek’s recent successes with its R1 and V3 models reportedly forced Meta to speed up Llama development. Internal teams were assembled to reverse-engineer DeepSeek’s strategies, focusing on how the Chinese lab managed to lower inference costs while maintaining top-tier performance.

Inside the Llama 4 Model Lineup

The Llama 4 AI model family consists of three key releases—two now publicly available and one still in development.

• Llama 4 Scout: Designed for deep document summarization, code analysis, and long-context reasoning, Scout boasts a whopping 10 million-token context window, making it capable of handling entire codebases or book-length documents in a single session. It features 17 billion active parameters and is optimized to run efficiently on a single NVIDIA H100 GPU.
• Llama 4 Maverick: Positioned as Meta’s premier general-purpose assistant, Maverick handles everything from creative writing to multilingual question answering and code generation. With 400 billion total parameters and 17 billion active ones across 128 experts, it rivals some of the best in the field—outperforming GPT-4o and Gemini 2.0 in many Meta-internal benchmarks.
• Llama 4 Behemoth: Still undergoing training, Behemoth is being hailed as a future juggernaut. Featuring nearly 2 trillion total parameters and 288 billion active ones, it’s designed for complex STEM reasoning, outperforming even GPT-4.5 and Claude 3.7 Sonnet in certain tasks, according to Meta’s own tests. However, it slightly trails behind Gemini 2.5 Pro on select evaluations.

The Power of MoE (Mixture of Experts)

What makes this generation of models unique is their underlying Mixture of Experts (MoE) architecture. Rather than activating the entire neural network for every prompt, Llama 4 selectively routes tasks to specialized “experts” depending on the input. This drastically reduces the computational load and speeds up response times, especially in large-scale use cases.

This architectural innovation allows models like Maverick to scale efficiently without requiring astronomical computing resources. It also makes the models more flexible for real-time applications across Meta’s ecosystem, including WhatsApp, Messenger, and Instagram.

Accessibility, Licensing & Geo Restrictions

Despite the open-source claim, access to Llama 4 isn’t without caveats. Meta has restricted usage across the European Union, citing ongoing compliance challenges with the EU AI Act and GDPR. Additionally, any organization with more than 700 million monthly active users must seek Meta’s special permission to use or distribute the models—permission that can be denied at Meta’s discretion.

Developers and businesses can access Scout and Maverick via Llama.com, Hugging Face, and other Meta partners. However, full multimodal support is currently limited to English-speaking users in the United States.

More Balanced Answers—and More Controversial Ones

One of the most notable changes with the Llama 4 AI models is their approach to political and social questions. Unlike previous models that often refused to engage with controversial topics, Llama 4 is tuned to offer balanced, fact-based responses across a wider range of debates. According to Meta, this was a deliberate choice to increase user trust and transparency.

“We’re continuing to make Llama more responsive so that it answers more questions, can respond to a variety of different viewpoints, and doesn’t favor some views over others,” a Meta spokesperson stated.

This tweak comes in response to increasing political pressure, particularly from conservative circles in the U.S., who have criticized AI systems as being too “woke.” By adjusting its refusal mechanisms, Meta aims to make its models more inclusive and representative of broader societal discourse.

Looking Ahead: What’s Next for Llama?

Meta’s unveiling of Llama 4 AI models marks the start of what the company calls a “new era” for open-source AI. In addition to further fine-tuning Behemoth, Meta is expected to continue iterating on reasoning models—those capable of verifying their outputs and generating highly accurate, trustable answers.

Future models may incorporate advanced reasoning, improved multilingual understanding, real-time multimodal input processing, and even further optimization for edge devices and mobile deployment.

In Meta’s own words:

“This is just the beginning for the Llama 4 collection.”

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