LLaMA 4

• Blockchain Council
• April 12, 2025

Meta has officially released LLaMA 4, its newest generation of open-weight large language models. Announced in April 2025, the LLaMA 4 family introduces substantial architectural upgrades, expanded capabilities, and a broader mission: to make high-performance AI more widely available outside the closed walls of Big Tech platforms.

What Is LLaMA 4?

LLaMA (short for Large Language Model Meta AI) is Meta’s family of foundational AI models. These are trained to understand and generate human-like language, write code, assist with reasoning, and process different types of content.

 

Introducing our first set of Llama 4 models!

We’ve been hard at work doing a complete re-design of the Llama series. I’m so excited to share it with the world today and mark another major milestone for the Llama herd as we release the *first* open source models in the Llama 4… pic.twitter.com/gmXgDw09qN

— Ahmad Al-Dahle (@Ahmad_Al_Dahle) April 5, 2025

LLaMA 4 builds on the momentum of LLaMA 2 and 3, but introduces a fundamentally different approach. It uses a Mixture of Experts (MoE) architecture — a model design where only a subset of the model’s internal “experts” are activated at any time. This allows LLaMA 4 to increase efficiency without sacrificing performance, making it easier to deploy in the real world.

LLaMa 4 will ultimately consider the integration of AI agents. For coders and non-coders alike, the Certified Agentic AI Expert™ is a great starting point. 

Meet the Models: Scout, Maverick, and Behemoth

Meta has introduced three models under the LLaMA 4 umbrella:

1. LLaMA 4 Scout

A lightweight model designed for speed and affordability. Scout has 17 billion active parameters and uses 16 experts per inference. It can run on a single Nvidia H100 GPU, making it attractive to independent developers and small teams. It supports a context window of 10 million tokens — allowing it to “remember” much more than typical models during a single session.

2. LLaMA 4 Maverick

This is the flagship model. Maverick also uses 17 billion active parameters, but with 128 experts to improve reasoning and code generation performance. It’s aimed at enterprise use cases and research environments where advanced logic, contextual understanding, and multi-step thinking are key.

3. LLaMA 4 Behemoth (Coming Soon)

Still in development, Behemoth is expected to exceed 2 trillion total parameters and will act as the most powerful version in the series. Meta has not yet confirmed a release date, but early information suggests it will power future enterprise integrations and Meta’s internal products.

What Meta Says About It

Meta CEO Mark Zuckerberg said in April:

“LLaMA 4 will be natively multimodal — it’s an omni-model and will have agentic capabilities, so it’s going to be novel and it’s going to unlock a lot of new use cases.”

In an investor call, he emphasized:

“Our goal is to build the world’s leading AI, open source it, and make it universally accessible so that everyone in the world benefits.”

These statements reflect Meta’s broader strategy: lead in AI while keeping one foot in the open-source community — even if licensing terms remain a point of debate.

Multimodal and Multilingual Capabilities

Unlike many open models before it, LLaMA 4 was designed to be natively multimodal. This means it can process different types of data — not just text, but also images, and soon, audio and video. Developers can use it for complex applications like:

• Visual question answering
• Image captioning
• Content moderation
• Code generation
• Language translation

LLaMA 4 also supports multiple languages, improving its global applicability for multilingual users and enterprises.

Performance: Is It Better Than GPT-4o or Gemini?

Meta claims that Maverick performs competitively with GPT-4o and Google Gemini Flash 1.5 on several benchmarks. It scored highly on LMArena and other industry-standard leaderboards.

However, controversy followed when Meta admitted the benchmark submission was based on an experimental fine-tuned version of Maverick not yet available to the public. While the performance is still impressive, this raised concerns over transparency and benchmark reliability — a challenge all AI developers are grappling with.

That said, early user tests show that LLaMA 4 models — especially Scout — offer exceptional value in terms of performance per dollar. It’s faster than previous generations, consumes fewer GPU resources, and handles longer context with ease.

How to Use LLaMA 4?

Meta has made Scout and Maverick available across several platforms:

• Hugging Face
• AWS SageMaker JumpStart
• Google Cloud (coming soon)
• Microsoft Azure (under review)

The models can be used under Meta’s LLaMA 4 license, which permits research and commercial use — except for companies with over 700 million monthly active users, which must request special licensing.

That clause has sparked debate in the open-source community. Some believe it conflicts with open-source values; others argue it’s a reasonable step to prevent misuse by AI-heavy competitors.

Real-World Developer Reaction

Developers are already testing LLaMA 4 in the wild. Here’s what some are saying:

 

Alright guys, hear me out

I was skeptical about Llama 4 coding skills… until I started comparing it to other models, including the earlier version of GPT-4o

This thing is free, open source, and honestly pretty close to GPT-4o (pre-update), wild if you think about it pic.twitter.com/yuMZjn4Nkp

— Flavio Adamo (@flavioAd) April 6, 2025

 

the new llama 4 models are so advanced that they require versions of hf transformers that haven’t even been invented yet pic.twitter.com/iDBpnFSlnS

— will brown (@willccbb) April 5, 2025

These perspectives highlight what Meta may have gotten right: building a serious model that doesn’t demand massive resources.

What Can You Actually Build With It?

Some practical ideas developers are exploring today:

• Chatbots with longer memory
• Visual document analysis tools
• Code review assistants
• Language tutors
• Knowledge base search engines

Because LLaMA 4 is downloadable, teams can build fully private AI applications — something that isn’t possible with GPT-4o or Gemini unless you go through a cloud provider.

Conclusion

LLaMA 4 is a calculated move by Meta to democratize AI while still competing with the best in the industry. The models are efficient, flexible, and built with a forward-looking architecture that emphasizes modularity and performance.

For developers and organizations looking for an open alternative to closed models like GPT-4o, LLaMA 4 offers a compelling balance of speed, performance, and accessibility.

It’s not perfect. Licensing limitations and benchmark debates are real issues. But Meta’s direction is clear — and with the upcoming Behemoth model and tools like Meta AI Assistant integrating LLaMA 4 under the hood, it’s a model worth paying attention to in 2025 and beyond.