LLaMA 4 Unveiled: Meta’s Latest AI Model Explained

Artificial Intelligence (AI) is evolving at a breakneck pace, and Meta is determined to stay at the forefront of this revolution. On April 5, 2025, Meta unveiled LLaMA 4, the latest iteration of its open-source large language model (LLM) series, and it’s already making waves in the tech world. Designed to push the boundaries of AI capabilities, LLaMA 4 introduces groundbreaking features like a mixture-of-experts (MoE) architecture, a jaw-dropping 10-million-token context window, and multimodal functionality. Whether you’re a developer, researcher, or just an AI enthusiast, this release is a big deal—and here’s everything you need to know about it.

In this blog post, we’ll dive deep into what LLaMA 4 is, its key features, how it compares to competitors like OpenAI’s GPT-4o and Google’s Gemini 2.0, and why Meta’s open-source approach continues to shake up the AI landscape. Let’s get started!

What Is LLaMA 4?

LLaMA (Large Language Model Meta AI) is Meta’s family of AI models, first introduced in 2023, aimed at advancing research and practical applications. Unlike many commercial AI models locked behind paywalls, LLaMA has always been about accessibility—offering powerful tools to developers and researchers for free (with some licensing caveats). LLaMA 4 is the latest chapter in this story, released on April 5, 2025, and it’s packed with upgrades that make it Meta’s most ambitious model yet.

This time, Meta has launched two ready-to-use models—LLaMA 4 Scout and LLaMA 4 Maverick—while teasing a third, LLaMA 4 Behemoth, which is still in training. These models power Meta AI, the company’s assistant integrated across WhatsApp, Messenger, Instagram, and the web at meta.ai. According to Meta, LLaMA 4 isn’t just an incremental update—it’s a leap forward in performance, efficiency, and versatility.

So, what’s driving this hype? Let’s break it down.

Key Features of LLaMA 4

Meta has pulled out all the stops with LLaMA 4, introducing features that set it apart from its predecessors and competitors. Here’s what makes this release special:

1. Mixture of Experts (MoE) Architecture

For the first time, LLaMA 4 adopts a Mixture of Experts (MoE) approach. Unlike traditional models that use all their parameters for every task, MoE splits the workload across specialized “expert” sub-models, activating only the relevant ones for a given query. This makes LLaMA 4 incredibly efficient—delivering top-tier performance with fewer active parameters.

• LLaMA 4 Scout: 17 billion active parameters, 109 billion total, with 16 experts.
• LLaMA 4 Maverick: 17 billion active parameters, 400 billion total, with 128 experts.
• LLaMA 4 Behemoth (unreleased): 288 billion active parameters, nearly 2 trillion total, with 16 experts.

This efficiency doesn’t just save compute power—it also means these models can run on less beefy hardware, making them more accessible to a wider audience.

2. A Massive 10-Million-Token Context Window

One of LLaMA 4’s standout features is its context window—the amount of data it can process at once. LLaMA 4 Scout boasts an unprecedented 10-million-token context window, dwarfing most industry standards (for reference, GPT-4’s context is around 128,000 tokens). In plain English, this means Scout can handle millions of words or massive documents in one go—perfect for tasks like summarizing lengthy reports, analyzing giant codebases, or reasoning over complex datasets.

Maverick and Behemoth are expected to follow suit with large context windows, though exact figures for them are still under wraps. This feature alone makes LLaMA 4 a game-changer for long-context tasks.

3. Multimodal Capabilities

LLaMA 4 isn’t just about text—it’s multimodal, meaning it can process images, text, and potentially other data types like video (details on video are still emerging). This “early fusion” approach integrates multiple data types during training, allowing the model to reason across modalities seamlessly. Imagine asking Meta AI to analyze a photo and write a description, or feeding it a PDF with charts and getting a detailed breakdown. That’s the power of LLaMA 4’s multimodal design.

For now, multimodal features are limited to English-speaking users in the U.S., but Meta plans to expand this soon.

4. Open-Source Accessibility

True to its roots, LLaMA 4 is open-source—available for download on platforms like Hugging Face and Meta’s official site (ai.meta.com). Scout and Maverick are ready now, while Behemoth will follow once training wraps up. However, there’s a catch: the license restricts use by companies with over 700 million monthly active users (unless they get special permission), and EU-based entities face regulatory hurdles. Still, for most developers and researchers, this is a goldmine of free AI power.

5. Performance That Rivals the Best

Meta claims LLaMA 4 outperforms heavyweights like GPT-4o, Gemini 2.0, and even DeepSeek-V3 on key benchmarks—coding, reasoning, multilingual tasks, and more. Maverick, for instance, uses less than half the active parameters of some rivals yet delivers comparable results. Behemoth, once released, is touted as “one of the smartest LLMs in the world,” potentially outshining GPT-4.5 and Claude 3.7 Sonnet in STEM-focused tests.

The Models: Scout, Maverick, and Behemoth

LLaMA 4 isn’t one model—it’s a trio, each tailored for different needs:

LLaMA 4 Scout

• Parameters: 17 billion active, 109 billion total.
• Context Window: 10 million tokens.
• Strengths: Document summarization, large-scale code reasoning, and long-context tasks.
• Hardware: Runs on a single Nvidia H100 GPU—pretty accessible for a model this powerful.

Scout is the lightweight champ, ideal for developers who need efficiency without sacrificing capability. Its massive context window makes it a standout for niche applications.

LLaMA 4 Maverick

• Parameters: 17 billion active, 400 billion total.
• Context Window: Not fully disclosed, but likely large.
• Strengths: General-purpose assistance, creative writing, coding, and multilingual tasks.
• Hardware: Requires an Nvidia H100 DGX system or equivalent.

Maverick is the all-rounder, competing head-to-head with GPT-4o and Gemini 2.0. It’s perfect for chatbots, creative projects, and more robust applications.

LLaMA 4 Behemoth (Coming Soon)

• Parameters: 288 billion active, nearly 2 trillion total.
• Context Window: Expected to be significant.
• Strengths: STEM problem-solving, advanced reasoning (once released).
• Hardware: Needs serious compute power—think multiple high-end GPUs.

Behemoth is Meta’s crown jewel, still in training but already hyped as a “teacher” for future models. It’s poised to redefine what open-source AI can do.

LLaMA 4 vs. Previous Generations

How Does LLaMA 4 Compare to GPT-4o and Gemini 2.0?

The AI race is heating up, and LLaMA 4 is throwing punches at the big players. Here’s how it stacks up:

• Performance: Meta’s internal tests show Maverick beating GPT-4o and Gemini 2.0 in coding, reasoning, and multilingual benchmarks. Scout excels in long-context tasks, an area where competitors lag. Behemoth promises to leapfrog even newer models like GPT-4.5.
• Efficiency: Thanks to MoE, LLaMA 4 uses fewer active parameters, making it less resource-hungry than GPT-4o’s rumored 1 trillion-plus parameters.
• Cost: LLaMA 4 is free (with licensing terms), while GPT-4o and Gemini 2.0 are locked behind OpenAI and Google’s paywalls.
• Reasoning: Unlike OpenAI’s o1, LLaMA 4 isn’t a dedicated reasoning model—it prioritizes speed over step-by-step fact-checking. This trade-off suits most use cases but may limit it in hyper-precise scenarios.

In short, LLaMA 4 offers a compelling alternative—powerful, efficient, and free—though it’s not without quirks (like its restrictive license).

Why Meta’s Open-Source Strategy Matters

Meta CEO Mark Zuckerberg has long championed open-source AI, likening LLaMA to Linux—a tool that empowers everyone, not just tech giants. With over 650 million downloads across the LLaMA family, this approach is paying off. Developers have built thousands of variants (e.g., Nvidia’s Nemotron), and Meta AI now boasts 600 million monthly users.

But it’s not all altruism. Open-sourcing LLaMA 4 strengthens Meta’s ecosystem—think WhatsApp bots, Instagram tools, and smart glasses like Ray-Ban Meta. It also positions Meta as a leader in a field dominated by closed models from OpenAI and Google.

Critics argue this openness could fuel misuse (e.g., spam or cyberattacks), but Meta counters with safety tuning and a belief that transparency drives innovation. Love it or hate it, this strategy is reshaping AI.

What’s Next for LLaMA 4?

Meta isn’t stopping here. LLaMA 4’s rollout is just the beginning of a busy 2025:

• Behemoth’s Release: Expected later this year, it’ll raise the bar further.
• Voice Upgrades: Plans for native speech (not just text-to-speech) could make Meta AI a true conversational powerhouse.
• LlamaCon: On April 29, 2025, Meta’s first AI conference will reveal more about its roadmap.

Training LLaMA 4 took a cluster of over 100,000 Nvidia H100 GPUs—bigger than anything Meta’s rivals have reported. Zuckerberg hints at “new modalities” and “stronger reasoning” for future iterations, so expect LLaMA 5 to be even wilder.

How to Get Started with LLaMA 4

Ready to try LLaMA 4? Here’s how:

• Download: Grab Scout or Maverick from ai.meta.com or Hugging Face.
• Test It: Use Meta AI on WhatsApp, Messenger, or meta.ai to see it in action.
• Build: Developers can tweak the models for custom projects—just check the license terms.

For hardware, Scout runs on a single H100, while Maverick needs more muscle. Behemoth? Start saving for a supercomputer.

Final Thoughts

LLaMA 4 is more than a new model—it’s a statement. With its MoE architecture, massive context window, and multimodal chops, Meta is proving that open-source AI can rival the best proprietary systems. Whether you’re coding, chatting, or analyzing data, LLaMA 4 has something to offer.

As of April 6, 2025, the AI world is buzzing, and Meta’s latest release is at the heart of it. What do you think—will LLaMA 4 change the game? Drop your thoughts below, and stay tuned for more updates as Behemoth looms on the horizon!

Disclaimer

The information in “LLaMA 4 Unveiled: Meta’s Latest AI Model Explained” is based on official announcements and publicly available data from Meta AI, primarily sourced from ai.meta.com and related channels as of April 6, 2025. While we strive for accuracy, details about LLaMA 4—including features, performance, and availability—may evolve as Meta releases updates or additional documentation. This blog is intended for informational purposes only and does not constitute professional advice or an endorsement of LLaMA 4 or Meta’s products.

Performance claims (e.g., comparisons to GPT-4o or Gemini 2.0) reflect Meta’s reported benchmarks and may vary based on real-world use, hardware, or independent testing. Open-source availability and licensing terms are subject to Meta’s LLaMA 4 Community License Agreement, and readers should review it at github.com/meta-llama/llama-models for restrictions (e.g., EU usage or limits for large companies). We are not affiliated with Meta AI, and any opinions expressed are those of the author, not Meta.

Links to external sites (e.g., ai.meta.com, Hugging Face) are provided for reference; we’re not responsible for their content or changes post-publication. For the latest on LLaMA 4, consult Meta’s official resources directly. Questions? Contact us via the blog’s comment section.

Also Read

Microsoft Turns 50: A Look at Its History, Innovations, and Future