Meta just announced Llama 4, and it’s a big deal for artificial intelligence. The company released three new models called Scout, Maverick, and Behemoth. These aren’t just incremental updates. They represent a fundamental shift in how AI models are built and deployed. The most exciting part? They use something called mixture-of-experts architecture, which makes them faster and cheaper to run while keeping them smart.
What makes this announcement matter right now is timing. We’re seeing a race between tech companies to build better AI agents—systems that can think, reason, and take actions on their own. Llama 4 is Meta’s answer to this challenge. The models support an enormous context window of up to 10 million tokens, meaning they can process and understand massive amounts of information at once. They’re also natively multimodal, which means they can handle text, images, and other data types together from the ground up.
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Understanding the Llama 4 Lineup
Meta released two production-ready models and one preview model. Let’s break down what each one does.
Llama 4 Scout: The Efficient Powerhouse
Scout is the smaller of the two available models, but don’t let that fool you. It has 17 billion active parameters spread across 16 experts. The total model contains 109 billion parameters, but only a fraction activates for any given task. This efficiency means Scout can run on a single high-end GPU with proper optimization, making it accessible to more developers and organizations.
The real breakthrough with Scout is its context length. It jumped from 128,000 tokens in the previous Llama 3 to 10 million tokens. This is genuinely transformative for real-world applications. Imagine analyzing an entire codebase at once, summarizing dozens of documents together, or tracking a user’s complete activity history for personalized recommendations. Scout makes all of this practical.
Llama 4 Maverick: The Balanced Performer
Maverick sits in the sweet spot between efficiency and raw power. It has 17 billion active parameters but 128 experts, with 400 billion total parameters. This configuration lets it run on a single NVIDIA H100 DGX host, which is Meta’s way of saying it’s deployable without massive infrastructure.
The performance numbers are impressive. Maverick beats GPT-4o and Gemini 2.0 on coding, reasoning, multilingual tasks, long-context work, and image understanding. It’s competitive with much larger models like DeepSeek v3.1 on coding and reasoning benchmarks. For most organizations, Maverick represents the sweet spot—powerful enough for complex tasks but practical enough to actually deploy.
Llama 4 Behemoth: The Preview
Behemoth is still training, so it’s not available yet. But the preview numbers are remarkable. It outperforms GPT-4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro on math and science benchmarks. Meta is using Behemoth as a teacher model to improve Scout and Maverick through a process called distillation. This approach lets smaller models learn from larger ones, capturing their reasoning abilities in a more compact form.
What’s Different About Mixture-of-Experts Architecture
The biggest technical innovation in Llama 4 is the mixture-of-experts (MoE) approach. This is different from how most large language models work today.
Traditional models activate all their parameters for every task. If a model has 70 billion parameters, all 70 billion are working together on your question. MoE works differently. Each piece of input (called a token) gets routed to only a fraction of the total parameters. Maverick has 400 billion total parameters, but only 17 billion activate for any given task.
Why does this matter? Speed and cost. When fewer parameters are active, inference runs faster and costs less to operate. You get the benefits of a massive model without paying the computational price. It’s like having a 400-person team available but only assigning the right specialists to each project instead of having everyone work on everything.
Meta uses alternating dense and mixture-of-experts layers. Every token goes to a shared expert plus one of the routed experts. This hybrid approach maintains quality while maximizing efficiency. The result is models that deliver high performance at lower operational costs.
Context Length: A Game-Changer for AI Agents
Scout’s 10 million token context window deserves its own section because it fundamentally changes what’s possible.
Previous language models maxed out around 100,000 to 200,000 tokens. That’s roughly equivalent to 75,000 to 150,000 words. Ten million tokens is 50 to 100 times larger. To put it in perspective, you could fit the entire codebase of a major software project into Scout’s context. You could include years of customer interactions, entire books, or comprehensive documentation.
For AI agents, this is crucial. Agents need to understand their environment completely. A customer service agent needs access to all previous interactions. A code analysis agent needs to see the entire system architecture. A research agent needs access to all relevant papers and data. Scout’s context window makes all of this practical for the first time.
Multimodal Capabilities From the Ground Up
Scout and Maverick are natively multimodal. This means they weren’t built for text first and then patched to handle images. Images and text understanding are built into the core architecture from the beginning.
This matters for agents because real-world tasks involve mixed data. A document analysis agent might need to read text and understand charts. A content moderation agent needs to evaluate text and images together. A design feedback agent should understand visual mockups and written requirements simultaneously. Native multimodal support makes these workflows natural rather than awkward.
Both models excel at image benchmarks compared to competitors like GPT-4o and Gemini 2.0. They’re not just capable of handling images—they’re genuinely good at understanding them.
Performance Benchmarks and Real-World Implications
Numbers tell part of the story. Maverick outperforms models that are significantly larger in raw parameter count. It beats GPT-4o on multiple benchmarks while being more efficient to run. It’s competitive with DeepSeek v3.1, which is one of the most advanced models available.
For organizations building AI agents, this means choices. You can use Scout for efficiency-critical applications where you need speed and low cost. You can use Maverick for more complex reasoning tasks where you need power but still want reasonable deployment costs. You don’t have to choose between capability and practicality anymore.
The math and science performance of Behemoth is particularly important for reasoning-heavy agent tasks. If you’re building agents that need to solve problems, write code, or work through complex logic, having access to a model trained on strong reasoning benchmarks changes what’s achievable.
What This Means for AI Agents
AI agents are the next frontier. Unlike chatbots that just answer questions, agents take actions. They plan, execute, learn, and adapt. Building good agents requires models that can understand context, reason through problems, and handle multiple types of information.
Llama 4’s combination of features addresses agent needs directly. The massive context window means agents can maintain detailed knowledge of their environment and history. The multimodal capability means agents can work with real-world data. The efficiency of MoE architecture means agents can run continuously without prohibitive costs. The strong reasoning performance means agents can solve actual problems, not just retrieve information.
For developers and organizations, this opens doors. You can now build sophisticated agents using open-weight models. You’re not locked into expensive API calls to closed systems. You can run these models on your own infrastructure, keeping your data private and maintaining full control.
The Bigger Picture: Open vs. Closed AI
Meta’s strategy with Llama 4 is important beyond just the technical specs. These are open-weight models, meaning researchers, developers, and organizations can download them and run them however they want.
This contrasts with closed models from OpenAI, Google, and Anthropic. Those companies control everything about their models. You access them through APIs with their rules and pricing. With Llama 4, you have freedom. You can fine-tune the models for your specific use case. You can run them offline. You can modify them. You can build businesses on top of them without worrying about API pricing changes.
For the AI agent space specifically, this matters enormously. Companies building agent systems need reliability, control, and cost predictability. Open-weight models provide all three. As agent technology matures, we’ll likely see open models play a bigger role than they do in consumer-facing AI today.
Challenges and Limitations
Llama 4 is impressive, but it’s not perfect. Behemoth is still training, so we don’t have a truly massive model available yet. The smaller models, while efficient, still require significant computational resources. Running Maverick needs a high-end GPU or distributed setup.
The 10 million token context window is powerful but comes with tradeoffs. Longer context can sometimes hurt reasoning on specific tasks. There’s also a practical question: most applications don’t actually need 10 million tokens. For many use cases, Scout’s capability exceeds what’s necessary.
Additionally, these models are new. Real-world performance in production environments will reveal strengths and weaknesses that benchmarks don’t capture. The AI field moves fast, and new models from competitors will emerge quickly.
Looking Forward: What’s Next
Meta’s Llama 4 announcement signals where AI development is heading. We’re moving toward more efficient models that do more with less. We’re moving toward multimodal systems that understand multiple data types naturally. We’re moving toward longer context windows that let AI systems understand more of their world.
For AI agents specifically, we’re at an inflection point. The technology is becoming practical. The models are becoming capable. The infrastructure for deployment is becoming accessible. We should expect to see significant agent applications emerge over the next year or two, powered by models like Llama 4.
Organizations that want to stay ahead should start experimenting now. Understanding how to work with these models, how to fine-tune them for specific tasks, and how to build agents on top of them will be valuable skills. The AI landscape is shifting from closed, centralized systems to open, distributed ones. Llama 4 represents Meta’s bet that this shift is the future.
