Llama 4 Scout, Maverick, Behemoth: Capabilities, Access, and How ...
Meta just dropped Llama 4 — and it’s a power move. Llama 4 Scout fits on a single H100, 17B active params, 10M context window, and it beats models twice its size with ease. Then, Llama 4 Maverick: 400B total params, 128 experts, multimodal, and outperforms GPT-4 + Gemini 2.0 on core benchmarks. To me, it seems like a best-in-class performance-to-cost ratio and that is why I want to review it in detail here.
Overview of Llama 4 Models
Both models are distilled from Llama 4 Behemoth (2T total params, still training). MoE architecture, native multimodality, and open weights. It’s the most developer-friendly AI release this year and is already running on WhatsApp, Messenger, IG, and Meta.ai. More will happen at LlamaCon April 29, and now, let's review some of Llama 4 capabilities and how you can integrate it into your line of work. Each of those three models works best with different use cases, so you need to know which one suits your tasks best.
Scout - Quick and Efficient
Let's start with the lightest one, it is called Scout and the name suggests its best use cases. Llama 4 Scout is optimized for quick response times and efficiency, it is the lightest one and works faster than most LLMs in 2025. This makes it great at real-time work where speed is at most priority for you.
- High Efficiency: Scout is designed to get you responses. It’s ideal for chatbots, or customer support (often beating Gemini), and interactive learning tools.
- Lightweight Design: The model’s small size means it can be also used on devices with limited computing power, it will not overheat or destroy some old tech.
- Comparison with Other Models: When compared to similar models by other manufacturers, Scout stands out for its balance between speed and performance. It is not just fast; but it also maintains accuracy, making it a great choice for everyday applications.
Maverick - Advanced Capabilities
The next model is much more robust. Llama 4 Maverick is designed for some of more complex and nuanced tasks in comparison to light models like Scout Llama. Maverick, as far as I can see, gives a lot deeper reasoning and works well with a variety of different multimodal inputs. This makes applications of it more advanced.
- Advanced Reasoning: Maverick gives more detailed and logical responses. In my experience, this may be just fine for tasks that require deeper analysis. To me it also seems that Maverick can beat a lot of OpenAI's reasoning.
- Multimodal Capabilities: Unlike Scout, Maverick can work with multiple input types: images, audio, and video along with text and documents. This makes it ideal for creative projects or work tasks that require both visual and textual analysis at once.
- Performance in Complex Scenarios: When compared to similar models on the market (like GPT-4 with its reasoning features), Maverick has a lot of performance in reasoning and in context comprehension as well. It also balances efficiency with in-depth processing.
Behemoth - Massive Scale
This powerhouse is still in training and is expected to be Meta's most powerful model of all time as of 2025. As the name suggests, Behemoth Llama is for heavy-duty tasks and for large-scale applications that need A LOT of processing power.
- Massive Scale: Behemoth is built with trillions of parameters in it (literally!), making it capable of performing in very large and complex datasets and tasks of huge scale. It is intended for tasks that require comprehensive analysis that almost no other model can do effectively.
- Enterprise-Level Applications: This model is suited for high-demand environments like large research institutions and enterprises. It's able to manage and analyze vast amounts of data. A strong competitor for any top LLM.
- Future-Proof Capabilities: Although Behemoth is not yet available, it will probably set a new standard in AI research. It is also expected to help in training future AI systems with its framework for even more advanced models.
Accessing Llama 4 Models
Now, let's see how to get to use those wonders yourself. If you want to know how to get access to llama 4, there are a couple available 'routes'. Each option also has different benefits, and you should choose one depending on your specific needs.
- Download Models via Llama.com: Simply go to llama.com, download models and try using them on your setup.
- Developer Portal: Register on Meta’s developer website, there you can also apply for access to the llama 4 API. If you then get approved models can be integrated into your projects directly. I would recommend doing that to get access to the latest models when they come out. Here is a link to Developer Portal to get yourself some Llama: https://developers.meta.com/
- Beta Programs and Early Access: Maybe also keep an eye on Meta’s announcements for beta testing programs. Participating in these programs can give you first access to Meta Llama 4 latest updates and features.
- Official Documentation: Use the resources available on Meta’s official documentation.
