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.

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, s you need to know which one suits your tasks best.

My name is Artem and I am using LLMs for many years, building tools upon AI models, testing new ones as soon as they come out and developing an all-in-one AI tool that combines all the best and newest AI models in a one simple chatbot, no API keys needed.

I will guide you through features capabilities and even some of benchmarks. If you want to see official model release note, you can go here: https://ai.meta.com/blog/llama-4-multimodal-intelligence/

Introduction

Meta's releases this time seem to make a new step, or even a leap, in how we would use AI daily. There are three variants of new Meta LLM and they may even become a daily 'meta' for many AI users. What are the differences between Llama 4 models? How do they compare to other models of Meta, Anthropic and GPT? What are some basic capabilities? Here you will find quite a simple explanation, so whether you are a beginner in AI world or a power user, you can easily follow along my guide. There will be also practical examples and instructions to help you integrate LLama4 models into research projects, coding, business applications or some personal creative enterprises. Below is all you need to know.

Detailed Overview of Llama 4 Models

Let's start with the lightest one, it is called Scout and the name suggests its best use cases.

Scout

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.

Key features:

High Efficiency:

Scout is designed to get you responses. It’s ideal for chatbots, or customer support (often beating Gemini), and interactive learning tools.

Its simplified architecture allows it to use a lot less resources while still giving quite accurate answers and operating at high speeds. This is the most efficient model of the three but does not suit very heavy tasks well.

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.

This Scout Llama 4 model is suitable for mobile and web applications where real-time performance matters the most.

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.

Use Cases

Let's see three examples of where Scout will work best:

• Quick customer service bots that can answer FAQs.

• Interactive educational tools where speed is key.

• Real-time translation and summarization tasks.

Practical example

There is an e-commerce website and it needs to answer customers in mere seconds. By integrating Scout, developers of this website can now make sure that responses aren't just fast, but also as accurate as they can be. That way, UX of the platform will be improved, and customers will tend to be happier and more inclined to buy from such a website. As simple as that.

Maverick

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 reasining and works well with a variety of different multimodal inputs. This makes applications of it more advanced.

Key features:

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 reasining.

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.

Use Cases

Let's see three use cases of this Maverick model that I came up with.

• Research applications that require detailed text analysis.

• Creative writing and content generation where nuanced outputs are needed.

• Enterprise solutions that require multimodal data integration.

Behemoth

This power house 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.

Key Features

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 f.e. 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.

Use Cases

Let's see three good use cases for this trained Behemoth

• Large-scale data analysis for market trends.

• Advanced natural language processing tasks.

• Applications in scientific research where massive computing power is required.
  

Practical Example

Let us all imagine a multinational corporation that needs to analyze a behaviour of millions of consumers, across millions upon millions of data points. Behemoth is a single model that may do it amazingly well and give compute necessary to have some insights from such big data. This potential scale may also remain effective as data volumes will grow in the future.

How to Get Access to Llama 4

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.

Official Meta Channels

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 to do that to get access to 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 press release page for guidance on accessing and using the models effectively. Keep in mind that Behemoth is still in training, while others are already in limited use.

Partner Platforms

Cloud Service Providers:

Llama 4 models are now being integrated with platforms like AWS, where you can access them through cloud solutions services like Amazon SageMaker. These platforms have a powerful support for large-scale uses and for enterprisecustomers.

Here is a link to AWS of Amazon that has new Llama 4 access: https://www.aboutamazon.com/news/aws/aws-meta-llama-4-models-available

Community and Third-Party Integrations:

Platforms such as Hugging Face offer some kind of access to llama 4 model weights and other resources. Explore Hugging Face’s collection for additional information.

Writingmate Labs

Soon, Writingmate Labs will integrate llama 4 into its all-in-one AI platform. It already has Claude 3.7 Sonnet, all previous Llamas, GPT 4o reasoning model, Mistral, and much more, all within a single interface, no API needed and with ability to switch between models at a click of a button

• Stay tuned to the Writingmate Blog for updates on when this feature becomes available.

• Writingmate also has Model Comparison feature that lets you to compare any models live, on your real tasks, see their response times and to create your own benchmark with your work load.

Additional Resources for Access

Online Forums and Communities:

• Join AI communities on Reddit, LinkedIn, and other social platforms to share experiences and tips on accessing llama 4.

  
  

  Workshops and Webinars:

• Look out for online events where Meta and partner organizations demonstrate the use of llama 4 and discuss practical applications.

Practical Use Cases and Examples

To better know how to use those new Llama 4 models, let's see detailed real-world scenarios below.

Chatbots and Virtual Assistants

Scenario:
A company wants to change their customer service with an intelligent chatbot agent that will give users instant answers to simple questions and will not bother the team when not needed. Team uses Scout for quick responses and developers integrate it at their webpage with API by Meta AI. For more complex questions they can integrate Maverick to provide in-depth responses. Chatbot can also be updated based on big data received from users.

• Such actions may make customer experience a lot better and save a company a lot of money and time when it comes to a customer support team.

Data Analysis and Research

Scenario:
A research team needs to analyze large volumes of data for market trends and compile comprehensive reports. Then they use Maverick Llama 4 model to summarize lengthy reports and extract key points. The team combins textual analysis with image-based data visualization using the multimodal capabilities.

When scaling, they Deploy Behemoth (once available) for processing very large datasets. And the team gets a lot of insights from such an approach.

Creative Projects and Multimedia Applications

Scenario:
A creative studio wants to generate innovative marketing materials that blend text and visuals. It Uses Scout to quickly generate multiple creative ideas for ad campaigns.

The studio then combines Maverick’s advanced text processing with image generation capabilities, for example of Writingmate, to produce a film, an animation or maybe another kind of multimedia content. Result is a marketing material output with some manual edits that make the creative vision fully realized with a lot less resources.

Compare Newest AI Models With Writingmate

Besides giving you access to all the best AI models on the market and giving a bunch of additional features to them, ChatLabs also helps to compare AI models based on your exact tasks. Let's see how it works:

Overall, if you use AI for your work tasks or you want to better integrate it into your workflow, try Writingmate. It is all-in-one AI tool in a single chatbot with one 20$ subscription, no API needed. Many of the features are also available for free. Try it here: https://writingmate.ai

For detailed articles on AI, visit our blog crafted with a love for technology, people, and their needs.

See you in the next articles!

Artem