All About Meta's Llama 4: Features And Innovations

Have you been following Meta’s latest updates?

Meta has been steadily building its presence in the AI world, focusing on creating powerful open language models that are accessible to all, and over the years, has released several Llama versions.

These models are designed to understand and generate human-like text, helping with tasks like summarization, code generation, and visual reasoning. With each version, Meta has improved performance, scale, and efficiency.

Yet, before discussing its latest and most advanced release, Llama 4, let’s understand what Llama is.
 

What Is Meta’s Llama?

Llama (Large Language Model Meta AI) is a series of advanced language models developed by Meta. These models range from 7 billion to 65 billion parameters and are designed to offer powerful performance while being more efficient in size and resource usage.

Unlike traditional models that demand high computational power, Llama models make it easier for researchers and developers to experiment, validate research, and explore new applications without massive infrastructure.

The models were trained on a large mix of data sources, as follows:
 

• 67.0% CommonCrawl

• 15.0% C4

• 4.5% GitHub

• 4.5% Wikipedia

• 4.5% Books

• 2.5% ArXiv

• 2.0% StackExchange

Now that we understand what Llama is, let’s get to what you are all here for—Llama 4.
 

What Is Llama 4?

Llama 4 is Meta’s latest family of language models. While its two versions, Llama 4 Scout and Llama 4 Maverick, are already available, the third and more powerful, Llama 4 Behemoth, is still in training.


One of the biggest improvements in Llama 4 is its new design. It uses something called a “mixture-of-experts” (MoE) system, so the model only uses the parts it needs for each task, making it faster and more efficient.

Meta still offers Llama 4 as an open-weight model, meaning others can use and build on it. However, companies with more than 700 million users need special permission from Meta. The release also comes when AI models are getting better and more competitive, with models like DeepSeek’s R2, Alibaba’s Qwen, Google’s Gemma, and a new open-weight model from OpenAI are raising the bar. This makes the competition–and the innovations–much more interesting.

Now, let’s find out more details about each model within the Llama suite!
 

Llama 4 Scout

Llama 4 Scout is a light model in Meta’s latest lineup, yet it stands out for its capabilities. It runs on a single H100 GPU and supports a massive 10 million-token context window, ideal for long-form tasks like summarizing multiple documents, parsing codebases, or reviewing legal files.

With 17 billion active parameters drawn from a total of 109 billion, Scout utilizes Meta’s mixture-of-experts (MoE) design, making it scalable and compute-friendly. Additionally, Scout is multimodal and concurrently trained on text, images, and videos. Hence, it can perform visual tasks such as image understanding and visual Q&A surprisingly well, while outperforming previous Llama models.
 

Llama 4 Scout Innovations

While Llama 4 Scout may be the light model in the lineup, it brings several impressive innovations. From visual reasoning to long-context understanding, Scout proves that smaller models can still pack a serious punch. Here are five standout innovations that make Scout worth watching:

 

• Performance

  Designed to run on a single H100 GPU, Scout delivers strong performance across reasoning, coding, and multimodal tasks, despite having fewer active parameters than larger models.

• Visual Understanding

  Scout scores 88.8 on ChartQA and 94.4 on DocVQA, outperforming Gemini 2.0 Flash-Lite and holding its own against Mistral 3.1 and Gemma 3.

• Visual Reasoning

  On image reasoning benchmarks like MMMU (69.4) and MathVista (70.7), Scout surpasses other open-weight models, proving its strength in handling complex visual tasks.

• Coding Abilities

  With a 32.8 score on LiveCodeBench, Scout beats Gemma and Gemini Flash-Lite. While not a coding-first model, it performs competitively.

• Contexts

  Scout handles long inputs well, scoring strongly on the MTOB benchmark, even in low-resource language tasks, demonstrating its potential in real-world, large-scale workflows.

 

Llama 4 Maverick

Llama 4 Maverick is the all-rounder in Meta’s latest lineup. Unlike Scout, which is optimized for extremely long inputs, Maverick is designed to perform well across various tasks, such as conversation, reasoning, image understanding, and coding. It’s Meta’s most balanced model yet, aimed at competing with top models like GPT-4o, DeepSeek-V3, and Gemini 2.0 Flash.

Maverick operates on a significantly larger 400 billion total parameter model while sharing Scout’s 17 billion active parameters and utilizing 128 experts. It also employs a mixture-of-experts (MoE) design to maintain efficiency.

Meta trained Maverick differently by skipping the easy tasks and focusing on harder ones, which helped the model develop better reasoning and conversation skills. Meta leveraged knowledge from its largest model, Llama 4 Behemoth, to enhance its capabilities without requiring extra training time or cost.
 

Llama 4 Maveric Innovations

While Llama 4 Maverick doesn’t chase extremes, it sets a new benchmark for balance, delivering reliable performance across tasks without compromising scale, speed, or quality. Here are five innovations that help Maverick stand out:

 

• Performance

  Maverick is built to handle reasoning, chat, coding, and image understanding equally well, making it Meta’s most versatile model.

• Visual Intelligence

  It scores 90.0 on ChartQA and 94.4 on DocVQA, outperforming GPT-4o and Gemini Flash and excelling at visual understanding and document comprehension.

• Coding Capability

  With a 43.4 score on LiveCodeBench, Maverick beats top-tier models like GPT-4o and Gemini Flash, coming close to DeepSeek v3.1.

• Reasoning & Knowledge

  Maverick leads with 80.5 on MMLU Pro and 69.8 on GPQA Diamond, showing a deep understanding in complex, multi-step tasks.

• Long-Context Recall

  Scoring over 50 on full-book MTOB tests, Maverick quietly delivers strong context retention, even outperforming models built specifically for it.

 

Llama 4 Behemoth

Llama 4 Behemoth is Meta’s biggest and most powerful language model so far, but it hasn’t been released yet. It’s still in training and isn’t meant for everyday use like Scout or Maverick. Instead, Behemoth works behind the scenes as a “teacher model,” helping shape and improve the smaller models through a process called distillation.

With 288 billion active parameters and nearly 2 trillion in total, Behemoth required a completely new training setup. Meta employed advanced techniques such as asynchronous reinforcement learning and a system that adjusts learning according to task difficulty. During post-training, Meta filtered out simpler examples and concentrated on challenging reasoning, coding, and multilingual prompts.
 

Llama 4 Behemoth Innovations

Although Llama 4 Behemoth hasn’t been released yet, early benchmarks suggest it’s one of Meta's most capable models ever trained. Here are five innovations that highlight what makes Behemoth so powerful:

 

• Scale

  With 288 billion active parameters and nearly 2 trillion total, Behemoth is Meta’s largest model yet, designed to push the limits of what language models can learn.

• Performance

  It scores 95.0 on MATH-500 and 82.2 on MMLU Pro, outperforming models like Claude, Gemini Pro, and even GPT-4.5 in advanced reasoning and problem-solving tasks.

• Multilingual Capabilities

  Behemoth leads with an 85.8 on Multilingual MMLU, making it ideal for use cases beyond English and developers working globally.

• Image Reasoning

  With 76.1 on MMMU, Behemoth competes well with top multimodal models, showing it can handle visual logic too.

• Coding Abilities

  Scoring 49.4 on LiveCodeBench, Behemoth sets a new code generation bar well ahead of Gemini 2.0 Pro and others.

 

Final Words

They say good things come in threes. Well, with Llama 4, that certainly holds true!

Meta’s Scout redefines how much context a single GPU can handle, Maverick strikes the perfect balance across tasks, and Behemoth (in training) offers a look into the future of large-scale teacher models.

In a fast-growing open-weight landscape filled with challengers like DeepSeek, OpenAI, and Mistral, Meta continues to carve out its unique space. Llama 4 isn’t just a response to competitors but a reaffirmation of Meta’s commitment to build powerful, efficient, and openly available models for a variety of AI applications.