In early April 2025, Meta launched Llama 4, its latest series of AI models designed to push the company to the next level. Each of the new Llama 4 models comes with serious improvements over its predecessors, and these are the standout new features to try.

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Mixture of Experts (MoE) Architecture

One of the most prominent features of Llama 4 models is the new MoE architecture, a first for the Llama series, which uses a different approach from the previous models. Under the new architecture, only a fraction of the model’s parameters is activated for each token, unlike in traditional dense transformer models like Llama 3 and below, where all parameters are activated for each task.

For example, Llama 4 Maverick uses only 17 billion active parameters out of 400 billion, with 128 routed experts and a shared expert. Llama 4 Scout, the smallest in the series, has a total of 109 billion parameters, activating only 17 billion with 16 experts.

The largest of the trio, Llama 4 Behemoth, uses 288 billion active parameters (with 16 experts) out of nearly two trillion total parameters. Thanks to this new architecture, only two experts are assigned to each task.

As a result of the architectural shift, Llama 4 series models are more computationally efficient in training and inference. Only activating a fraction of the parameters also reduces serving costs and latency. Thanks to the MoE architecture, Meta claims that Llama can run on a single Nvidia H100 GPU, an impressive feat considering the number of parameters. While there are no specific figures, it’s thought that each query to ChatGPT uses multiple Nvidia GPUs, which creates a larger overhead in almost every measurable metric.

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Native Multimodal Processing Capabilities

Another key update to Llama 4 AI models is native multimodal processing capabilities, meaning the trio can simultaneously understand text and images.

This is thanks to fusion performed during the early training stages, where text and vision tokens are integrated into a unified architecture. The models are trained using large amounts of unlabeled text, image, and video data.

It doesn’t get better than this. If you remember, Meta’s Llama 3.2 upgrade, released in September 2024, introduced several new models (a total of ten), including five multimodal vision models and five text models. With this generation, the company doesn’t need to release separate text and vision models thanks to native multimodal processing abilities.

Additionally, Llama 4 uses an improved vision encoder, enabling the models to handle complex visual reasoning tasks and multi-image inputs, making them capable of handling applications that require advanced text and image understanding. Multimodal processing also allows LLama 4 models to be used across a wide range of applications.

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Industry-Leading Context Window

Llama 4 AI models boast an unprecedented context window of up to 10 million tokens. Although Llama 4 Behemoth is still in training as of publishing, Llama 4 Scout sets a new industry benchmark with support for up to 10 million tokens in context length, which allows you to input text of over five million words.

This expanded context length is a dramatic increase from Llama 3’s 8K tokens when it was first unveiled, and even the follow-up expansion to 128K after Llama 3.2 upgrade. And it’s not just Llama 4 Scout’s 10 million context length that’s exciting; even Llama 4 Maverick, with its one million context length, is an impressive feat.

Llama 3.2 is already among the best AI chatbots for extensive conversations. However, Llama 4’s expanded context window positions Llama as the leader, surpassing Gemini’s previous top-of-the-line two-million token context window, Claude 3.7 Sonnet’s 200K, and ChatGPT’s GPT-4.5’s 128K.

With the large context window, the Llama 4 series can handle tasks that require input with massive amounts of information. That massive window is useful in tasks like long and multi-document analysis, detailed analysis of large codebases, and reasoning over large datasets.

It also enables Llama 4 to conduct extended conversations, unlike previous Llama models and those from other AI companies. If one of the reasons Gemini 2.5 Pro is the best reasoning model is its large context window, you can imagine how powerful a 5x or 10x context window could be.

Meta’s Llama 3 series models were already one of the best LLMs on the market. But with the release of the Llama 4 series, Meta is taking it a step further by not only focusing on improved reasoning performance (thanks to the new industry-leading context window) but also on ensuring the models are as efficient as possible by using the new MoE architecture in both training and inference.

Together, Llama 4’s native multimodal processing, efficient MoE architecture, and massive context window position it as a versatile, high-performance, open-weight AI model that rivals or surpasses leading models in reasoning, coding, and other tasks.