On April 5, 2025, Meta launched Llama 4, an open-source artificial intelligence (AI) model that promises to be a game-changer.
This model, the fourth iteration of its family of language models, is presented as a powerful and multimodal tool, capable of processing text and images natively, demonstrating the ambition behind Meta’s AI innovations.
Let’s explore what makes the Llama 4 special, how it compares to other models, and what it means for the future of technology.
A look at the Llama family
The story of Llama 4 is best understood by going back in time. Each iteration of the Llama family has taught valuable lessons. The progressive increase in the number of parameters has improved performance in complex tasks, such as the generation of elaborate texts and advanced reasoning.
In 2023, Meta launched Llama 1, a model with 7 billion parameters that offered a free alternative to proprietary options such as OpenAI’s GPT-3, quickly gaining the preference of those looking for accessible AI solutions.
In 2024, Llama 2 arrived with 13 billion parameters and efficiency improvements, and shortly after, Llama 3 expanded its capabilities to 70 billion parameters, integrating support for text and image processing and expanding the language offering.
Llama 4 stands as the next step in this evolution, coming in three versions (Scout, Maverick and Behemoth), each designed to meet different needs and use scenarios.
What makes Llama 4 unique?
Llama 4 uses the MoE architecture (like Deepseek R1), which activates only the necessary part of its parameters for each task, as opposed to using the entire model.
This strategy significantly reduces computational requirements, allowing outstanding performance even on less powerful systems.
Additionally, Llama 4 was trained with over 30 trillion tokens (twice as many as its predecessor) with information updated through August 2024. It also supports 200 languages, with over 100 of them represented by at least 1 billion tokens.
Its natively multimodal design, which integrates text, images and video using an early fusion architecture and a MetaCLIP-based vision encoder, makes it an exceptionally versatile tool.
The three faces of Llama 4
Llama 4 comes in 3 versions, which are distinguished by the number of parameters and the technical requirements to operate:
#### Flame 4 Scout
With 17 billion active parameters out of a total of 109 billion, this version is ideal for tasks that require the analysis of large volumes of information, thanks to a context window of up to 10 million tokens (powered by the iRoPE technique).
It runs on a single NVIDIA H100 GPU and is capable of processing up to eight images per request, making it the perfect choice for deep analytics applications and multi-modal tasks.
#### Call 4 Maverick
Also with 17 billion active parameters, but with a total of 400 billion, this variant is aimed at virtual assistants and advanced chatbots.
It runs on an NVIDIA H100 DGX platform, enabling detailed understanding of images and offering accurate responses in multiple languages. Its design makes it especially suitable for complex conversations and interactive applications.
#### Call 4 Behemoth
The most ambitious version, with 288 billion active parameters and almost 2 trillion in total, is currently under development.
Intended for intensive tasks (such as advanced mathematical calculations and extensive multilingual support) and as a base for “distilling” smaller models, Behemoth promises unprecedented performance to solve highly complex technological challenges.
How does Llama 4 compare to the competition?
The Scout version of Llama 4 appears to outperform smaller models like Gemma 3 and Mistral 3.1, especially in tasks that require combined handling of text and images.
In the case of Llama 4 Maverick, it can compete on equal terms with medium-sized models, standing out in coding, reasoning and visual understanding benchmarks, and positioning itself as a strong alternative to options like GPT-4o.
Although still in development, Llama 4 Behemoth is expected to take on giants like GPT-4.5 and Claude Sonnet 3.7 in high-demand tests.
Llama 4 has also incorporated learnings from other leading models such as DeepSeek v3, opting for the iRoPE technique to achieve efficient integration of information, which gives it a competitive advantage in terms of efficiency and flexibility.
Applications that transform the world
Llama 4’s applications are wide and varied, ranging from the technology sector to education and commerce.
First of all we have virtual assistants. Integrated into platforms such as WhatsApp, Messenger and Instagram, Llama 4 will surely enable more natural and contextual interactions, improving the user experience.
With support for 200 languages, this model facilitates accurate and culturally adapted communications, bringing communities around the world closer together. Its ability to summarize large volumes of textual and visual information makes it an essential tool for market research and analysis.
The future with Llama 4
Llama 4 is more than a model, it is Meta’s vision for open AI that is for everyone, not just the tech giants.
But although Llama 4 is open source, its license imposes limitations, such as a ban on use in the European Union and specific restrictions for large companies. This has sparked debates about the true openness and accessibility of the model.
This post is also available in: