In April 2025, Meta announced the release of Llama 4, an advanced generative AI model that introduces three distinct versions: Scout, Maverick, and Behemoth. Each model is purpose-built for distinct applications, representing a major leap for defining AI technology. Llama 4 Scout and Maverick are already available — check them out today! Behemoth is still under development and intended to be deployed in the future for specialized research missions.
Additionally, Llama 4 is a first for Meta’s open-weight multimodal models, processing text, images and video inputs. These generative models are deeply trained on huge datasets. They train on vast amounts of unlabeled text, images and videos in over 200 languages, giving them the ability to understand high-level visual and contextual cues. Llama 4 is at the cutting edge in AI development. It is particularly powerful in applications ranging from pair programming to more advanced tasks requiring sophisticated reasoning.
Detailed Specifications of Llama 4 Models
Llama 4 Scout features an outstanding architecture. So it has 17 billion active (i.e. total) parameters, but its total number of parameters reaches up to 109 billion. Additionally, the model is specifically optimized for long-form workflows and data analysis tasks, with a context window of 10 million tokens. This design presents an elegant solution to copious amounts of data. It’s an ideal fit for a wide variety of applications that demand advanced processing and analysis.
Llama 4 Maverick has 17 billion active parameters. It stands out for the gigantic overall size of 400 billion parameters of which it consists. With a context window of 1 million tokens, it gives itself a lot of flexibility around a lot of tasks. Maverick is described as a generalist model, balancing speed of response with the ability to reason. This versatility lends itself to many use cases such as code generation, conversational chatbots, and IT help desks.
The differentiation doesn’t stop there, both Scout and Maverick leverage cutting-edge expert systems. Scout uses 16 experts to improve its processing power, and Maverick uses 128 experts. These heuristic functions allow models to achieve far more complicated tasks with great computational efficiency.
Future Prospects with Llama 4 Behemoth
Today, you can find Llama 4 Scout and Maverick on Llama.com and in partnerships with platforms such as Hugging Face. The much awaited Llama 4 Behemoth is still under tight wraps for now. Behemoth is enormous, with 288 billion active parameters but a whopping 2 trillion total parameters. This ensures that it is a very effective teacher to the smaller models.
Behemoth is intended for the highest levels of research, model distillation, and for commercial and academic STEM applications. It aspires to advance the field of what generative AI can do. Though no release date has been set, anticipation is mounting. People in the US are already looking forward to what its fresh perspective will help it contribute to the field.
“Attack” – [“Prompt Guard can block text intended for Llama, but only text meant to “attack” the model and get it to behave in undesirable ways.” – techcrunch.com/2023/02/24/can-language-models-really-be-protected-from-text-based-attacks/]
Security Features and Ethical Considerations
Now, with generative AI models rapidly moving into commercial deployments, making safe and ethical use indispensable, the stakes have become higher. To help address risks that can arise from the use of AI in deployment, Meta has embedded several security features into Llama 4. With these issues in mind, we built the Llama Firewall to detect and prevent threats like prompt injection and insecure code interactions. Further, an open-source tool like Code Shield offers safe command execution in seven different programming languages.
Given its implications for security, Llama Guard quickly assumes an important role. It is taking steps to help flag harmful content designed for the model or fed into it. This means content that promotes or glorifies crime, exploitation, copyright infringement, harassment, hate speech, incitement to violence, self-harm, and sexual abuse. As some experts point out, there is no silver bullet when it comes to risk mitigation.
“Injected inputs” – [“The Llama Firewall works to detect and prevent risks like prompt injection, insecure code, and risky tool interactions.” – techcrunch.com/2022/12/13/image-generating-ai-can-copy-and-paste-from-training-data-raising-ip-concerns/]
Critics have noted that even with these measures, vulnerabilities still exist. Some have raised specific concerns about situations in which the model might accidentally create or reply to dangerous content. What’s more, past versions of Meta’s guidelines permitted some interactions that led to the toxic exchanges we see today.