Artificial intelligence is advancing at an unprecedented pace, and Meta’s recent release of Llama 4, the most powerful open-source model yet, exemplifies this progress. Llama 4 marks a new era in the AI revolution, introducing a multimodal model with three different versions: Behemoth, Maverick, and Scout. This new model family, which is natively multimodal, utilizes a mixture of expert architecture, making it a significant addition to the AI arena.
This video is from Mervin Praison.
Llama 4 has been introduced alongside the Gemini 2.5 Pro, the second-best model, which is also open-source. The potential applications for these models are broad, ranging from social media platforms like Facebook and Instagram to web development with Meta.ai. The impact of Llama 4, however, extends beyond its multimodal capabilities.
The Llama 4 Scout version boasts 17 billion active parameters and fits into a single H100 GPU. With an industry-leading 10 million token context window, it surpasses the performance of previous models such as Gemini 3, Gemini 2.0, and Mistral 3.1. Its best-in-class image grounding capability also exceeds the performance of all previous Llama models, highlighting its significant advancement in the field.
The Llama 4 Maverick has the same number of active parameters as Scout, but with 128 experts, it fits into an H00 DGX host. Despite having fewer parameters, its multimodal performance is comparable to Deepseek version 3, outperforming GPD40 and Gemini 2.0.
The Llama 4 Behemoth, with 288 billion active parameters and 16 experts, is the largest model ever trained as an open-source. It has nearly 2 trillion total parameters, outperforming GPT 4.5, CL 3.7, and Gemini 2.0 Pro on STEM benchmarks. Although still in training, it serves as a teacher model for knowledge distillation.
These models are not just large; they also introduce several technical innovations. Llama 4 is natively multimodal with early fusion integration, features an improved vision encoder based on Meta clip, and utilizes a new Meta pruning technique for hyperparameters. It’s been pre-trained on 200 languages with ten times more multi-lingual tokens, double than the previous Llama 3 model. The use of FP8 precision for efficient training and Irobe architecture for enhanced context length also sets it apart.
Moreover, Llama 4 has been trained on 30+ trillion token token training data, with both pre-training and post-training safeguards in place. It has been designed to provide more balanced responses across the political spectrum, with a political lean comparable to Grock.
In terms of access, Llama 4 is readily available on various platforms, including WhatsApp, Messenger, Instagram Direct, and the Meta.ai website. Users can also download it directly from Hugging Face, and it’s available on Grock Cloud and the Praisai Agents framework.
However, it’s not just the technical specifications that make Llama 4 impressive. When put to the test, the model proved its capabilities, successfully completing expert-level Python challenges and generating a multifaceted dashboard with various charts using HTML, CSS, and JS.
In conclusion, Llama 4 is a breakthrough in the world of AI, bringing enhanced multimodal capabilities and technical innovations. Its superior performance and open-source nature make it a valuable tool for developers, researchers, and tech enthusiasts alike. With the anticipated arrival of Llama 4 Reasoning, the future of AI looks promising indeed. As the world of artificial intelligence continues to evolve, we can expect more impressive advancements and applications to emerge.
