Llama 4: Meta's Multimodal AI Breakthrough in 2025

Meta has unveiled Llama 4, a groundbreaking suite of open multimodal AI models that push the boundaries of language, vision, and reasoning capabilities. Designed with a focus on openness, efficiency, and versatility, Llama 4 is set to empower developers, researchers, and businesses worldwide.

Meet the Llama 4 Family

Llama 4 Scout

Llama 4 Scout features 17 billion active parameters supported by 16 experts, culminating in a total of 109 billion parameters. It fits on a single NVIDIA H100 GPU with quantization and offers an industry-leading context window of 10 million tokens. This model excels in multi-document summarization, code understanding, and long-context reasoning, achieving best-in-class performance on both text and image tasks for its size.

Llama 4 Maverick

Llama 4 Maverick has 17 billion active parameters enhanced by 128 experts, with a total of 400 billion parameters. It outperforms competitors such as GPT-4o and Gemini 2.0 Flash on a range of benchmarks and holds its own against much larger models like DeepSeek v3.1. This model is particularly well-suited for chat assistants, creative writing, and precise image understanding, and it has achieved an impressive ELO score of 1417 on LMArena.

Llama 4 Behemoth (Preview)

The Llama 4 Behemoth, currently in preview, boasts 288 billion active parameters alongside 16 experts, with nearly 2 trillion parameters in total. Recognized as one of the world's smartest large language models, it is still undergoing training but already demonstrates superior performance compared to GPT-4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro on STEM benchmarks. Additionally, it serves as a teacher model for distillation into smaller variants of Llama 4.

Multimodal Intelligence

Llama 4 is designed to be natively multimodal, capable of both understanding and generating text, images, and video frames. Its early fusion architecture allows for seamless integration of text and vision tokens. The model has been pre-trained on over 30 trillion tokens from diverse sources, including multilingual texts, images, and videos. It exhibits strong visual reasoning, capable of handling multiple images per prompt with excellent image grounding and localization. This allows it to tackle complex tasks that require long-context multimodal understanding.

Technical Innovations

At the core of Llama 4 lies a Mixture-of-Experts (MoE) approach, which efficiently activates only a subset of parameters per token. This mechanism enhances quality while reducing computational costs. The Scout variant offers a massive context window of up to 10 million tokens, enabling it to handle tasks that demand extensive long-context analysis. Furthermore, Llama 4 employs advanced training techniques such as a new hyperparameter tuning method known as MetaP, FP8 precision for efficient large-scale training, mid-training and curriculum learning to improve reasoning capabilities, and codistillation from the Behemoth to smaller models. A post-training pipeline featuring lightweight supervised fine-tuning, online reinforcement learning, and direct preference optimization further balances intelligence with conversational ability.

Performance Highlights

In performance benchmarks, Llama 4 Maverick outshines competitors like GPT-4o and Gemini 2.0 Flash in areas including coding, reasoning, multilingual processing, long-context tasks, and image analysis. Llama 4 Scout has surpassed all previous Llama models and comparable offerings in its class, while Behemoth has demonstrated exceptional performance on STEM-related tasks when measured against models such as GPT-4.5 and Claude Sonnet 3.7.

Safety and Bias Mitigation

Meta has taken considerable steps to ensure the safety and fairness of Llama 4. Data filtering during pre-training helps maintain high-quality inputs, and adaptive fine-tuning techniques are applied to reduce the generation of harmful outputs. Open-source tools like Llama Guard and Prompt Guard play a critical role in maintaining safety, while advanced red-teaming—combining both automated and manual testing—further enhances the model's robustness. Additionally, efforts to reduce bias have rendered Llama 4 more balanced, making it less prone to refusing or favoring specific viewpoints compared to previous iterations.

Availability and Ecosystem

Llama 4 Scout and Maverick are available for download from llama.com and Hugging Face. The innovative technology is already powering Meta AI applications across platforms such as WhatsApp, Messenger, Instagram Direct, and the web. Moreover, an upcoming event, LlamaCon on April 29, will provide further insights into this transformative technology. The open ecosystem is designed to empower developers, researchers, and enterprises worldwide.

Conclusion

Llama 4 marks a significant leap forward in the development of open, multimodal AI. With its robust models, massive context windows, and a strong commitment to safety and bias mitigation, Llama 4 opens new possibilities for innovation across various industries. Are you ready to explore what Llama 4 can do for you? Book a Consultation →