Meta is charging back into the AI arena with a herd of groundbreaking large language models. Say hello to Llama 4—not just one model, but a trio of intelligent, efficient, and enterprise-ready powerhouses: Scout, Maverick, and the upcoming Behemoth.
In this post, we break down everything you need to know about the Llama 4 family—its cutting-edge architecture, cost-efficiency, benchmark wins, and real-world applications.
Scout has 17 billion active parameters among a total of 109 billion. It's based on a Mixture of Experts (MoE) model with 16 experts and can handle a context window of up to 10 million tokens. Although it is powerful, Scout is efficient enough to run on a single NVIDIA H100 GPU.
This model is perfect for summarizing long documents, analyzing legal and financial reports, and carrying out academic research at an enormous scale. It has low-resource requirements and a large context window and is a pragmatic option for domain-specific, in-depth tasks.
Maverick also has 17 billion active parameters, albeit its total parameters reach 400 billion, which is aided by 128 specialists. It is a multimodal model that can process both image and text data and handles 12 languages.
Maverick is more appropriate for multilingual assistants and chatbots, creative writing, and tasks such as image captioning and translation. Its versatility and multimodal input capability make it a gem for companies that are global in nature or require flexible conversational AI.
Behemoth is now in training and is meant to redefine AI performance. With 2 trillion total parameters and 288 billion active ones, it's made for heavy-duty applications that require high-level reasoning and multimodal at scale.
This model will be the basis for advanced AI research, training future AI systems, and facilitating enterprise-scale data analysis. Behemoth strives to break boundaries in artificial intelligence.
Meta’s Llama 4 models bring cutting-edge AI performance and efficiency to the open-access world—making enterprise-grade innovation more accessible than ever before.
| Benchmark | Maverick Score | Comparison |
|---|---|---|
| MMLU (Knowledge) | ~87% | On par with GPT-4 & Claude 3 |
| ARC (Reasoning) | Top-tier | Slightly below GPT-4, ahead of Gemini |
| GSM8K (Math) | Competitive | Close to Claude 3 |
| Winogrande (Logic) | Very strong | Near GPT-4 levels |
| Image QA (Multi) | Solid multimodal results | Outperforms Gemini 1.0 in some tasks |
Llama 4 Maverick delivers elite performance—rivalling proprietary giants at a fraction of the cost.
Meta’s pricing is designed to be developer-friendly, opening high-end AI capabilities without the high-end price tag.
| Model | Cost per Million Tokens |
|---|---|
| GPT-4o | $4.38 / million tokens |
| Maverick | ~$0.19–$0.49 / million tokens |
| Llama 4 | Up to 20x more cost-efficient than GPT-4 |
Llama 4 employs a Mixture of Experts (MoE) architecture, which only triggers the most appropriate sub-models, or "experts," for every query. This architecture provides quicker inference rates, reduced memory usage, and task-specific knowledge.
Imagine it as summoning only the correct experts to work on a task rather than notifying the whole hospital staff—brighter, quicker, and more efficient.
Each Llama 4 model possesses specialized strengths that can be used in practical applications. Scout is particularly good at legal tech by summarizing long contracts and finance by scanning long annual reports. In academia, it is particularly good at huge literature reviews.
Maverick is best suited for multi-language customer support, content and creative writing software, healthcare and e-commerce support platforms. Behemoth, on the other hand, will be the foundation for state-of-the-art AI research, big-scale multimodal data processing, and prototyping next-generation general-purpose AI systems once it has been deployed.
Llama 4 is defined by its multimodal and multilingual support, ultra-long context window size of as much as 10 million tokens, and sparse activation structure that facilitates efficient inference. Mix that with enterprise-class prices and open-access deployment flexibility, and you have a model suite positioned to serve up to businesses and developers.
Llama 4 is shaping up to be a major force in the AI ecosystem—bringing near-GPT-4 performance to the open-access world, with a modular lineup built for real-world tasks and budgets.
Whether you're building AI tools, processing huge datasets, or pushing the boundaries of research, there’s a Llama 4 model ready for the ride.
Scout and Maverick are live. Behemoth is coming. Saddle up—the AI frontier just got a lot wilder.
