Complete Guide to Llama 4 Models: Scout, Maverick, and Behemoth

Meta recently launched Llama 4, a new generation of open-source artificial intelligence models that redefines industry standards. This model family includes Scout, Maverick, and Behemoth, each designed for specific use cases and representing a significant advance over Llama 3.

Overview of the Llama 4 Models

Meta’s Llama 4 models are a group of open, multimodal, and extremely efficient models. The most impressive is Llama 4 Maverick, which crossed the 1400-point threshold on the LMArena benchmark, outperforming models such as GPT-4o, DeepSeek V3, Gemini 2.0 Flash, and many others.

One of the most remarkable features of these models is their ability to handle contexts of 10 million tokens, the longest context window of any open-source LLM to date.

The Three Models and Their Specifics

Llama 4 Scout: Small, Fast, and Smart

Scout is the most efficient model in the Llama 4 family. It is a lightweight, fast model, ideal for developers and researchers who don’t have access to significant GPU resources.

Key Features:

• Architecture: Scout uses a Mixture of Experts (MoE) architecture with 16 experts, activating only 2 experts at a time, which results in 17 billion active parameters out of a total of 109 billion. It supports a context window of 10 million tokens.
• Efficiency: The model runs efficiently on a single H100 GPU thanks to Int4 quantization, making it an affordable high-performance option.
• Performance: Scout outperforms competitors such as Gemma 3, Gemini 2.0 Flash-Lite, and Mistral 3.1 across a variety of benchmarks.
• Training: Pretrained on 200 languages (including 100 with more than one billion tokens each) and on diverse image and video data, supporting up to 8 images per prompt.
• Applications: Thanks to its advanced visual reasoning capabilities, Scout is ideal for long-memory chatbots, code summarization tools, educational bots, and assistants optimized for mobile or embedded devices.

Llama 4 Maverick: Powerful and Reliable

Maverick is the flagship open-source model of the family. It is designed for advanced reasoning, programming, and multimodal applications. Although it is more powerful than Scout, it maintains good efficiency thanks to the same MoE strategy.

Key Features:

• Architecture: Maverick uses a MoE architecture with 128 routed experts and one shared expert, activating only 17 billion parameters out of a total of 400 billion during inference. It is trained with early fusion of text and image inputs and supports up to 8 images as input.
• Efficiency: The model runs efficiently on a single H100 DGX host or can be distributed across multiple GPUs.
• Performance: Maverick achieves an ELO score of 1417 on the LMSYS Chatbot Arena benchmark, outperforming GPT-4o and Gemini 2.0 Flash, while matching DeepSeek v3.1 in reasoning, programming, and multilingual capabilities.
• Training: Built with cutting-edge techniques such as MetaP hyperparameter scaling, FP8 precision training, and a dataset of 30 trillion tokens. It offers robust image understanding, multilingual reasoning, and cost-effective performance that outperforms the Llama 3.3 70B model.
• Applications: Its strengths make it ideal for collaborative AI programming, enterprise-level document understanding, and educational tutoring systems.

Llama 4 Behemoth: The Teacher Model

Behemoth is Meta’s largest model to date. It is not available for public use, but it played a crucial role in the development of Scout and Maverick.

Key Features:

• Architecture: Behemoth uses a MoE architecture with 16 experts and activates 288 billion parameters out of nearly 2 trillion during inference. It is natively multimodal and excels at reasoning, mathematics, and tasks that combine vision and language.
• Performance: Behemoth consistently outperforms GPT-4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro on STEM benchmarks such as MATH-500, GPQA Diamond, and BIG-bench.
• Role: It plays a key role as a teacher model, guiding Scout and Maverick through co-distillation with a novel loss function that balances soft and hard supervision.
• Training: The model was trained in FP8 precision, with optimized MoE parallelism delivering 10x speed gains over Llama 3, and a new reinforcement learning strategy. This includes sampling hard prompts, building multi-capability batches, and sampling from diverse system instructions.

Technical Innovations of the Llama 4 Models

Pretraining

Pretraining forms the foundation of a model’s knowledge and capabilities. Meta introduced several innovations at this stage:

• Multimodal Data: The Llama 4 models were trained on more than 30 trillion tokens from diverse text, image, and video datasets. They are natively multimodal, processing both language and vision from the start.

• Mixture of Experts (MoE): Only a subset of the model’s total parameters is active during each inference. This selective routing allows massive models like Maverick (400B parameters) and Behemoth (~2T) to be more efficient.

• Early Fusion Architecture: Text and vision inputs are trained jointly using early fusion, integrating both into a shared model architecture.

• MetaP Hyperparameter Tuning: This new technique lets Meta set per-layer learning rates and initialization scales that transfer well across different model sizes and training configurations.

• FP8 Precision: All models use FP8 for training, which increases compute efficiency without sacrificing model quality.

• iRoPE Architecture: A new approach using interleaved attention layers without positional embeddings and inference-time temperature scaling, helping Scout generalize to extremely long inputs (up to 10M tokens).

Post-training

Once the base models were trained, they were refined through a carefully crafted sequence:

• Lightweight Supervised Fine-Tuning (SFT): Meta filtered out easy prompts using Llama models as judges and used only the harder examples to refine performance on complex reasoning tasks.

• Reinforcement Learning (RL): Continuous RL training was implemented using hard prompts, adaptive filtering, and curriculum design to maintain reasoning, programming, and conversation capabilities.

• Direct Preference Optimization (DPO): After RL, lightweight DPO was applied to refine edge cases and response quality, balancing helpfulness and safety.

• Behemoth Co-distillation: Behemoth acted as a teacher by generating outputs to train Scout and Maverick. Meta even introduced a novel loss function to dynamically balance soft and hard supervision targets.

These steps produced models that are not only large, but deeply optimized, safer, and more capable across a variety of tasks.

Performance and Benchmarks

Llama 4 Maverick recently reached 2nd place in the overall ranking, becoming the 4th organization to exceed 1400+ on Arena! Here are the highlights:

• Open model, outperforming DeepSeek
• Tied for 1st place in hard prompts, programming, mathematics, and creative writing
• Huge jump over Llama 3 405B: 1268 → 1417
• Style control applied

On the ELO vs. cost chart, Llama 4 Maverick is positioned remarkably well, delivering top-tier performance at an estimated inference cost of $0.19 to $0.49 per million tokens, far lower than proprietary alternatives like GPT-4o and GPT-4.5.

Use Cases and Practical Applications

For Llama 4 Scout:

• Long-memory chatbots on mobile devices
• Code summarization and analysis tools
• Educational assistants for schools with limited resources
• Embedded applications requiring high-performing yet efficient AI

For Llama 4 Maverick:

• AI-assisted programming in the enterprise
• Analysis and understanding of large documents
• Intelligent multimedia tutoring systems
• Multilingual applications requiring sophisticated reasoning

How to Get Started with Llama 4

To use the Llama 4 models, you can:

1. Download the models: Available on Meta AI’s official website and on Hugging Face
2. Use the API: Meta offers an API for Llama 4 model inference
3. Deploy locally: For those with adequate hardware resources, deployment on local machines is possible

For Scout, a minimum of 24 GB of VRAM is recommended, while Maverick works best with at least 48 GB.

Conclusion

The Llama 4 models represent a significant advance in the field of open-source LLMs. With their innovative MoE architecture, 10-million-token context window, and impressive performance, Scout and Maverick offer competitive alternatives to proprietary models like GPT-4o and Claude.

What especially sets Llama 4 apart is the balance between performance and efficiency. Maverick demonstrates that it is possible to achieve top-tier performance at a significantly lower inference cost than its proprietary competitors, all while remaining open-source.

Whether you are a developer, a researcher, or a business, the Llama 4 models deserve your attention for their advanced multimodal capabilities, their efficiency, and their openness, which enables innovation and customization.