Meta AI Llama 4 Scout vs Llama 4 Maverick vs Llama 4 Behemoth: models available today, actual features, technical limits, and operational differences

Today, the Meta AI ecosystem offers several Llama 4 models, each with distinct functions and targets.

Within the Meta AI lineup, the Llama 4 family represents the cutting edge for chatbots, assistance, and multimodal applications in both consumer and enterprise contexts. The models that are actually available differ in architecture, context window size, reasoning capability, and degree of openness. Users access these models via Meta AI platforms (Instagram, WhatsApp, Messenger, dedicated web app, and Ray-Ban Meta), selecting the most suitable option among Llama 4 Scout, Llama 4 Maverick, and—for now, only in closed beta—Llama 4 Behemoth. Each is tailored for different technical and operational needs and reflects a deliberate design choice by Meta focused on efficiency, scalability, and seamless integration with Meta group services.

Llama 4 Scout is designed for long workflows and massive data analysis, with the most extensive context window currently available.

Llama 4 Scout stands out for its ability to handle up to 10 million tokens of context, the highest value released in the consumer space: this means it can analyze, summarize, or manipulate entire manuals, databases, code archives, video transcripts, or very long data sequences in a single step, maintaining coherence and historical references without “forgetting.”Technically, Scout is built on a Mixture-of-Experts (MoE) architecture with 16 active “experts,” able to optimize inference and reduce computational load: in certain configurations, the model can even run on a single optimized Nvidia H100 GPU, making it accessible for advanced deployments on enterprise infrastructures. Scout natively supports text, image, and video input, enabling multimodal analysis and cross-referencing between different media. Its best performance comes in tasks like long-document summarization, multi-file coding, forensic analysis, data verification, and content generation requiring the maximum possible “memory” in the prompt.

Llama 4 Maverick strikes a balance between reasoning power and response speed for most practical uses.

Llama 4 Maverick is conceived as a generalist model, designed to cover a wide range of use cases in a balanced way. With a 1 million token context window, Maverick provides enough “memory” to manage long conversations, complex dataset analyses, detailed logs, and structured work on code or technical texts, while keeping response times very short.

On the architectural side, Maverick uses an MoE with 128 experts, enabling advanced reasoning capabilities and excellent performance on coding, calculation, and data analysis benchmarks: recent comparisons place it above rival models like GPT-4o or Gemini 2.5 Flash for many programming and logical text generation activities.

The model natively supports multimodal input and is particularly suited for interactive bots, virtual assistants, technical autocompletion, enterprise chat, and all workflows that require execution speed, robustness, and consistency in handling information. Economically, Maverick is also more cost-effective per token compared to direct competitors, supporting scalable deployment in large professional projects.

Llama 4 Behemoth is Meta’s frontier model, currently available only in closed beta to selected partners.

Llama 4 Behemoth represents the new generation “teacher model,” with a scale of 288 billion active parameters (out of a total of 2 trillion), and is designed more as a distillation and advanced research platform than a direct consumer model. At the moment, Behemoth is not available to the public, but Meta uses it for internal training and evaluation of other Llama 4 models, as well as for pilot projects with academic and industrial partners. Its MoE architecture ensures a combination of depth, accuracy, and “teaching” capability for distilled models like Scout and Maverick. Early internal benchmarks and reports from early partners indicate performance exceeding GPT-4.5 and Claude Sonnet 3.7 in STEM and advanced analysis domains, but with hardware and operational requirements that are far beyond the standard consumer deployment.

All Llama 4 models share Mixture-of-Experts architecture, multimodality, and controlled openness through licensing.

From a technical standpoint, the Llama 4 family uses Mixture-of-Experts architecture in all its variants: only a subset of “neurons” (experts) activates at each generated token, significantly reducing computational load and improving efficiency in both training and inference. Multimodality is native, supporting text, images, and video input (the latter a new feature compared to competitors), as well as responses in at least 12 different languages. On the openness front, Scout and Maverick are published as “open weight” models but with restricted-use licenses: large-scale commercial applications require specific agreements with Meta, while development, research, and non-profit uses are allowed up to 700 million monthly users. Operationally, these models allow for customized deployments, proprietary tuning, and the integration of multimodal capabilities within both enterprise and consumer Meta services.

The practical limits and strengths of Llama 4 models vary according to target, context, and available resources.

Operationally, Scout excels when manipulating large archives, developing “memory-intensive” workflows, or integrating AI into knowledge management and document analysis processes; Maverick is the ideal compromise for all business scenarios where speed, consistency, and logic capabilities are required at low cost; Behemoth remains reserved for the research frontier and has no direct impact on consumer users. Actual performance depends heavily on usage context, available hardware, and the depth of customizations applied, but the scalability and controlled openness of the Llama 4 family make it today the most flexible AI platform for those working in the Meta ecosystem or seeking adaptable models for complex, multi-format applications.

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SUMMARY TABLE

Model	Active / Total Parameters	Context Window	Multimodality	Availability	Best Use Case	Unique Strength
Llama 4 Scout	17B / 109B	10M tokens	Text, Images, Video	Public (Meta apps, open weights for research/dev)	Long-context workflows, knowledge management, document analysis	Largest context window (10M); efficient (runs on single H100 GPU)
Llama 4 Maverick	17B / 400B	1M tokens	Text, Images, Video	Public (Meta apps, open weights for research/dev)	Coding, chatbots, technical assistants, enterprise bots	Best balance of reasoning, speed, and cost efficiency
Llama 4 Behemoth	288B / 2T	Not public	Text, Images, Video	Closed beta (research/partners only)	Advanced research, model distillation, STEM tasks	Teacher model for distillation; “frontier” benchmark performance

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