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Meta AI has officially released the Llama 4 model family—its most advanced and capable suite of open-weight AI models to date.
This launch marks a significant architectural shift for the Llama ecosystem, introducing two foundational innovations: Mixture-of-Experts (MoE) and native multimodality.
In this breakdown, you'll learn what sets Llama 4 apart, how its architecture works, and what it means for developers, researchers, and enterprise teams building with AI.
Llama 4 is Meta AI’s latest open-weight model series, including Llama 4 Scout, Llama 4 Maverick, and the upcoming Llama 4 Behemoth.
These models are the first in the Llama family to support two major features: a Mixture-of-Experts (MoE) architecture and native multimodal capabilities, enabling seamless processing of text, images, and videos.
They also introduce support for very large context windows—up to 10 million tokens—making them ideal for handling extended documents, multi-turn conversations, and enterprise-scale datasets.
Compared to Llama 3, Llama 4 represents a leap in both efficiency and capability. It delivers higher performance with fewer active parameters, supports longer context windows, and enables more advanced use cases across disciplines like search, education, and customer experience.
The transition from Llama 3 to Llama 4 brings measurable improvements, addressing both technical and practical business needs:
Context Window:
Llama 3: Supported a context window of 8,000 tokens.
Llama 4: Expands this to 1 million tokens in Llama 4 Maverick and up to 10 million tokens in Llama 4 Scout. This enhancement supports more complex business processes, such as long-form document analysis, multi-turn conversations, and large-scale data parsing.
Model Parameters:
Llama 3: Llama 3 70B model contained 70 billion parameters.
Llama 4: Introduces a more efficient Mixture-of-Experts architecture with fewer active parameters per request, offering optimal performance with reduced computational demands. For example, Llama 4 Scout uses 17 billion active parameters, leveraging 109 billion total parameters for better adaptability.
Multimodal Capabilities:
Llama 3: Text-only model, suitable for basic text processing.
Llama 4: Adds multimodal processing, enabling both text and image input/output. This innovation allows businesses to apply AI in more diverse contexts, including visual data interpretation and integrated multimedia solutions.
Refusal Rates:
Llama 3: Higher refusal rates for sensitive topics (7%).
Llama 4: Reduces refusal rates to below 2%, providing more balanced responses across a wider range of topics, which is crucial for maintaining brand consistency in customer-facing applications.
Training Data:
Llama 3: Trained on publicly available data.
Llama 4: Includes Meta's internal data from services like Instagram and Facebook, offering richer contextual understanding and improved responsiveness based on real-world interactions.
Multilingual Support:
Llama 3: Supported a limited multilingual token dataset.
Llama 4: Expands its capabilities by including 10x more multilingual tokens, with training on 200 languages, including more than 100 languages with over a billion tokens each, making it more suited for global enterprises and diverse customer bases.
Knowledge Cut-off:
Llama 3: Knowledge cut-off as of December 2023.
Llama 4: Updated with more recent data, including a March 2025 knowledge cut-off, ensuring more relevant and timely information for business applications.
Llama 4 introduces an MoE design in which only a small subset of model components—called "experts"—are activated per token. For example, Maverick uses 128 routed experts and one shared expert, but only a few are active per inference step.
This selective activation significantly reduces compute cost and latency without sacrificing performance. It enables the deployment of highly capable models on smaller hardware footprints, such as a single H100 GPU.
Llama 4 is the first Llama family to be designed for multimodal input from the ground up. It uses early fusion to integrate text, image, and video tokens directly into the training process.
Unlike prior-generation models that bolt on vision systems post-training, Llama 4 fuses modalities within the same model backbone—enabling unified reasoning across formats and more coherent multimodal outputs.
Llama 4 supports context lengths of up to 10 million tokens, a significant leap that unlocks new enterprise use cases—such as querying full-length research archives, entire codebases, or long-running customer chats.
This eliminates the need for complex retrieval pipelines in many scenarios, simplifying architecture and improving response quality in knowledge-intensive applications.
For developers, MoE reduces deployment costs and unlocks edge or low-latency applications. For researchers, native multimodality creates a foundation for more ambitious, real-world experiments.
And for product teams, Llama 4 enables richer, more responsive user experiences—from AI copilots that understand charts and visuals to support agents that handle images and instructions in real time.
Meta's Llama 4 release introduces three distinct models tailored to a range of enterprise and research use cases. These models are designed to support diverse performance requirements, from compact deployments to high-capacity, multimodal applications. Here’s how they compare:
Scout is the smallest model in the Llama 4 family. With 17 billion active parameters, 16 experts, and a total of 109 billion parameters, Scout is optimized for environments where efficiency and long-context understanding are critical.
Its most notable technical feature is a 10 million token context window, allowing the model to process large sequences of input without segmenting or losing coherence. This makes it suitable for:
Scout performs competitively with other small models on the market, and it is deployable on a single H100 GPU, making it well-suited for resource-constrained or high-throughput environments.
Maverick is the general-purpose foundation model in the Llama 4 series. It shares the same number of active parameters as Scout (17 billion) but is built on a significantly larger model backbone, with 400 billion total parameters and 128 experts.
Maverick was developed using a structured, multi-phase training approach, combining supervised fine-tuning with online reinforcement learning and direct preference optimization. This enables it to deliver strong performance across multiple dimensions, including:
Maverick is optimized for enterprise applications requiring flexibility, balanced performance, and reliability. It performs well in standardized benchmarks, particularly in multilingual and visual reasoning tasks, and is priced for cost-effective deployment compared to earlier models in its class.
Behemoth is Meta’s largest Llama 4 model and is currently still in training. It features 288 billion active parameters, with an estimated total of around 2 trillion parameters. It has been developed primarily as a teacher model to support training and fine-tuning of smaller systems.
Behemoth is focused on tasks that require high reasoning capacity and domain-specific knowledge. This includes:
It has already demonstrated competitive performance on benchmark evaluations, including outperforming other large-scale models in areas such as STEM reasoning and tool use. Its architecture includes curriculum learning components and prompt optimization techniques designed to support high-stakes AI research and model codistillation.
Each Llama 4 model is aligned with specific technical needs and business priorities. Their capabilities map to a wide range of enterprise and developer-facing applications.
Llama 4 Model: Scout and Maverick
Scout’s extended context length allows it to process entire codebases or project histories, enabling accurate documentation, summarization, and structural analysis. It can also be used to trace dependencies or evaluate legacy systems with minimal context loss.
Maverick adds deeper reasoning capabilities, making it more suitable for automated coding assistants, logic-driven refactoring tools, and structured code generation.
Llama 4 Model: Scout
With its 10M-token context window, Scout is suited for enterprise knowledge tasks involving long-form documents. These include:
Scout enables full-context processing of lengthy inputs without traditional token-window limitations.
Llama 4 Model: Maverick and Behemoth
Maverick supports basic multimodal input (image and text), allowing for image captioning, document parsing, and combined vision-language tasks. It can be deployed in tools that assist with form digitization, image-based search, or asset annotation.
Behemoth extends this capability further to support more complex multimodal workflows, such as video understanding or medical image analysis, where higher reasoning capacity is required.
Llama 4 Model: Maverick
Maverick’s balanced language fluency and reasoning ability make it suitable for internal and external assistant applications, including:
Its support for multiple languages and structured response formats enhances its usability across departments and geographies.
Llama 4 Model: Behemoth
Behemoth is positioned for use in high-performance environments and AI research pipelines. Its primary roles include:
It is not yet generally available but is intended to support advanced domains and long-horizon planning applications.
At the initial release, the model attracted significant attention from the community. The response was diverse, reflecting a wide range of perspectives, use cases, and expectations. Early adopters engaged actively—some offering praise, others raising concerns—while analysts and researchers began publishing detailed breakdowns of its capabilities.
To understand the model’s real-world performance, we analyzed user feedback and third-party evaluations, identifying key strengths and limitations. Below, we summarize the most notable observations, followed by insights into reactions from early adopters.
1. Industry-Leading Context Window
Llama 4 Scout supports a staggering 10 million token context window — by far the longest available in any major LLM.
2. Native Multimodal Intelligence
All Llama 4 models are trained from the ground up to handle both text and image inputs natively. This multimodal capability, combined with early fusion techniques, gives it an edge in tasks requiring deep integration of visual and linguistic data (e.g., visual reasoning, document analysis).
3. Efficient, Scalable Architecture
Llama 4’s Mixture-of-Experts architecture activates only a subset of parameters per input, allowing Scout and Maverick to deliver high performance while keeping inference costs low. For example:
4. Multilingual Reach
Pretrained on data spanning over 200 languages, Llama 4 demonstrates strong multilingual performance, thanks to a 10x increase in non-English tokens compared to Llama 3. This makes it a strong candidate for translation and international applications.
5. Open-Source Availability (with caveats)
While hardware limitations apply, Llama 4 continues Meta’s open-weight approach. Developers with high-performance GPUs appreciate the ability to fine-tune and deploy without vendor lock-in.
1. Underwhelming Coding Performance
Despite improvements, Llama 4's coding capabilities have been met with mixed feedback. For example, the “20 bouncing balls” benchmark yielded disappointing results, with some developers favoring smaller models like DeepSeek V3 for better performance.
2. Inference Bugs and Memory Inconsistencies
Early adopters reported bugs, such as the model forgetting or denying prior outputs in extended conversations and failing to adhere to structured prompts, like standardized multiple-choice formatting.
3. Overfitting to Benchmarks
Llama 4 has been critiqued for appearing optimized for specific benchmarks (e.g., LMSYS Arena) rather than real-world applications. This focus on producing emotionally satisfying outputs has resulted in shallow responses that may lack deeper reasoning.
4. Writing Quality and Recall Issues
Maverick’s writing style has been described as mechanical and lacking creativity, while Scout, despite its extensive context capabilities, struggles with reasoning depth. Long-context recall issues also persist, with vague responses and memory resets in practical use.
5. Limited GPU Accessibility
While Scout can operate on a single H100 GPU, Maverick and Behemoth require more advanced infrastructure. The higher resource demands, even for quantized versions, have made these models difficult to access for most users, frustrating the open-source community.
Reddit Devs and Evaluators praise Scout's long context and Maverick’s efficient performance but are skeptical about overall reliability in high-stakes tasks.
Enterprise testers appreciate the structured output and competitive pricing, especially compared to GPT-4o or Claude.
AI researchers note a "step change in data scale" but warn that early implementation issues (e.g., memory bugs) may limit the model’s short-term utility.
Llama 4 brings significant architectural advancements, particularly in context length, multimodality, and efficiency. However, its performance has faced scrutiny, especially regarding inconsistencies in key areas like reasoning and coding. While the community is optimistic about its potential, there remains a sense of cautious anticipation.
This only reinforces the need for task-specific evaluation frameworks like Deploy.AI's Agent Testing — where models are tested directly on the business tasks that matter most.
Llama 4 stands out for its groundbreaking architectural advancements, offering significant improvements in context length, multimodality, and efficiency. While performance has shown some inconsistencies, the model’s potential for flexibility and accessibility cannot be overlooked. As Meta continues to refine Llama’s capabilities, the community remains hopeful that the model will address its current shortcomings.
Looking ahead, Meta’s upcoming LlamaCon on April 29 promises to shed light on the latest developments in its open-source AI initiatives. This first-ever developer conference will focus on empowering developers with the tools needed to create innovative apps and products using Llama. Meta is poised to share key insights into their open AI strategy and highlight new features expected to strengthen Llama’s role in the generative AI landscape.
With significant industry backing and Meta’s continued commitment to open AI, Llama 4 and its future iterations are well-positioned to play a pivotal role in the future of AI.
Llama 4 Scout supports a 10M token context length, and Llama 4 Maverick supports 1M token context length.
Llama 4 Scout is best for code analysis, with its 10M token context length enabling efficient reasoning over large codebases. Maverick also performs well for coding tasks, especially with its multimodal capabilities for multi-document and visual analysis.
Yes, all Llama 4 models are multimodal, processing text, images, and video content.
Llama 4 Behemoth is still in training, with release details coming soon.
Yes, Llama 4 is open-source for developers to use and build on.
Llama 4 excels in long-context fact retrieval. Scout’s 10M token context window enables efficient retrieval and reasoning over large datasets, while Maverick supports 1M tokens and multimodal capabilities, ideal for summarizing multiple documents or analyzing extensive codebases.
Llama 4 outperforms Llama 3 with a 10M token context window, multimodal capabilities, and reduced refusal rates (to <2%). It also uses Mixture-of-Experts architecture, offering better efficiency with fewer active parameters, and supports 200 languages with updated data (with knowledge cut-off in March 2025).
Llama 4 is available on Meta’s open-source platform. Visit Llama’s website for access and integration guidelines.
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