With the Launch of Llama 4, Meta AI has yet again proved its open source model is no less than its Chinese and US Competitors
With the launch of new artificial intelligence models coming from China and regular updates from US counterparts like Google’s Gemini, OpenAI’s ChatGPT and Anthropic’s Claude Sonnet 3.7, Meta is reaffirming its open source model’s capabilities. Meta AI has just announced today the launch of its latest generative artificial intelligence models, the Llama 4 family, marking a significant advancement in the field of open-source AI. This new suite of models includes Llama 4 Scout and Llama 4 Maverick, both available at launch, alongside a preview of Llama 4 Behemoth.
These models incorporate notable architectural innovations, primarily a Mixture of Experts (MoE) design and native multimodality through early fusion. Llama 4 Scout distinguishes itself with an industry-leading long context window, while Llama 4 Maverick offers a robust balance of performance across various tasks. The introduction of multiple models with distinct specializations indicates a strategic direction by Meta AI to address a broader spectrum of application needs and computational resource constraints. This approach, offering tailored solutions rather than a singular model, reflects a growing understanding of the diverse requirements within the AI community and industry.
Introduction to the Llama 4 Family
Meta AI continues its commitment to fostering open innovation within the artificial intelligence domain with the introduction of the Llama 4 family. Building upon the foundations of previous Llama models, Llama 4 represents the next generation of natively multimodal AI, capable of processing and understanding both text and visual information seamlessly. This advancement is underpinned by two key architectural innovations. The first is the implementation of Mixture of Experts (MoE) architecture. This design allows the models to be significantly larger in terms of total parameters, yet more efficient during inference by activating only a specific subset of these parameters for any given input.
In an MoE model, the network comprises numerous expert sub-networks, and for each input token, a routing mechanism intelligently directs the token to a selection of these experts for processing. This selective activation leads to increased computational efficiency during both the training and inference phases. The second major innovation is native multimodality achieved through early fusion. This technique involves integrating text, image, and video tokens into a unified model backbone right from the initial layers of processing. This early integration allows the model to jointly attend to and understand different modalities from the outset, potentially leading to a more coherent and comprehensive understanding compared to approaches that process modalities separately.
The vision encoder used in Llama 4 is an improved version based on MetaCLIP, further enhancing its multimodal capabilities. The Llama 4 family at launch consists of two primary models, Llama 4 Scout and Llama 4 Maverick, along with a preview of a larger model named Llama 4 Behemoth.
Llama 4 Scout: The Long-Context Multimodal Model
Llama 4 Scout is designed as a lightweight yet powerful model, optimized for tasks that require processing and understanding extensive sequences of information.
Detailed Technical Specifications: Llama 4 Scout features 17 billion active parameters, drawn from a total of 109 billion parameters. Its architecture incorporates a Mixture of Experts with 16 experts. A standout specification of Llama 4 Scout is its industry-leading context window of 10 million tokens.
Training Data, Modalities, and Languages: Llama 4 Scout was trained on a massive dataset exceeding 30 trillion tokens, encompassing a diverse mixture of text, images, and video data. It is a natively multimodal model, capable of understanding both text and visual inputs from the ground up. The model was pre-trained on an impressive 200 languages, with particularly strong support for 12 languages.
Key Unique Features and Strengths: The most notable feature of Llama 4 Scout is its unprecedented 10 million token context window. This allows the model to analyze and understand extremely long documents, entire code repositories, or extensive histories of user interactions, opening up new possibilities for tasks like Retrieval Augmented Generation (RAG), summarizing vast amounts of information, and reasoning over complex, lengthy data.
This capability is enabled by new training methodologies, including specialized datasets for long context extension and the implementation of the iRoPE (interleaved RoPE) architecture. Furthermore, Llama 4 Scout exhibits best-in-class image grounding capabilities. Image grounding refers to the model’s ability to accurately align user prompts with specific visual concepts and anchor its responses to particular regions within an image, leading to more precise and contextually relevant answers in visual question answering tasks.
Despite its substantial capabilities, Llama 4 Scout is designed to be efficient, capable of running on a single NVIDIA H100 GPU with Int4 quantization. This accessibility makes it a viable option for a wider range of researchers and developers, including those with limited computational resources. The model also benefits from advanced length generalization due to its pre-training and post-training with a 256K context length, further enhanced by the iRoPE architecture.
Intended Use Cases and Target Audience:
Llama 4 Scout is particularly well-suited for applications involving multi-document summarization, parsing extensive user activity for personalized experiences, and reasoning over large codebases. Its target audience includes developers and enterprises seeking a potent yet resource-efficient multimodal model with an exceptionally long context window. Additionally, its relatively smaller size makes it accessible to researchers and smaller organizations looking to explore advanced AI capabilities.
Performance Benchmarks and Comparisons:
Llama 4 Scout has demonstrated strong performance across various benchmarks, outperforming models such as Gemma 3, Gemini 2.0 Flash-Lite, and Mistral 3.1. It also exceeds comparable models in coding, reasoning, long context understanding, and image-related tasks. Specific benchmark scores include performance on MMLU, MMLU-Pro, MATH, MBPP, TydiQA, ChartQA, DocVQA, MMMU, MathVista, LiveCodeBench, and MTOB.
Llama 4 Maverick: The High-Performance Generalist
Llama 4 Maverick is positioned as a highly capable general-purpose model, designed to excel in a wide array of tasks, particularly those involving image and text understanding.
Detailed Technical Specifications:
Llama 4 Maverick shares the same active parameter count as Scout, with 17 billion, but its total parameter count is significantly higher at 400 billion. This model utilizes a larger Mixture of Experts architecture with 128 experts. Its context window size is reported as 1 million tokens, with some indications that it performs well on long-context benchmarks compared to other models.
Training Data, Modalities, and Languages:
Similar to Llama 4 Scout, Maverick was trained on over 30 trillion tokens of text, image, and video data. It is also natively multimodal, capable of processing both text and visual inputs. The pre-training encompassed 200 languages, with strong support for 12.
Key Unique Features and Strengths:
Llama 4 Maverick stands out for its strong capabilities in both image and text understanding. It is specifically optimized for high-quality chat interactions, making it suitable for conversational AI applications. The model also demonstrates robust performance in coding and reasoning tasks, achieving results comparable to the significantly larger DeepSeek v3 model. Notably, Llama 4 Maverick offers a best-in-class performance to cost ratio. An experimental chat version of Maverick achieved an impressive ELO score of 1417 on the LMArena leaderboard, further highlighting its strong conversational abilities.
Intended Use Cases and Target Audience:
Llama 4 Maverick is designed as a workhorse model for general assistant and chat use cases. It is ideal for applications requiring precise image understanding and creative writing capabilities. The model is targeted towards developers and enterprises seeking high-performance multimodal capabilities for building sophisticated chatbots and AI assistants that require top-tier intelligence and image understanding.
Performance Benchmarks and Comparisons:
Llama 4 Maverick outperforms GPT-4o and Gemini 2.0 Flash across a broad range of benchmarks. It achieves comparable results to DeepSeek v3 in reasoning and coding tasks. Specific benchmark scores include performance on MMLU, MMLU-Pro, MATH, MBPP, TydiQA, ChartQA, DocVQA, MMMU, MathVista, LiveCodeBench, and MTOB.
Llama 4 Behemoth: The Powerful Teacher Model (Preview)
Llama 4 Behemoth is the largest and most powerful model in the Llama 4 family, currently available as a preview as it is still undergoing training.
Detailed Technical Specifications:
Llama 4 Behemoth boasts 288 billion active parameters, with a total parameter count nearing 2 trillion. Its architecture also utilizes a Mixture of Experts with 16 experts.
Training Data, Modalities, and Languages:
Similar to its smaller counterparts, Behemoth was trained on over 30 trillion tokens, including text, image, and video data. It is natively multimodal, capable of understanding both text and visual information. The model was pre-trained on 200 languages.
Key Unique Features and Its Role as a Teacher Model:
The primary purpose of Llama 4 Behemoth is to serve as a highly intelligent teacher model for knowledge distillation, aimed at enhancing the capabilities of the smaller Llama 4 models, Scout and Maverick. Knowledge distillation is a process where a smaller “student” model learns from a larger, more capable “teacher” model, often achieving performance levels close to the teacher while requiring significantly fewer computational resources.
Meta AI developed a novel distillation loss function that dynamically adjusts the weighting of soft and hard targets throughout the training process to optimize this knowledge transfer. Behemoth demonstrates state-of-the-art performance for non-reasoning models on various benchmarks, including math, multilinguality, and image understanding. The post-training process for Behemoth involved significant refinements, including a high degree (95%) of supervised fine-tuning (SFT) data pruning to focus on higher-quality examples. This selective pruning helps to improve the model’s efficiency and performance. Additionally, a large-scale reinforcement learning (RL) phase was implemented, utilizing a continuous online RL strategy with adaptive data filtering to further enhance the model’s intelligence and conversational abilities.
Anticipated Performance:
According to Meta AI, Llama 4 Behemoth is expected to outperform leading models such as GPT-4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro on several STEM benchmarks, underscoring its potential as one of the most powerful language models available.
Comparative Analysis of Llama 4 Models
The Llama 4 family comprises three distinct models, each with unique specifications and intended use cases. A summary of their key characteristics is provided in the table below:
| Model Name | Active Parameters | Total Parameters | Number of Experts | Context Window Size | Multimodality | Intended Use Cases |
| Llama 4 Scout | 17 billion | 109 billion | 16 | 10 million tokens | Native | Multi-document summarization, personalized tasks, reasoning over codebases, image grounding, visual reasoning. |
| Llama 4 Maverick | 17 billion | 400 billion | 128 | 1 million tokens | Native | General assistant and chat, precise image understanding, creative writing, coding, reasoning, multilingual applications. |
| Llama 4 Behemoth | 288 billion | ~2 trillion | 16 | Not publicly stated | Native | Teacher model for knowledge distillation, advanced intelligence, state-of-the-art performance on math, multilinguality, and image benchmarks. |
This table highlights the trade-offs between model size, context window, and intended applications within the Llama 4 family. Scout prioritizes an exceptionally long context, while Maverick offers a balance of performance across various tasks, and Behemoth serves as a powerful foundation for improving the capabilities of the other models.
Llama 4: Advancements Over Previous Generations
The Llama 4 models represent a significant leap forward compared to the Llama 3 series. One of the most significant advancements is the introduction of native multimodality. Unlike Llama 3, both Llama 4 Scout and Maverick can inherently process and understand both text and visual information from the beginning, thanks to the early fusion technique.
Another key improvement is the dramatically increased context length, particularly for Llama 4 Scout, which boasts a 10 million token context window, a substantial increase from Llama 3’s 128K. The adoption of the Mixture of Experts architecture in Llama 4 is a fundamental change that contributes to both improved efficiency and higher quality models compared to the dense transformer architecture used in Llama 3.
Llama 4 also exhibits enhanced multilingual capabilities, having been pre-trained on 200 languages, a significant expansion from the 8 languages supported by Llama 3.3. Across various benchmarks, Llama 4 demonstrates improved performance compared to its predecessors. Furthermore, Meta AI has made progress in reducing bias in Llama 4, with the models showing improvements in refusing less on debated topics and providing more balanced responses. Finally, the training data size for Llama 4 has more than doubled compared to Llama 3, with over 30 trillion tokens used for pre-training.
Llama 4’s introduction positions it as a strong contender in the competitive landscape of large language models, offering compelling capabilities compared to models like GPT-4o, Gemini, Claude, and DeepSeek. While some analyses suggest that Llama 4 might not yet match the reasoning capabilities of models like GPT-4o and DeepSeek R1 in all complex scenarios , its strengths in areas like image grounding and the exceptionally long context window of Scout provide unique advantages.
Licensing and Availability of Llama 4
The Llama 4 models are released under the Llama 4 Community License Agreement, a custom commercial license. This license permits broad commercial use but includes a specific restriction for companies with over 700 million monthly active users, requiring them to request a separate license from Meta.
Llama 4 Scout and Llama 4 Maverick are readily available for download on platforms such as llama.com and Hugging Face as mentioned at the end of this article. This widespread availability underscores Meta’s commitment to making these advanced models accessible to a broad community of developers and researchers. The licensing terms of Llama models have been a subject of ongoing discussion within the open-source community, with some criticisms regarding the restrictions imposed by the license.
The launch of the Llama 4 family represents a significant milestone in the evolution of open-source artificial intelligence, particularly in the domain of multimodal models. Llama 4 Scout introduces an unprecedented long context window, enabling new possibilities for processing and understanding vast amounts of information. Llama 4 Maverick offers a robust and versatile model with strong performance across a wide range of tasks, including impressive capabilities in image and text understanding, coding, and reasoning.
The preview of Llama 4 Behemoth hints at even greater potential, serving as a powerful teacher model to further enhance the capabilities of its smaller counterparts. The architectural innovations of Mixture of Experts and early fusion multimodality underscore Meta AI’s commitment to pushing the boundaries of efficiency and capability in large language models. While Llama 4 demonstrates strong competitiveness over proprietary models in many areas, its unique strengths in long context and multimodality position it as a valuable asset for a wide range of applications and industries. Meta AI’s continued focus on open innovation ensures that these advancements will contribute to the broader progress of artificial intelligence.
For more information: https://ai.meta.com/blog/llama-4-multimodal-intelligence/
To download Meta Llama 4: