Meta has just announced the release of LLaMA 4, a groundbreaking language model that promises to revolutionize the world of AI. This new model comes in three different versions - small, medium, and large - each with its own unique capabilities.

The smallest version, LLaMA 4 Scout, boasts an impressive 109 billion total parameters, with 17 billion active parameters and 16 experts. What sets this model apart is its industry-leading 10 million token context window, a significant improvement over the previous market-leading 2 million tokens. This expanded context window unlocks a vast array of use cases, allowing for more comprehensive and accurate analysis of large volumes of data.

The medium-sized model, LLaMA 4 Maverick, takes things a step further with 400 billion total parameters and 128 experts. This model outperforms GPT-4 and Gemini 2.0 Flash across a range of benchmarks, while maintaining a cost-effective price point of 19-49 cents per million input and output tokens.

The third and most powerful version, LLaMA 4 Behemoth, is a true frontier model, boasting an astounding 2 trillion total parameters. This model is currently in development and is expected to outperform the likes of OpenAI's GPT-4.5 and Anthropic's Claude models, setting a new standard for large language models.

What's particularly exciting about these LLaMA 4 models is their multimodal capabilities, allowing for the seamless integration of text, images, and other modalities. This opens up a world of possibilities for businesses and developers, enabling them to leverage the latest advancements in AI to automate workflows, extract insights from content, and build custom AI agents.

The release of LLaMA 4 marks a significant milestone in the evolution of AI, and the potential impact on various industries is truly remarkable. As we eagerly await the arrival of the LLaMA 4 Behemoth model and the upcoming reasoning capabilities, the future of AI has never been brighter.

Meta has just announced the release of LLaMA 4, a groundbreaking set of large language models with impressive capabilities. LLaMA 4 comes in three different versions: Small, Medium, and Large, each with its own unique features and performance characteristics.

LLaMA 4 Scout:

• 109 billion total parameters, with 17 billion active parameters and 16 experts
• Industry-leading 10 million token context window
• Outperforms previous-generation LLaMA models and other leading models like Gemini 3, Gemini 2.0 Flashlight, and Mistral 3.1 on a range of benchmarks
• Can be run on a single NVIDIA H100 GPU

LLaMA 4 Maverick:

• 400 billion total parameters, with 17 billion active parameters and 128 experts
• 1 million token context window
• Outperforms GPT-4.0 and Gemini 2.0 Flashlight across various benchmarks
• Offers an excellent performance-to-cost ratio, with a blended inference cost of 19-49 cents per million input and output tokens

LLaMA 4 Behemoth:

• 2 trillion total parameters
• Not yet released, but expected to outperform GPT-4.5, Claude, Sonnet 3.7, and Gemini 2.0 Pro on several STEM benchmarks
• Will serve as the foundation for distilling the other LLaMA 4 models

All three LLaMA 4 models are natively multimodal, capable of processing and generating text, images, and other modalities. They are also based on a Mixture of Experts architecture, which allows for efficient and scalable model training and inference.

The LLaMA 4 models are pre-trained on a vast multilingual dataset, with over 200 languages and more than 10 times the multilingual tokens compared to LLaMA 3. This extensive training data, combined with the models' large scale and advanced architectures, enables them to deliver state-of-the-art performance across a wide range of tasks and applications.

LLaMA 4 Scout is the smallest of the three LLaMA 4 models announced by Meta, but it is anything but small in capability. This 109 billion total parameter model boasts 17 billion active parameters and 16 expert modules, making it the best multimodal model in its class.

One of the standout features of LLaMA 4 Scout is its industry-leading context window of 10 million tokens. This is a massive leap from the previous market-leading context window of 2 million tokens, unlocking a wealth of new use cases that were previously limited by context size constraints.

Despite its smaller size compared to the other LLaMA 4 models, LLaMA 4 Scout outperforms previous-generation Llama models, as well as other prominent models like Gemini 3, Gemini 2.0 Flashlight, and Mistral 3.1, across a broad range of widely reported benchmarks. This impressive performance is achieved while still fitting within a single NVIDIA H100 GPU.

The key to LLaMA 4 Scout's success lies in its innovative architecture. It utilizes a Mixture of Experts (MoE) approach, where different parts of the model are specialized in different tasks, allowing for efficient and effective multimodal processing. Additionally, the model was pre-trained and post-trained with a 256k context length, empowering it with advanced length generalization capabilities.

The combination of its powerful performance, industry-leading context window, and efficient architecture make LLaMA 4 Scout a true powerhouse in the world of multimodal AI. As businesses and developers unlock the potential of their unstructured data with tools like Box AI, the impact of this model is poised to be transformative.

LLaMA 4 Maverick is a powerful model that outperforms its competitors across a range of benchmarks. With 17 billion active parameters and 128 experts, this model delivers best-in-class performance at a cost-effective price.

Compared to models like GPT-4.0 and Gemini 2.0 Flash, LLaMA 4 Maverick achieves comparable results on reasoning and coding tasks, but at less than half the active parameters. Its experimental chat version even scores an ELO of 1417, placing it at number two on the LM Marina leaderboard, just behind Gemini 2.5 Pro.

The cost-efficiency of LLaMA 4 Maverick is also noteworthy. With a blended rate of 19 to 49 cents per million input and output tokens, it is significantly cheaper to run than models like GPT-4.0, which costs $4.38 per million tokens.

In terms of performance, LLaMA 4 Maverick excels across a range of benchmarks. On the image reasoning MMU benchmark, it scores 73.4, outperforming the competition. It also delivers strong results on tasks like chart QA (90.0) and doc VQ QA (94.4), showcasing its versatility and capabilities.

Overall, LLaMA 4 Maverick represents a significant advancement in the field of large language models, offering a powerful and cost-effective solution for a wide range of applications.

The LLaMA 4 Behemoth model is the crown jewel of Meta's latest AI innovation. With a staggering 2 trillion total parameters, this model represents the frontier of AI intelligence.

While not yet publicly released, the Behemoth model is the foundation upon which the smaller LLaMA 4 Scout and Maverick models are built. Despite its massive scale, the Behemoth model is still in training, with the potential to further improve its capabilities by the time of its official launch.

According to the information provided, the Behemoth model outperforms the current state-of-the-art language models, including GPT-4.5, Claude, and Gemini 2.0 Pro, on several STEM-focused benchmarks. This impressive performance, combined with its sheer size, positions the Behemoth model as a true powerhouse in the world of large language models.

Notably, the Behemoth model utilizes a Mixture of Experts (MoE) architecture, similar to its smaller counterparts. While MoE may be considered a slightly dated approach, the team at Meta has recognized the potential to leverage reinforcement learning to imbue the model with true reasoning capabilities, taking it beyond the current generation of language models.

As the development of the LLaMA 4 Behemoth model continues, the AI community eagerly awaits its release, which promises to push the boundaries of what is possible in the realm of large language models and multimodal AI.

The LLaMA 4 models have demonstrated impressive performance across a wide range of benchmarks, showcasing their superiority over previous-generation models.

The LLaMA 4 Maverick model outperforms GPT-4.0, Gemini 2.0 Flash, and DeepSEEK V3.1 on various tasks, including image reasoning, chart QA, and doc VQ QA, while offering a significantly better cost-to-performance ratio.

The LLaMA 4 Scout model, the smallest of the three, also delivers better results than Llama 3.370B, Llama 3.1405B, Gemma 3.27B, Mistral 3.124B, and Gemini 2.0 Flashlight across a broad range of benchmarks. The only exception is the Live CodeBench, where Llama 3.370B still edges out the Scout model.

One of the standout features of the LLaMA 4 models is their industry-leading context window size. The LLaMA 4 Scout model, for instance, has a 10 million+ token context window, far surpassing the 2 million token context of previous-generation models. This expanded context window unlocks a wide range of enterprise use cases, enabling more effective analysis of large volumes of unstructured data.

Furthermore, the LLaMA 4 models demonstrate exceptional performance on the Needle in the Haystack test, with the LLaMA 4 Scout model achieving a sea of blue, indicating very high success rates in recalling information from massive text inputs.

Overall, the LLaMA 4 models have set a new benchmark for large language models, showcasing their dominance across a variety of tasks and their potential to revolutionize the way businesses and developers leverage AI for their content and data-driven applications.

Box AI is poised to revolutionize the way businesses leverage the latest advancements in AI technology. With the integration of the powerful LLaMA 4 models, Box AI is set to unlock the true potential of unstructured data that has long remained untapped.

The LLaMA 4 models, with their industry-leading context window of up to 10 million tokens, offer unprecedented capabilities in processing and understanding vast amounts of data. This breakthrough enables Box AI to automate document processing and workflows, extract valuable insights from content, and build custom AI agents that can work seamlessly with a company's existing data.

By leveraging the LLaMA 4 models, Box AI developers and businesses can now tackle complex challenges with ease. From extracting key metadata fields from contracts, invoices, and financial documents to automating workflows and answering questions about sales presentations or research reports, the possibilities are endless.

Moreover, Box AI's secure and compliant platform ensures that businesses can harness the power of these advanced AI models while maintaining the highest levels of data governance and security. This integration empowers enterprises to unlock the true value of their content, driving efficiency, productivity, and innovation across the organization.

As the world of AI continues to evolve, Box AI's partnership with the LLaMA 4 models positions it at the forefront of intelligent content management. Businesses can now confidently embrace the future of AI-powered content analysis and automation, unlocking new opportunities for growth and success.

The Llama 4 models employ a Mixture of Experts (MoE) architecture, which is a departure from the traditional monolithic language models. In this approach, the model is composed of multiple specialized "expert" sub-networks, each focusing on a particular aspect of the task. A "router" component dynamically selects the most appropriate experts to process the input, allowing the model to leverage the strengths of different sub-networks.

This modular design offers several advantages. First, it enables more efficient and scalable training, as the experts can be trained independently and in parallel. Second, the MoE architecture allows for better performance on diverse tasks, as the experts can specialize in different domains or capabilities. Finally, the dynamic routing mechanism helps the model adapt to the specific input, potentially leading to improved overall performance.

While the MoE approach may be seen as a slightly dated technique compared to the current trend towards "thinking" models, it serves as a solid foundation for the Llama 4 series. The ability to leverage reinforcement learning and other advanced techniques to imbue these models with more sophisticated reasoning capabilities is an exciting prospect for the future.

LLaMA 4 is a groundbreaking language model that boasts unparalleled multilingual capabilities. By pre-training on over 200 languages, including more than 100 with over a billion tokens each, LLaMA 4 has amassed an unprecedented level of linguistic knowledge.

This expansive multilingual foundation enables LLaMA 4 to seamlessly handle a wide range of languages, empowering global collaboration and communication. Whether you need to process documents, extract insights, or build custom AI applications, LLaMA 4's multilingual prowess ensures that language barriers are no longer a hindrance.

Compared to its predecessor, LLaMA 3, the latest iteration has increased its multilingual token count tenfold. This remarkable achievement underscores Meta's commitment to fostering a more inclusive and accessible AI ecosystem, where users from diverse linguistic backgrounds can leverage the power of advanced language models.

By embracing this level of multilingual proficiency, LLaMA 4 opens up new possibilities for global enterprises, researchers, and developers. From automating document processing in multiple languages to building AI-powered translation services, the model's versatility knows no bounds.

As the world becomes increasingly interconnected, the need for seamless cross-cultural collaboration has never been more pressing. LLaMA 4's multilingual capabilities are poised to be a game-changer, empowering users to bridge linguistic divides and unlock new frontiers of innovation.

Meta's Llama 4 models leverage FP8 (8-bit floating-point) precision during pre-training to achieve highly efficient model training. By using FP8 instead of higher precision formats, the team was able to maintain model quality while significantly improving training throughput and cost-effectiveness.

Specifically, when pre-training the massive Llama 4 Behemoth model using FP8 and 32,000 GPUs, they achieved an impressive 390 TFLOPS per GPU. This demonstrates the ability to train these large-scale models in a highly efficient manner, unlocking new possibilities for the development of powerful AI systems.

The use of FP8 precision allows for higher model flops utilization without sacrificing quality, making the training process more cost-effective and scalable. This approach aligns with Meta's focus on efficient model training, enabling the development of cutting-edge AI capabilities while optimizing for performance and resource utilization.

The licensing of Llama 4 continues to be a point of concern, as it was with the previous Llama 3 model. The new license comes with several limitations that may impact its widespread adoption.

Firstly, companies with more than 700 million active users must request a special license from Meta, who can grant or deny the request. This restriction echoes the previous issues with Llama 3, where Meta maintained control over the licensing.

Secondly, users must prominently display "Built with Llama" on their websites, interfaces, and documentation when using the model. This requirement may be seen as intrusive by some developers.

Additionally, any AI model created using Llama materials must include "Llama" at the beginning of its name. While this may not be a significant issue, it could be seen as a limitation on the flexibility of model naming.

Users must also include the specific attribution notice in a notice text file with any distribution, and they must comply with Meta's separate acceptable use policy, which may not be readily available or transparent.

These licensing restrictions, while not necessarily deal-breakers, may still be a concern for some developers and organizations who value more open and flexible licensing models. The community will need to carefully navigate these limitations to fully leverage the capabilities of Llama 4.

The release of Llama 4 by Meta is a significant milestone in the world of AI. The three versions of Llama 4 - Scout, Maverick, and Behemoth - offer impressive capabilities, with the Behemoth model boasting an astounding 2 trillion total parameters.

The Llama 4 models are designed to be natively multimodal, capable of processing text, images, and other modalities. They also utilize a mixture of experts architecture, which allows different parts of the model to specialize in different tasks.

One of the most notable features of Llama 4 is the industry-leading context window size, with the Scout model offering a 10 million token context length. This unlocks a wide range of use cases, particularly in enterprise settings where analyzing large volumes of unstructured data is crucial.

The benchmarks provided demonstrate the superior performance of Llama 4 across a variety of tasks, including image reasoning, math, and coding. The cost-effectiveness of the Maverick model is also highlighted, with a significantly lower cost per million input and output tokens compared to other leading models.

While the licensing and deployment limitations of Llama 4 may pose some challenges, the overall potential of these models is undeniable. The upcoming release of the Llama 4 Reasoning model is particularly exciting, as it could further expand the capabilities of the Llama 4 family.

In conclusion, the Llama 4 models represent a significant advancement in the field of AI, offering unprecedented scale, multimodal capabilities, and cost-effectiveness. As the technology continues to evolve, the impact of Llama 4 on various industries and applications is sure to be profound.