The Llama 4 family of models from Meta offers several key features that set them apart:

1. Varying Model Sizes: Llama 4 comes in three different sizes - Llama 4 Scout (smallest), Llama 4 Maverick (medium), and Llama 4 Behemoth (largest). This allows users to choose the model that best fits their hardware and performance requirements.

2. Multimodal Capabilities: All Llama 4 models are multimodal, meaning they can understand and process both text and images. This makes them versatile for a wide range of applications.

3. Unprecedented Context Window: The Llama 4 Scout model boasts an industry-leading 10 million token context window, which is about 5 million words. This is a significant improvement over previous models like GPT-4 (128,000 tokens) and Gemini 2.1 (2 million tokens). This larger context window enables the models to maintain better context and coherence in conversations.

4. Efficient Architecture: The Llama 4 models utilize a "mixture of experts" approach, where only the relevant parts of the model are activated for a given task. This allows for high performance while using only 17 billion active parameters, which can run on a single Nvidia H100 GPU.

5. Competitive Benchmarks: The Llama 4 models have demonstrated strong performance in various benchmarks, often outperforming closed-source models from leading AI companies. This includes the Llama 4 Maverick model, which performs on par with more resource-intensive models like GPT-4 and Gemini 2.0 Flash.

6. Open-Source Availability: The Llama 4 models are open-source, providing developers with more flexibility and control compared to closed-source models that require API access. This allows for customization, fine-tuning, and self-hosting of the models.

Overall, the Llama 4 family of models from Meta offers a compelling combination of performance, efficiency, and accessibility, making them a promising choice for a wide range of natural language processing and multimodal applications.

Llama 4 Scout is the smallest model in the Llama 4 family, but it packs a punch. With 17 billion active parameters and 109 billion total parameters, this general-purpose model boasts impressive capabilities, including multimodal understanding of both text and images.

The key features of Llama 4 Scout include:

• Efficient Architecture: The model uses a mixture of expert approach, where only the relevant parts of the model are activated for a specific task. This allows it to achieve high performance while using only 17 billion active parameters, which can run on a single Nvidia H100 GPU.
• Industry-Leading Context Window: Llama 4 Scout has an astounding 10 million token context window, far surpassing the 128,000 token context window of ChatGPT and the 2 million token context window of Gemini. This allows the model to maintain context and coherence over much longer inputs and conversations.
• Impressive Benchmarks: In benchmarks, Llama 4 Scout outperforms the older Llama 3.3 model, as well as other open-source models like Gemini 2.1 and Flashlight, across a range of tasks, including multimodal capabilities.

The combination of efficiency, large context window, and strong performance makes Llama 4 Scout a compelling option for developers and researchers looking to leverage the power of large language models in their applications.

Llama 4 Maverick is the medium-sized model in the Llama 4 family, offering a balance of performance and efficiency. With 128 experts and 400 billion total parameters, Maverick manages to maintain a lean 17 billion active parameters, allowing it to run on a single NVIDIA H100 GPU.

Despite its relatively small active parameter count, Maverick outperforms larger models like GPT-4 and Gemini 2.0 Flash across a range of benchmarks, including coding and reasoning tasks. This efficiency is reflected in its cost, starting at just 19 cents per 1 million input and output tokens, on par with Gemini 2.0 Flash and more affordable than DeepSeek.

The key advantage of Maverick is its ability to deliver high-performance results without the need for extensive hardware resources. This makes it an attractive option for developers and organizations looking to leverage the power of large language models without the associated infrastructure costs.

Llama 4 Behemoth is the largest and most capable model in the Llama 4 family, boasting an impressive 2 trillion parameters. Despite being in preview mode, this model is already outperforming other top AI models like Gemini 2.0 and Clova Sonnet 3.7 in various benchmarks.

The Behemoth model features 288 billion active parameters and 16 experts, allowing it to efficiently utilize its vast parameter count. This efficiency, combined with its impressive performance, makes the Behemoth a compelling choice for developers and companies looking to build applications using a large language model.

One of the key advantages of the Llama 4 Behemoth is its open-source nature. Unlike closed-source models that require API access and come with limitations, the Behemoth offers developers more flexibility and control. They can self-host the model, fine-tune it, and customize it to their specific needs, providing a level of customization that is often not possible with proprietary models.

While the Behemoth is currently in preview mode and not yet available for download, the other Llama 4 models, Scout and Maverick, are already accessible. Developers can request access to these models and start experimenting with them, taking advantage of their impressive capabilities, including the industry-leading 10 million token context window.

The Llama 4 family of open-source large language models from Meta offers several key advantages over closed-source alternatives:

1. Flexibility and Customization: As open-source models, Llama 4 can be easily customized, fine-tuned, and self-hosted by developers. This provides much more control and flexibility compared to closed-source models that require API access and are limited in their customization options.

2. Cost-Effectiveness: The Llama 4 models, particularly the Maverick variant, are priced very competitively, starting at just 19 cents per 1 million input and output tokens. This is on par with or cheaper than other open-source models like Deepseek, making Llama 4 a cost-effective choice for developers and businesses.

3. Impressive Performance: Despite their open-source nature, the Llama 4 models have demonstrated impressive performance, often outperforming closed-source models like GPT-4 and Gemini 2.0 in various benchmarks, including coding, reasoning, and domain-specific tasks.

4. Scalable Context Window: The Llama 4 Scout model boasts an industry-leading 10 million token context window, a significant improvement over the 128,000 token context window of ChatGPT. This expanded context window enables Llama 4 to handle much larger input and output, making it better suited for working with large documents and datasets.

5. Multimodal Capabilities: All Llama 4 models are multimodal, meaning they can understand and process both text and images, providing more versatile capabilities compared to some closed-source models that are limited to text-only interactions.

6. Open-Source Transparency: As open-source models, the Llama 4 family provides transparency into their inner workings, allowing developers to better understand and audit the models, which can be important for certain applications or regulatory requirements.

By offering these advantages, the Llama 4 models from Meta present a compelling alternative to closed-source large language models, empowering developers and businesses to leverage powerful AI capabilities while maintaining more control and flexibility over their technology stack.

To try Llama 4 for yourself, you have a few options:

1. Web Demo:

   • Visit the Meta AI website at meta.ai and interact with the Llama 4 model directly on the website.
   • Use the Grock website, which allows you to choose between the Llama 4 Scout and Maverick models to test.

2. Download the Models:

   • To download the Llama 4 models, visit the Hugging Face website and follow the instructions.
   • Note that the Llama 4 Behemoth model is still in preview and not available for download yet.
   • You'll need to have the hardware (e.g., Nvidia H100 GPU) to run the larger Llama 4 models.

3. Request Access:

   • If you want to access the Llama 4 models, you can fill out the request form provided by Meta.
   • Select the Llama 4 model you're interested in, and Meta will provide you with the necessary information to access the model.

Regardless of the option you choose, you'll be able to test and explore the capabilities of the Llama 4 models, including their impressive context window and performance benchmarks.

The release of Meta's Llama 4 models, including Llama for Scout, Llama for Maverick, and Llama for Behemoth, represents a significant advancement in the field of large language models. These models offer impressive capabilities, with the smallest Llama for Scout boasting an industry-leading 10 million token context window, far surpassing the 128,000 token context window of ChatGPT.

The use of a mixture of experts approach in these models allows for efficient resource utilization, with only the necessary parts of the model being activated for a given task. This efficiency is further demonstrated by the models' ability to run on a single NVIDIA H100 GPU.

The benchmarks showcased in the transcript highlight the impressive performance of these Llama 4 models, often outperforming closed-source models from leading AI companies. This is a testament to the power of open-source models, which offer developers and companies greater flexibility, customization, and control compared to API-based models.

The availability of these Llama 4 models through platforms like Meta AI and Grock provides users with the opportunity to explore and experiment with these cutting-edge language models. As the technology continues to evolve, the Llama 4 family of models promises to push the boundaries of what is possible with large language models, particularly in terms of context handling and efficient resource utilization.