LLaMA ✓

Model Sizes:

• Scout: 109B parameters (17B active via MoE)
• Maverick: 400B parameters
• Behemoth: In training

Multimodal Capabilities: Processes text, images, and videos.

Architecture: Introduced Mixture-of-Experts (MoE) for computational efficiency.

Performance Enhancements:

Improved coding, reasoning, and multilingual tasks.

Optimized for long-context processing.

Applications: General assistant tasks, creative writing, document summarization, and advanced visual comprehension.

Licensing: Stricter terms, especially in the EU; special approval required for enterprises with >700M MAUs.

Llama 3.2 builds upon Llama 3.1 by introducing new model sizes—1B, 3B, 11B, and 90B parameters—allowing deployment on devices ranging from mobile to servers.

The 11B and 90B models add multimodal capabilities, processing both text and images for tasks like visual reasoning, which Llama 3.1 lacks.

Developer experience is enhanced with Llama Stack, offering streamlined development, better tool integration, and flexible deployment options across various programming languages.

Overall, Llama 3.2 offers more versatility, multimodal support, and improved developer tools compared to Llama 3.1.

LLaMA 3.1 is an updated version of LLaMA 3, bringing several key enhancements in performance, efficiency, and functionality. It offers improved accuracy and contextual relevance in responses, with better handling of complex queries and specialized tasks compared to LLaMA 3.

Safety features in LLaMA 3.1 have been further advanced, incorporating enhanced mechanisms to minimize the risk of generating harmful or biased content. The model aligns more closely with ethical standards, ensuring that its outputs are even more reliable and responsible.

LLaMA 3.1 benefits from an upgraded training dataset, providing access to a broader and more current range of information. This results in more precise and relevant answers, improving the quality of interactions and content creation beyond what was achieved with LLaMA 3.

The user experience is smoother with LLaMA 3.1, featuring more natural conversation patterns and improved engagement. Enhanced support for diverse applications, including more sophisticated multimodal tasks, makes it a more versatile tool compared to LLaMA 3.

Overall, LLaMA 3.1 builds on the foundation of LLaMA 3 with significant improvements in accuracy, safety, and usability, making it a more advanced and effective AI model.

LLaMA 3 is a significant upgrade over LLaMA 2, offering notable improvements in performance, efficiency, and versatility. It provides more accurate and contextually nuanced responses, with enhanced capabilities in handling complex tasks and specialized domains compared to LLaMA 2.

Safety features in LLaMA 3 have been further refined, reducing the risk of generating harmful or biased content. The model is better aligned with ethical standards and incorporates advanced mechanisms to ensure that its outputs are both reliable and responsible.

LLaMA 3 benefits from an even more updated training dataset, which allows it to draw from a wider and more current range of information. This results in more precise, relevant, and up-to-date answers, significantly enhancing the overall quality of interactions and content generation.

The user experience with LLaMA 3 is markedly improved, featuring smoother and more natural conversation patterns along with greater engagement. The model offers enhanced support for diverse applications, including more advanced multimodal tasks and integration with other data types.

Overall, LLaMA 3 builds on the advancements of LLaMA 2 with substantial enhancements in accuracy, safety, and usability, making it a more advanced and capable AI tool for a wide range of applications.

LLaMA 2 is an enhanced version of LLaMA 1, bringing notable improvements in performance, versatility, and safety. It delivers more accurate and contextually relevant responses, with enhanced capabilities in handling complex tasks and specialized domains compared to LLaMA 1.

Safety features in LLaMA 2 are further refined, reducing the risk of generating harmful or biased content. The model is better aligned with ethical standards, ensuring that its outputs are more reliable and responsible.

LLaMA 2 benefits from an updated training dataset, which allows it to draw from a broader and more current range of information. This results in more precise and relevant answers, improving the overall quality of interactions and content generation.

The user experience is notably enhanced with LLaMA 2, featuring more natural conversation patterns and improved engagement. It also offers better support for diverse applications, making it more adaptable for various use cases compared to LLaMA 1.

Overall, LLaMA 2 builds on the foundation of LLaMA 1 with significant advancements in accuracy, safety, and usability, making it a more powerful and refined AI tool.