What is Meta Llama?

Meta Llama (originally named Large Language Model Meta AI) was developed to be a competitor of other generative AI models, including OpenAI’s ChatGPT, Google’s Gemini and DeepSeek v3.1. Together, the Meta Llama models comprise a set of large language models (LLMs) designed to analyze various types of data and generate all kinds of content.

Initially released in February 2023, Llama has been upgraded several times since its introduction. The latest version is the 4th generation Llama 4, which was released in April 2025. Llama 4 is natively multimodal, supports multiple data types and languages, and is used to power Meta AI in its suite of products, including Facebook, Instagram and WhatsApp. 

Businesses can use Llama for tasks such as content generation, summarization, translation, image analysis and conversational AI. Developers use the model by integrating it into other applications and leveraging Llama to develop new AI products and apps. The model can be downloaded from Llama.com or from HuggingFace.

Llama 4 upgrade: New features and capabilities

Meta has made significant changes with each of its upgrades. The latest set of models in Llama 4 includes Maverick and Scout (as well as the Behemoth model, which is still in testing as of May 2025). 

Models Llama 4 Scout and Llama 4 Maverick are “the first open-weight natively multimodal models with unprecedented context length support and our first built using a mixture-of-experts (MoE) architecture,” Meta stated. The models are intended for different purposes and include new upgrades like a larger supported context length.

Meta Llama 4 Maverick is considered to be a “general purpose” LLM. This model “contains 17 billion active parameters, 128 experts and 400 billion total parameters, offering high quality at a lower price compared to Llama 3.3 70B,” Meta notes.

Meanwhile, Meta Llama 4 Scout is a smaller general purpose model, with “17 billion active parameters, 16 experts, and 109 billion total parameters that delivers state-of-the-art performance for its class.” Meta notes that Llama 4 Scout “dramatically increases the supported context length from 128K in Llama 3 to an industry leading 10 million tokens.”

Llama 4 models have been enhanced with a number of new capabilities, including the following: 

• Natively multimodal: Designed and built as a single model to specifically handle multiple data types at the same time, including text, audio, speech, video and images, rather than adding these capabilities with separate models.
• Mixture-of-experts architecture: MoE models operate with a ‘router’ component that manages input tokens and uses specialized experts that work together to offer deeper results, with far less compute. 
• Expanded context windows: Long context processing and significant performance improvements.
• Extended multilingual support: Llama 4 supports an expanded set of languages (now at 12 up from 8 previously), including English, French, German, Hindi, Italian, Portuguese, Spanish, Thai, Arabic, Indonesian, Tagalog and Vietnamese.

Meta has also introduced a set of enhanced security features, including upgrades to existing security tools. These tools include: 

• Llama Guard 4: A unified safeguard across modalities that supports protections for text and image understanding. 
• LlamaFirewall: A security guardrail tool to help build secure AI systems and works with other Meta protection tools to detect and prevent AI system risks, such as insecure code or risky plugin interactions.
• Llama Prompt Guard 2: A safety classifier that helps guard LLM-powered applications against malicious inputs, including prompt injections and jailbreaks. 

Llama API 

At its first-ever AI developer conference, LlamaCon, Meta introduced a preview version of Llama API, a developer platform for Llama application development that’s currently being beta tested by a chosen group of developers and organizations. This API was developed to enable businesses to more easily build AI products, Reuters reported. 

With Llama API, developers can more quickly generate keys as well as access interactive testing environments for Llama models, including Llama 4 Scout and Llama 4 Maverick. In addition, Meta provides a lightweight SDK in both Python and Typescript, while “Llama API is also compatible with the OpenAI SDK, making it easy to convert existing applications,” according to Meta. The platform can be exported for independent hosting, with SDKs available and privacy assurances for user data, the company said. The security tool Llama Guard 4 is also available on the Llama API. 

Common use cases for Meta Llama

Llama 4 was specifically designed to build highly personalized multimodal experiences. Some of the most common use cases and functions for the latest Llama model include the examples below.

• Multilingual chatbots and assistants: Can power high-quality multilingual assistant and chat applications with image understanding, without needing separate systems.
• Coding assistance: assist developers and users with code generation including through text prompts. 
• E-commerce: Enhanced e-commerce with visual understanding and ability to conduct visual search and product recommendations based on images. 
• Advanced processing: Capable of processing long documents, including understanding for structured data extraction. 
• Enhanced customer support: Can leverage Llama’s sophisticated image analysis capabilities. 
• Multilingual content creation: Creative content generation across multiple languages.
• Research applications: For complex research requiring text analysis and multimodal data integration.

A snapshot of Meta Llama’s role in the AI landscape

The release of Meta Llama 4 highlights the rapid pace of innovation in the generative AI space. Its open-weight, multimodal capabilities, expanded context length and enhanced security tools reflect broader industry trends aimed at improving usability, performance and safety in AI systems.

While Llama 4 introduces noteworthy features—such as MoE architecture and expanded multilingual support—it’s part of a larger movement among leading AI developers to build more flexible, scalable and accessible models. Its introduction of tools like the Llama API also points to growing interest in enabling easier integration and experimentation for developers and organizations.

As with other leading models, Llama 4 underscores the importance of continuous evolution in generative AI. For businesses and technical teams keeping an eye on this space, Llama serves as one example of how LLMs are being shaped to meet increasingly complex demands.