meta llama: everything you need to know about the open generative ai model

Meta’s Llama: A Comprehensive Overview

Similar to other leading technology corporations, Meta has developed its own prominent generative AI model, known as Llama.

Llama distinguishes itself from many major models through its “open” nature.

This openness allows developers to download and utilize the model with considerable flexibility, subject to specific restrictions.

Open Access vs. API-Based Models

This approach differs significantly from models such as Anthropic’s Claude, Google’s Gemini, xAI’s Grok, and the majority of OpenAI’s ChatGPT offerings.

These alternative models are typically accessed exclusively through Application Programming Interfaces (APIs).

Cloud Availability and Developer Resources

Recognizing the value of providing developers with options, Meta has established partnerships with several vendors.

These partnerships include AWS, Google Cloud, and Microsoft Azure, to offer cloud-based versions of Llama.

Furthermore, Meta provides a comprehensive suite of resources for developers.

This includes tools, libraries, and detailed guidance within the Llama cookbook.

These resources are designed to facilitate the fine-tuning, evaluation, and adaptation of the models to specific application areas.

Advancements in Llama Generations

With the introduction of newer iterations, such as Llama 3 and Llama 4, the model’s capabilities have been significantly enhanced.

These advancements encompass native multimodal support and expanded availability across various cloud platforms.

Key Information About Meta’s Llama

This article provides a detailed examination of Meta’s Llama, covering its functionalities, available editions, and deployment options.

We will continually update this information as Meta releases new upgrades and developer tools to support the model’s ongoing development and use.

Introducing Llama: A Family of Models

Llama represents a collection of models, rather than a single entity. The most recent iteration is Llama 4, which became available in April 2025. This version encompasses a trio of distinct models:

• Scout: Features 17 billion active parameters, a total of 109 billion parameters, and a context window extending to 10 million tokens.

• Maverick: Incorporates 17 billion active parameters, 400 billion total parameters, and a context window of 1 million tokens.

• Behemoth: Currently unreleased, it is projected to possess 288 billion active parameters and 2 trillion total parameters.

(Within the field of data science, tokens are defined as segmented units of raw data, akin to the syllables comprising a word, such as “fan,” “tas,” and “tic” within “fantastic”).

A model’s context window defines the extent of input data – for instance, text – that it processes prior to generating output, such as further text. Extended context capabilities can mitigate the risk of models losing track of recent information and prevent irrelevant extrapolations. However, larger context windows can also potentially diminish adherence to safety protocols.

As a point of comparison, the 10 million token context window offered by Llama 4 Scout is approximately equivalent to the textual content of around 80 typical novels. Llama 4 Maverick’s 1 million token context window corresponds to roughly eight novels.

According to Meta, all Llama 4 models underwent training utilizing substantial volumes of unlabeled text, image, and video data, granting them a comprehensive understanding of visual information. Training also included data from 200 different languages.

Llama 4 Scout and Maverick are Meta’s inaugural open-weight, natively multimodal models. Their architecture leverages a “mixture-of-experts” (MoE) approach, which optimizes computational efficiency during both training and inference. Scout utilizes 16 experts, while Maverick employs 128.

Llama 4 Behemoth also includes 16 experts and is designated by Meta as a guiding model for the smaller versions.

The development of Llama 4 follows the Llama 3 series, which featured the 3.1 and 3.2 models, widely adopted for instruction-tuned applications and cloud-based deployments.

Capabilities of the Llama AI Model

Similar to other generative artificial intelligence systems, Llama is capable of assisting with diverse tasks. These include code generation and the resolution of fundamental mathematical problems.

Furthermore, it can summarize documents in a minimum of 12 different languages: Arabic, English, German, French, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese.

A wide array of text-based operations fall within Llama’s capabilities. This encompasses the analysis of extensive files, such as PDFs and spreadsheets.

All Llama 4 models accept text, image, and video as input.

Llama 4 Model Variations

Llama 4 Scout is specifically engineered for extended processes and the analysis of substantial datasets.

Maverick functions as a versatile model, effectively balancing computational reasoning and response time. It is well-suited for coding applications, chatbot development, and the creation of technical assistants.

Behemoth is optimized for sophisticated research, model refinement, and tasks within the science, technology, engineering, and mathematics (STEM) fields.

Integration with External Tools

Llama models, including Llama 3.1, can be customized to utilize external applications, tools, and APIs for task completion.

The models are trained to employ Brave Search when responding to inquiries about current events.

For mathematical and scientific questions, the Wolfram Alpha API is utilized.

A Python interpreter is incorporated to validate code.

It’s important to note that these tools necessitate appropriate configuration and are not activated by default.

Utilizing Llama: Avenues for Access

For users interested in conversational interaction, Llama serves as the engine behind the Meta AI chatbot, accessible through Facebook Messenger, WhatsApp, Instagram, Oculus, and the Meta.ai platform across 40 nations.

Refined iterations of Llama are currently integrated into Meta AI functionalities spanning over 200 countries and territories.

The Llama 4 models, specifically Scout and Maverick, can be found on Llama.com and through Meta’s collaborative partners, notably the AI development hub Hugging Face. The Behemoth model is still undergoing its training phase.

Availability for Developers

Developers are empowered to download, implement, and customize Llama models for use on prevalent cloud platforms.

Meta reports a network of over 25 partners providing Llama hosting, including prominent companies like Nvidia, Databricks, Groq, Dell, and Snowflake.

While Meta does not directly profit from selling access to its open-source models, the company does generate revenue through collaborative agreements with those hosting the models.

Several partners have developed supplementary tools and services leveraging Llama, including functionalities for accessing proprietary data and achieving reduced latency in model operation.

Licensing Considerations

The Llama license includes stipulations regarding model deployment. Specifically, application developers exceeding 700 million monthly active users are required to obtain a specific license from Meta, granted at the company’s discretion.

In May 2025, Meta initiated a program designed to encourage startup adoption of its Llama models. Llama for Startups provides participating companies with dedicated support from the Meta Llama team and potential access to funding opportunities.

Meta's Toolkit for Llama Models

In addition to the Llama large language model, Meta has released a suite of tools designed to enhance its safe and secure application.

Key Safety and Security Tools

These tools aim to mitigate potential risks associated with LLMs. They include:

• Llama Guard: A framework for content moderation.

• Prompt Guard: A defense mechanism against prompt-injection attacks.

• CyberSecEval: A suite for evaluating cybersecurity risks.

• Llama Firewall: A security guardrail for building secure AI systems.

• Code Shield: A tool for filtering insecure code generated by LLMs.

Llama Guard functions by identifying potentially harmful content, both in inputs provided to and outputs generated by Llama models. This includes content related to illegal activities, exploitation of children, copyright infringement, hate speech, self-harm, and sexual abuse.

However, it's important to note that Llama Guard is not foolproof. Previous guidelines from Meta permitted the chatbot to engage in inappropriate conversations with minors, and reports indicate some escalated to sexually explicit content.

Developers have the ability to customize the categories of content that are blocked and apply these restrictions across all languages supported by Llama.

Prompt Guard, similar to Llama Guard, focuses on blocking text directed at the Llama model. However, its specific purpose is to prevent "attacks" designed to manipulate the model's behavior.

Meta asserts that Prompt Guard effectively defends against both explicitly malicious prompts – those attempting to bypass Llama’s safety filters – and prompts containing “injected inputs.”

The Llama Firewall actively detects and prevents various risks, including prompt injection, insecure code execution, and potentially dangerous interactions with external tools.

Furthermore, Code Shield works to reduce the risk of insecure code suggestions and provides a secure environment for executing commands in seven different programming languages.

CyberSecEval differs from the other tools as it is primarily a collection of benchmarks used to assess model security. It evaluates the potential risks posed by a Llama model, according to Meta’s defined criteria, to both developers and end-users.

These assessments cover areas such as “automated social engineering” and the potential for “scaling offensive cyber operations.”

Limitations of the Llama AI Model

Like all generative AI models, Llama possesses inherent risks and limitations. Currently, its advanced multimodal capabilities are largely restricted to processing the English language.

Copyright and Data Usage Concerns

The training of Llama models involved the utilization of a dataset containing pirated e-books and articles. A recent legal ruling favored Meta in a copyright case initiated by thirteen authors, determining that employing copyrighted material for training purposes constitutes “fair use.”

However, potential copyright infringement issues may arise if Llama reproduces copyrighted content, and this reproduced material is subsequently used within a commercial product.

Data Sourcing from Social Media

Meta’s practice of training its AI using data from Instagram and Facebook – including posts, images, and associated captions – has been met with controversy. Opting out of this data collection process is reportedly difficult for users.

Programming and Code Generation

Caution is advised when leveraging Llama for programming tasks. The model may generate code that contains bugs or security vulnerabilities more frequently than other generative AI alternatives.

Performance on the LiveCodeBench benchmark, which assesses AI models on coding challenges, demonstrates this. Meta’s Llama 4 Maverick achieved a score of 40%.

In comparison, OpenAI’s GPT-5 high scored 85%, and xAI’s Grok 4 Fast reached 83% on the same benchmark.

Therefore, it is crucial to have a qualified human expert thoroughly review any code produced by Llama before its integration into any service or software application.

Potential for Inaccurate Information

Similar to other AI models, Llama can generate information that appears plausible but is, in fact, false or misleading. This applies across various domains, including coding, legal advice, and interactions with AI-powered personas.

This article was first published on September 8, 2024, and receives regular updates to reflect the latest information.