Like every Big Tech company these days, Meta has its own flagship generative AI model called Llama. Llama is somewhat unique among major models because it is open, meaning developers can download and use it however they please, with certain limitations. That contrasts with models like Anthropic’s Claude, Google’s Gemini, xAI’s Grok, and most of OpenAI’s ChatGPT models, which can only be accessed via APIs.
To give developers more choice, Meta has partnered with vendors, including AWS, Google Cloud, and Microsoft Azure, to make cloud-hosted versions of Llama available. The company also publishes tools, libraries, and recipes in its Llama cookbook to help developers fine-tune, evaluate, and adapt the models to their domain. With newer generations like Llama 3 and Llama 4, these capabilities have expanded to include native multimodal support and broader cloud rollouts.
Here is everything you need to know about Meta’s Llama, from its capabilities and editions to where you can use it. We will keep this post updated as Meta releases upgrades and introduces new developer tools to support the model’s use.
Llama is a family of models, not just one. The latest version is Llama 4, released in April 2025. It includes three models. Scout has 17 billion active parameters, 109 billion total parameters, and a context window of 10 million tokens. Maverick has 17 billion active parameters, 400 billion total parameters, and a context window of 1 million tokens. Behemoth is not yet released but will have 288 billion active parameters and 2 trillion total parameters.
In data science, tokens are subdivided bits of raw data, like the syllables fan, tas, and tic in the word fantastic. A model’s context, or context window, refers to input data that the model considers before generating output. Long context can prevent models from forgetting the content of recent documents and data and from veering off topic and extrapolating wrongly. However, longer context windows can also result in the model forgetting certain safety guardrails and being more prone to produce content that is in line with the conversation, which has led some users toward delusional thinking.
For reference, the 10 million context window that Llama 4 Scout promises roughly equals the text of about 80 average novels. Llama 4 Maverick’s 1 million context window equals about eight novels.
All of the Llama 4 models were trained on large amounts of unlabeled text, image, and video data to give them broad visual understanding, as well as on 200 languages, according to Meta. Llama 4 Scout and Maverick are Meta’s first open-weight natively multimodal models. They are built using a mixture-of-experts architecture, which reduces computational load and improves efficiency in training and inference. Scout, for example, has 16 experts, and Maverick has 128 experts.
Llama 4 Behemoth includes 16 experts, and Meta is referring to it as a teacher for the smaller models. Llama 4 builds on the Llama 3 series, which included 3.1 and 3.2 models widely used for instruction-tuned applications and cloud deployment.
Like other generative AI models, Llama can perform a range of different assistive tasks, like coding and answering basic math questions, as well as summarizing documents in at least 12 languages, including Arabic, English, German, French, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese. Most text-based workloads, such as analyzing large files like PDFs and spreadsheets, are within its purview, and all Llama 4 models support text, image, and video input.
Llama 4 Scout is designed for longer workflows and massive data analysis. Maverick is a generalist model that is better at balancing reasoning power and response speed, and is suitable for coding, chatbots, and technical assistants. Behemoth is designed for advanced research, model distillation, and STEM tasks.
Llama models, including Llama 3.1, can be configured to leverage third-party applications, tools, and APIs to perform tasks. They are trained to use Brave Search for answering questions about recent events, the Wolfram Alpha API for math- and science-related queries, and a Python interpreter for validating code. However, these tools require proper configuration and are not automatically enabled out of the box.
If you are looking to simply chat with Llama, it is powering the Meta AI chatbot experience on Facebook Messenger, WhatsApp, Instagram, Oculus, and Meta.ai in 40 countries. Fine-tuned versions of Llama are used in Meta AI experiences in over 200 countries and territories.
Llama 4 models Scout and Maverick are available on Llama.com and Meta’s partners, including the AI developer platform Hugging Face. Behemoth is still in training. Developers building with Llama can download, use, or fine-tune the model across most of the popular cloud platforms. Meta claims it has more than 25 partners hosting Llama, including Nvidia, Databricks, Groq, Dell, and Snowflake. And while selling access to Meta’s openly available models is not Meta’s business model, the company makes some money through revenue-sharing agreements with model hosts.
Some of these partners have built additional tools and services on top of Llama, including tools that let the models reference proprietary data and enable them to run at lower latencies. Importantly, the Llama license constrains how developers can deploy the model. App developers with more than 700 million monthly users must request a special license from Meta that the company will grant at its discretion.
In May 2025, Meta launched a new program to incentivize startups to adopt its Llama models. Llama for Startups gives companies support from Meta’s Llama team and access to potential funding.
Alongside Llama, Meta provides tools intended to make the model safer to use. Llama Guard is a moderation framework. Prompt Guard is a tool to protect against prompt injection attacks. CyberSecEval is a cybersecurity risk assessment suite. Llama Firewall is a security guardrail designed to enable building secure AI systems. Code Shield provides support for inference-time filtering of insecure code produced by large language models.
Llama Guard tries to detect potentially problematic content either fed into or generated by a Llama model, including content relating to criminal activity, child exploitation, copyright violations, hate, self-harm and sexual abuse. That said, it is clearly not a silver bullet since Meta’s own previous guidelines allowed the chatbot to engage in sensual and romantic chats with minors, and some reports show those turned into sexual conversations. Developers can customize the categories of blocked content and apply the blocks to all the languages Llama supports.
Like Llama Guard, Prompt Guard can block text intended for Llama, but only text meant to attack the model and get it to behave in undesirable ways. Meta claims that Llama Guard can defend against explicitly malicious prompts in addition to prompts that contain injected inputs. The Llama Firewall works to detect and prevent risks like prompt injection, insecure code, and risky tool interactions. And Code Shield helps mitigate insecure code suggestions and offers secure command execution for seven programming languages.
As for CyberSecEval, it is less a tool than a collection of benchmarks to measure model security. CyberSecEval can assess the risk a Llama model poses to app developers and end users in areas like automated social engineering and scaling offensive cyber operations.
Llama comes with certain risks and limitations, like all generative AI models. For example, while its most recent model has multimodal features, those are mainly limited to the English language for now.
Zooming out, Meta used a dataset of pirated e-books and articles to train its Llama models. A federal judge recently sided with Meta in a copyright lawsuit brought against the company by 13 book authors, ruling that the use of copyrighted works for training fell under fair use. However, if Llama regurgitates a copyrighted snippet and someone uses it in a product, they could potentially be infringing on copyright and be liable.
Meta also controversially trains its AI on Instagram and Facebook posts, photos and captions, and makes it difficult for users to opt out. Programming is another area where it is wise to tread lightly when using Llama. That is because Llama might, perhaps more so than its generative AI counterparts, produce buggy or insecure code. On LiveCodeBench, a benchmark that tests AI models on competitive coding problems, Meta’s Llama 4 Maverick model achieved a score of 40 percent. That is compared to 85 percent for OpenAI’s GPT-5 high and 83 percent for xAI’s Grok 4 Fast. As always, it is best to have a human expert review any AI-generated code before incorporating it into a service or software.
Finally, as with other AI models, Llama models are still guilty of generating plausible-sounding but false or misleading information, whether that is in coding, legal guidance, or emotional conversations with AI personas.
This was originally published on September 8, 2024 and is updated regularly with new information.