Just as every major tech company now has its own generative AI model, Meta also has their flagship model, Llama. Unlike other major models, Llama is somewhat different, because it’s “open”—meaning developers can download and use it on their own terms (with certain restrictions). This is the opposite of models like Anthropic’s Claude, Google’s Gemini, xAI’s Grok, and most of OpenAI’s ChatGPT models, which are accessible only via API.
To give developers more options, Meta has again partnered with vendors such as AWS, Google Cloud, and Microsoft Azure, so Llama is available as a cloud-hosted version as well. The company also releases tools, libraries, and recipes in the Llama cookbook, which help developers fine-tune the model, evaluate it, and adapt it to their own domains. In the newest generations, Llama 3 and Llama 4, these capabilities have been further expanded—now featuring native multimodal support and even more extensive cloud rollout.
What is Llama?
Llama is actually a family name—not just a single model. The latest version is Llama 4, released in April 2025, which includes three models:
- Scout: 17 billion active parameters, a total of 109 billion parameters, with a context window of 10 million tokens.
- Maverick: 17 billion active parameters, a total of 400 billion parameters, with a context window of 1 million tokens.
- Behemoth: Not yet released; will feature 288 billion active parameters and a total of 2 trillion parameters.
(In data science, tokens mean small chunks of raw data, such as “fan,” “tas,” and “tic” in the word “fantastic.”)
The context window of a model refers to how much input data the model can consider before generating output. The larger the context, the less likely the model is to forget recent information; however, sometimes this can allow it to bypass safety rules and produce misleading outputs. For example, Scout’s 10 million token context is roughly equal to 80 average-sized novels, while Maverick’s 1 million tokens is about 8 novels.
What can Llama do?
Like other generative AI models, Llama can write code, solve basic math problems, summarize documents (in at least 12 languages), and handle text, image, and video inputs.
- Scout: Suitable for long and complex workflows and analyzing large amounts of data.
- Maverick: Generally versatile; effective for coding, chatbots, and technical assistants.
- Behemoth: Designed for research, model distillation, and advanced STEM tasks.
Additionally, they can be configured to use third-party tools such as Brave Search, Wolfram Alpha API, and the Python interpreter.
Where can it be used?
If you simply want to chat, Llama-powered chatbot experiences are available on Messenger, WhatsApp, Instagram, Oculus, and Meta.ai in 40 countries.
For developers, Scout and Maverick are available on Llama.com and platforms like Hugging Face, while Behemoth is still in training.
Security Tools
Meta also provides some security tools with Llama:
- Llama Guard (content moderation)
- Prompt Guard (prompt injection prevention)
- CyberSecEval (cybersecurity evaluation)
- Llama Firewall (security guardrails)
- Code Shield (unsafe code filtering)
All these work together to reduce model risk and ensure safer usage.
Limitations
Like all generative AI, Llama also has its limits. Its multimodal features are mainly restricted to English. There have also been allegations that pirated ebooks and articles were used in training the model, which courts have considered “fair use.” However, this can still pose copyright risks.
Additionally, code generation often produces bugs or unsafe code, where OpenAI’s GPT-5 or xAI’s Grok are much more advanced.
Finally, like other AIs, Llama sometimes generates incorrect or misleading information that sounds convincing, especially in the areas of law, coding, or emotionally charged conversations.
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