Categories: GADGET

Meta introduces Llama 4 with two new AI models available now, and two more on the way


Meta has released the first two models from its multimodal Llama 4 suite: LLama 4 Scout and Llama 4 Maverick. Maverick is “the workhorse” of the two and excels at image and text understanding for “general assistant and chat use cases,” the company said in a blog post, while the smaller model Scout could tackle things like “multi-document summarization, parsing extensive user activity for personalized tasks, and reasoning over vast codebases.” The company also introduced Llama 4 Behemoth, an upcoming model it says is “among the world’s smartest LLMs” — and CEO Mark Zuckerberg said we’ll be hearing about a fourth model, LLama 4 Reasoning, “in the next month.” 

Both Maverick and Scout are available to download now from the LLama website and Hugging Face, and they’ve been added to Meta AI, including for WhatsApp, Messenger and Instagram DMs. 

Meta

Scout has 17 billion active parameters with 16 experts, Meta says. According to Zuckerberg, “It’s extremely fast, natively multimodal, and has an industry leading, nearly infinite 10 million token context length, and it is designed to run on a single GPU.” Maverick on the other hand has 17 billion active parameters with 128 experts. The company says it beats competitors like GPT-4o and Gemini 2.0 on coding, reasoning, multilingual, long-context and image benchmarks, and stacks up against DeepSeek v3.1 on reasoning and coding.

Zuckerberg is already calling the upcoming Behemoth model, which is still training, “the highest performing base model in the world,” with 288 billion active parameters, according to the company. It may not be here yet, but it’s likely we’ll be hearing a lot more about that and the Reasoning model soon; Meta’s big AI developer conference, LlamaCon, is just a few weeks away.

This article originally appeared on Engadget at https://www.engadget.com/ai/meta-introduces-llama-4-with-two-new-models-available-now-and-two-more-on-the-way-214524295.html?src=rss



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