On April 5, 2025, Meta AI made big news in the AI world with the release of Llama 4—a new set of large language models (LLMs) that’s getting a lot of attention and starting plenty of conversations. With models like Llama 4 Scout and Llama 4 Maverick already out (and the huge Llama 4 Behemoth still being trained), Meta isn’t just trying to keep up—they’re looking to change the game.
Thanks to its ability to handle both text and images, and a huge 10-million-token memory, Llama 4 is making news for all the right reasons. But why should developers, businesses, or even everyday users care? Here’s a simple breakdown of what Llama 4 is and why it’s making waves in the AI world.
Llama 4 is the newest version of Meta’s open AI models, built to compete with big names like OpenAI’s GPT-4o and Google’s Gemini 2.0. It was launched on an unexpected Saturday and includes two ready-to-use models—Scout and Maverick—while giving a sneak peek at Behemoth, a massive model still being trained.
Unlike earlier versions, Llama 4 isn’t just about text. It’s naturally multimodal, meaning it can understand both text and images at the same time. Support for audio and more is planned in the future.
Llama 4 Scout:
Has 17 billion active settings and 109 billion total, with a huge 10-million-token memory. It’s fast, light, and can run on a single GPU—great for developers with limited resources.
Llama 4 Maverick:
Also has 17 billion active settings but with 400 billion total spread across 128 experts. It’s built for chatting and is said to perform better than GPT-4o in key tests.
Llama 4 Behemoth:
Still being trained, this giant model has 288 billion active settings and 2 trillion in total. It’s meant to teach and improve other models once released.
What makes Llama 4 different?
It uses a smart design called Mixture of Experts (MoE), which only turns on the parts of the model needed for each task. This makes it much faster and more efficient than older models. And because it’s open for anyone to use, it could really shake up the AI space.
MetaLlama 4’s ability to handle text and images natively is a standout. Imagine uploading a photo and asking, “What’s this about?” Llama 4 can analyze both the image and your question to respond intelligently. This opens doors for instant captions, visual Q&A, or even meme summaries. For developers, it’s a leap toward building richer, more interactive AI tools, rivaling closed systems like GPT-4.
Scout boasts a 10-million-token context window, think entire books or months of chat history processed in one go, while Maverick’s 1-million-token context still impresses. The MoE design means only a fraction of the model activates per task, slashing compute costs. Scout runs on a single GPU, making it accessible to hobbyists and startups, not just tech giants. Meta claims Maverick outpaces GPT-4o and Gemini 2.0 in coding, reasoning, and multilingual tasks, backed by benchmarks, though some controversy lingers over their transparency.
By releasing Llama 4 freely, Meta is doubling down on its mission to democratize AI. Mark Zuckerberg, in an Instagram video, said, “Our goal is to build the world’s leading AI, open-source it, and make it universally accessible.” With downloads hitting over a billion pre-launch, Llama 4 empowers developers to customize it without paywalls, challenging proprietary models’ dominance. Critics note the “open-weight” label dodges full transparency (training data isn’t disclosed), but the impact is undeniable
– CEO Mark Zuckerberg said, “The highest performing base model in the world” is yet to come.
Let’s break down why Llama 4 is stealing the spotlight:
Imagine uploading a photo of a recipe and asking, “How do I make this?”—and getting a step-by-step guide. Or tossing in a meme and having the AI explain the joke. Llama 4’s “early fusion” approach means it’s trained on text, images, and even video frames from the get-go, not tacked on later like some competitors.
This native multimodality unlocks real-world uses: instant captions, visual Q&A, or summarizing complex visuals with text context. For businesses, this could mean AI that understands product images and customer queries simultaneously—game-changing for e-commerce or support.
Scout’s 10-million-token context window (and Maverick’s still-impressive 1 million) is a monster leap from Llama 3’s 128,000. In human terms, that’s like going from remembering a short story to recalling an entire encyclopedia. It means Llama 4 can analyze massive documents, track long conversations, or reason over sprawling codebases without losing the plot. Developers are already dreaming up use cases: multi-document summarization, personalized assistants with deep memory, or debugging entire software projects in one go.
The MoE architecture is the secret sauce. Instead of firing up all 400 billion parameters for every query (like a dense model would), Maverick picks just 17 billion “experts” tailored to the task. It’s like having a team of specialists instead of a jack-of-all-trades—faster, cheaper, and still brilliant. Scout takes it further, squeezing top-tier performance into a single Nvidia H100 GPU. For the open-source community, this accessibility is a holy grail—high-end AI without breaking the bank.
Meta’s doubling down on its open-source commitment. Scout and Maverick are available for download on Llama.com and Hugging Face, free for developers to tweak and deploy (with some license quirks—more on that later). This isn’t just charity; it’s a strategic flex. By empowering the community, Meta’s betting Llama 4 will spawn an ecosystem of innovations, driving adoption and keeping it competitive with closed-source titans.
Meta says Maverick beats GPT-4o and Gemini 2.0 in coding, reasoning, languages, and image tasks. Scout, the smaller one, is said to outperform other lightweight models like Mistral 3.1. But there’s a twist: the much-talked-about 1417 ELO score on LMArena came from a test version of Maverick, not the public one. Some people raised questions—did Meta boost the numbers with a custom model? Meta says the score is real, but it’s a reminder not to trust all benchmarks until more people test them.
Early users say Maverick feels more natural and less technical in conversations, which is great since Meta plans to use it in apps like WhatsApp, Instagram, and Messenger. Scout is winning fans for its speed and ability to handle long inputs. But using its full 10-million-token memory needs a lot of computing power, like eight H100 GPU,s just to hit 1.4 million tokens. Behemoth, still in training, is expected to beat top models like GPT-4.5 and Claude 3.7 in science and math tasks. Stay tuned.
Meta dropped Llama 4 on a Saturday, and people are curious why. Some say Meta wanted to beat other companies like Alibaba’s Qwen or DeepSeek, which were getting attention with their cheap and powerful models. Others think Meta was under pressure—Llama 4 had delays because of trouble with math and logic tasks, and it was behind in voice features too.
The weekend launch might’ve been a way to get ahead of the news before LlamaCon on April 29, where Meta might show more features (like a reasoning model?). Either way, it shows Meta isn’t waiting around.
Since Scout runs on just one GPU and both models are open to the public, Llama 4 makes powerful AI available to more people like students, small businesses, and solo developers. It could lead to a new wave of cool, homegrown AI projects.
Llama 4 is already part of Meta AI, built into WhatsApp, Instagram, and Messenger in 40 countries. That means 600 million people are already using it. Meta’s not just building a smart model it’s building a whole AI-powered system. Think smarter ads, better customer support, or AR glasses that understand what you see—all powered by Llama 4.
Llama 4 brings back the debate: should AI be open to all or kept behind closed doors? Mark Zuckerberg says open models will win, and Llama 4 is his proof. But the license isn’t fully open—companies in the EU can’t use it, and big firms (over 700 million users) need Meta’s okay. Some call this “open-washing.” Others say it’s just smart policy. Either way, it’s making other companies rethink their plans.
If Maverick can beat GPT-4o using fewer resources, and Scout can outdo Gemini Flash on one GPU, then OpenAI and Google might need to catch up. Llama 4 proves that powerful AI doesn’t have to be expensive.
There are some concerns. The benchmark drama made people question Meta’s openness. And while the massive memory sounds great, not many people can fully use it without expensive gear. Also, even though it supports images, Llama 4 can’t yet do deep reasoning like OpenAI’s newest model. And the surprise weekend release, along with mixed early reviews (some call it “just okay” or too chatty), shows it’s still a work in progress. This is just the start—not the final version.
Meta isn’t stopping here. The Behemoth model could make Llama 4 the new leader in generative AI. Rumors say Meta might launch a version that’s even better at reasoning. At LlamaCon, we might see exciting new features like voice support or agent-style AI services that can act on their own. For now, developers can start using Scout and Maverick, and anyone can chat with Meta AI on WhatsApp. This is only the beginning.
Llama 4 isn’t just a model, it’s a message. Meta is going big on open access, smarter designs, and real-world use. It’s not perfect, but it’s a bold move: showing what the future of AI services might look like without huge costs or limits. Whether it replaces other models like Grok or becomes the base for tomorrow’s smart tools, one thing’s clear: Llama 4 has everyone talking.
So, what do you think—just hype, or history in the making?
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Pranjal Mehta is the Managing Director of Zealous System, a leading software solutions provider. Having 10+ years of experience and clientele across the globe, he is always curious to stay ahead in the market by inculcating latest technologies and trends in Zealous.
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