If you’re even remotely following the AI space, you’ve probably heard the buzz this weekend. Meta dropped its new Llama 4 family of models—quietly, on a Saturday—but there’s nothing low-key about what these AI models bring to the table. Llama 4 is Meta’s biggest push yet to compete with the likes of GPT, Gemini, and Claude. Here’s everything you need to know about Meta AI’s Llama 4 AI models.
Meta Released Llama 4
Meta has launched three models under the Llama 4 collection: Scout, Maverick, and Behemoth.
- Scout: Lightweight, 109B parameters model
- Maverick: Middle-tier model, 400B total parameters
- Behemoth: Meta’s largest model, 2 trillion parameters
In simple terms, parameters are like the brain cells of the AI model. The more parameters a model has, the more information it can understand even when trained on the same amount of data.
However, as of now, only Scout and Maverick are available. Behemoth is still in training. The models were trained on massive amounts of unlabeled text, image, and video data to enable native multimodal capabilities—yes, these models understand both text and visuals from the ground up, similar to other models like Gemini 2.0 and ChatGPT 4o.
Scout and Maverick are openly available via Llama.com and platforms like Hugging Face. They also now power Meta AI in WhatsApp, Instagram, Messenger, and the Meta AI web app in 40 countries. However, the multimodal features are limited to English users in the U.S.
What’s New In Llama 4
1. Efficient with the Mixture of Experts (MoE) Architecture
Unlike dense models that use every part of the model for every task, MoE only activates select “experts” depending on the task.
For example, when you ask a math-related question, instead of using the whole model, this architecture activates only the math-related expert in it, keeping the rest of the model idle. So the model becomes very efficient, fast and also can be cost-effective for developers. This was first popularized by DeepSeek models, and now, many companies are using MoE for efficiency.
- Scout has 109B total parameters, 16 experts, but only 17B active at a time.
- Maverick has 400B total parameters, 128 experts, also 17B active at a time.
- Behemoth will have 2 trillion total parameters, 288B active across 16 experts.
2. Huge Memory Upgrade with Huge Context Windows
Scout supports up to 10 million tokens in a single input. Simply put, context window is nothing but memory AI can keep in it’s mind while replying. The more context window an AI has, the more of the past conversations and uploaded files it can remember while answering each question.
Previously, Gemini used to the highest that too with just 1 million tokens. With the 10 times the context window to Gemini, now you can upload entire code bases or even long and multiple documents to Llama’s Scout model.
On the other hand, Maverick only supports 1 million tokens, which is still more than enough for most high-end tasks.
3. Native Multimodal Support
All Llama 4 models can handle text and images together, similar to other models like ChatGPT and Gemini. However, Meta claims their multimodal ability isn’t just added on later—it was part of the model’s core training. That means these models understand and reason over both types of input more naturally.
However, we do not have enough info on how ChatGPT and Gemini have trained their multimodal capabilities and are not sure how well this early fusion system will be helpful in the real world. Nonetheless, the text and image understanding capabilities are going to be much better compared to previous Llama models.
4. Stronger Benchmark Performance
Scout beats Gemma 3, Gemini 2.0 Flash Lite, and Mistral 3.1 on many reported benchmarks while running on a single Nvidia H100 GPU. Maverick scores 1417 on the LMArena ELO leaderboard, outperforming GPT-4o, GPT-4.5, and Claude Sonnet 3.7. It holds second place overall, just below Gemini 2.5 Pro.
Behemoth (still in training) reportedly beats GPT-4.5, Gemini 2.0 Pro, and Claude Sonnet 3.7 in STEM-related tests.
5. Looser Guardrails
Meta says Llama 4 answers more political and social questions than before. The models are tuned to be less dismissive of “contentious” prompts and aim to give factual, balanced responses without outright refusals. After Grok became popular, this became a common move for many AI companies, and I hope this trend continues.
6. Licensing Restrictions
However, it’s not all good. Llama 4 is open-weight, not open-source like before. Companies with more than 700 million MAUs need special permission. And anyone in the EU is barred from using or distributing it under current terms. Anyhow, still, Llama is the only AI from big tech that is open and completely free to use, at least for most people.
Meta Llama 4 AI Model Llama
Llama 4 isn’t just a step up—it’s Meta’ answer to ChatGPT, Grok, and Gemini. With native multimodality, MoE architecture, longer context, and powerful performance with fewer active parameters, Meta is aiming for both scale and efficiency.
And the story’s not done. Behemoth is still coming. More updates are expected at Meta’s LlamaCon event on April 29. If you thought Meta was lagging behind in the AI race, Llama 4 proves they’re not just in it—they’re sprinting.
