Meta’s latest creation, the Llama 4 series AI models, are now officially open source! Featuring native multimodality, ultra-long context, and a mixture-of-experts (MoE) architecture, Llama 4 Scout and Maverick deliver powerful performance—with the behemoth-level Llama 4 Behemoth still to come. Dive in and discover this new AI revolution!
Hey AI enthusiasts and tech fans—listen up! Meta just dropped a major bombshell, officially launching their most powerful open-source AI models yet: Llama 4! And they’re not pulling any punches—the launch includes two powerhouse models right out of the gate: Llama 4 Scout and Llama 4 Maverick. This isn’t just another small upgrade—it feels like a whole new wave is about to hit the AI world.
You might be thinking, “Another AI model? What’s the big deal?” Well, Llama 4 is not just any model. Meta has leveled up the intelligence of its models, introduced the mixture-of-experts (MoE) architecture for the first time, and made them natively multimodal—meaning they don’t just understand text, but can also comprehend images and even video! This opens a whole new frontier for human-AI interaction.
Meta has launched two different models to meet diverse needs.
First up is the “scout” of the family—Llama 4 Scout. It has 109 billion total parameters, but only 17 billion active parameters, with 16 experts working together. Sounds modest? The kicker is…
It supports a 10 million token context length (yes, that’s 10M!). What does that mean? Imagine being able to process over 20 hours of video content in one go—or analyze entire massive reports or codebases all at once. And thanks to Int4 quantization, it can run on just a single NVIDIA H100 GPU! That’s a dream come true for developers.
In benchmarks, Scout leaves competitors like Google’s Gemma 3, Gemini 2.0 Flash-Lite, and Mistral 3.1 in the dust. For applications needing to handle lots of data with a compact model, Scout is a top choice.
Next is the “lone wolf” Llama 4 Maverick. It boasts 400 billion total parameters with the same 17 billion active ones, but with a massive 128 experts! It also supports a 1 million token context.
Maverick clearly aims for top-tier performance. In the popular LMSYS leaderboard, an experimental chat version scored second (ELO 1417), just behind the closed-source Gemini 2.5 Pro! Even more impressively, it matched the reasoning and coding abilities of DeepSeek-v3 using half the number of active parameters. That means incredible efficiency and value.
Meta describes Maverick as their flagship model—great for general-purpose assistants, precise image understanding, and creative writing. If you’re building complex AI applications, Maverick is one to watch.
Think that’s it? Nope! Meta also teased the upcoming Llama 4 Behemoth—a true giant of a model. It’s expected to launch in the coming months, with nearly 2 trillion parameters (2T) and 288 billion active ones, supported by 16 experts.
Though still in training, Behemoth is already beating GPT-4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro in STEM-related benchmarks. Meta says they used Behemoth as a “teacher” to train Maverick using a technique called codistillation, dramatically boosting the performance of smaller models. We’ll just have to wait for its official debut!
The power of the Llama 4 series comes from multiple technological innovations.
Llama 4 is the first in the family to adopt the MoE architecture. Think of it as having a team of specialists. When handling a task (one token), the model only activates the relevant experts—not all of them.
What’s the benefit? Efficiency. Whether in training or inference, it requires fewer resources. MoE models often outperform dense models at the same compute cost. Maverick has 400B total parameters but only uses 17B actively—meaning faster speed and lower cost.
Llama 4 was built from the ground up to be natively multimodal. Meta used a technique called early fusion, which lets the model seamlessly integrate and process text, image, and video tokens together.
Unlike older methods that pre-convert images into text, early fusion allows the model to truly understand the relationship between visuals and language. Meta also upgraded its visual encoder (based on MetaCLIP, but improved) and introduced a new training method called MetaP for hyperparameter optimization.
To create a top-tier model like Llama 4, Meta employed clever training techniques:
Pretraining is only the beginning. Meta fine-tuned the models to make them smarter and more aligned with human expectations.
This time, Meta used a lightweight SFT (supervised fine-tuning) -> online RL (reinforcement learning) -> lightweight DPO (direct preference optimization) process. They found that heavy SFT or DPO can limit RL exploration and hurt reasoning and code capabilities.
So, they filtered out over 50% of “easy” training data and only applied SFT on more difficult examples. Then, during online multimodal RL, they trained the model on carefully chosen hard prompts to boost performance. Their continuous online RL strategy helps strike a balance between accuracy and efficiency. Finally, DPO was applied to fine-tune tricky edge cases.
This full pipeline—plus codistillation from Behemoth—is why Maverick combines intelligence, dialogue, and vision understanding so well.
For Scout’s ultra-long context, Meta pre- and post-trained it on 256K tokens. The key innovation was using interleaved attention layers without positional embeddings and temperature-scaled attention during inference, forming the iRoPE architecture (i = interleaved, RoPE = rotary positional embedding). It sounds nerdy, but the result? Scout handles an insane 10 million tokens!
Both models were also trained with tons of images and video frames. They can understand sequences of images and their relationships. During pretraining, they processed up to 48 images at once; in later tests, 8-image performance remained strong. Scout also excels in image grounding, aligning prompts with relevant image regions.
AI safety and responsibility are top priorities. Meta embedded protections at every stage—from data filtering in pretraining to safe datasets in post-training, plus customizable system-level safeguards for developers.
They’re also releasing open-source tools to help:
They also conduct automated and manual red teaming to stress-test models, and developed GOAT (Generative Offensive Agent Testing)—a new method that simulates mid-level attackers engaging in multi-turn interactions to uncover potential vulnerabilities faster.
Let’s be real—every leading AI model faces bias issues, especially on controversial topics. Meta aims to eliminate bias and ensure Llama can fairly represent both sides of any debate.
They’ve made big progress with Llama 4:
Still, Meta acknowledges that more work remains and is committed to continued improvements.
Excited? Want to try Llama 4 yourself?
Meta has released Llama 4 Scout and Llama 4 Maverick for download. Visit llama.com or Hugging Face to get started. Cloud and edge partners will support them soon as well.
You can also try Meta AI powered by Llama 4 through WhatsApp, Messenger, Instagram Direct, or the Meta.AI website.
Llama 4 isn’t just another model drop—it’s a huge contribution to the open AI ecosystem. With ultra-long context, native multimodality, and efficient MoE architecture, Llama 4 gives developers and researchers powerful new tools. Now it’s up to the community to unleash its full potential!
Oh—and don’t forget, Meta will share more about their vision at LlamaCon on April 29. Stay tuned!
Disclaimer: This article is based on publicly available information. Model performance and results may vary depending on usage and context.
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