Meta Unveils Llama 4 with Long Context Scout and Maverick Models, 2T Parameter Behemoth Coming Soon!

Back in January 2025, the AI world was rocked when a relatively unknown Chinese AI startup, DeepSeek, threw down the gauntlet with their groundbreaking open-source language reasoning model, DeepSeek R1. This model not only outperformed the likes of Meta but did so at a fraction of the cost—rumored to be as little as a few million dollars. That's the kind of budget Meta might spend on just a couple of its AI team leaders! This news sent Meta into a bit of a frenzy, especially since their latest Llama model, version 3.3, released just the month before, was already looking a bit dated.

Fast forward to today, and Meta's founder and CEO, Mark Zuckerberg, has taken to Instagram to announce the launch of the new Llama 4 series. This series includes the 400-billion parameter Llama 4 Maverick and the 109-billion parameter Llama 4 Scout, both available for developers to download and start tinkering with right away on llama.com and Hugging Face. There's also a sneak peek at a colossal 2-trillion parameter model, Llama 4 Behemoth, still in training, with no release date in sight.

Multimodal and Long-Context Capabilities

One of the standout features of these new models is their multimodal nature. They're not just about text; they can handle video and imagery too. And they come with incredibly long context windows—1 million tokens for Maverick and a whopping 10 million for Scout. To put that in perspective, that's like handling up to 1,500 and 15,000 pages of text in one go! Imagine the possibilities for fields like medicine, science, or literature where you need to process and generate vast amounts of information.

Mixture-of-Experts Architecture

All three Llama 4 models employ the "mixture-of-experts (MoE)" architecture, a technique that's been making waves, popularized by companies like OpenAI and Mistral. This approach combines multiple smaller, specialized models into one larger, more efficient model. Each Llama 4 model is a mix of 128 different experts, which means only the necessary expert and a shared one handle each token, making the models more cost-effective and faster to run. Meta boasts that Llama 4 Maverick can be run on a single Nvidia H100 DGX host, making deployment a breeze.

Cost-Effective and Accessible

Meta is all about making these models accessible. Both Scout and Maverick are available for self-hosting, and they've even shared some enticing cost estimates. For instance, the inference cost for Llama 4 Maverick is between $0.19 and $0.49 per million tokens, which is a steal compared to other proprietary models like GPT-4o. And if you're interested in using these models via a cloud provider, Groq has already stepped up with competitive pricing.

Enhanced Reasoning and MetaP

These models are built with reasoning, coding, and problem-solving in mind. Meta's used some clever techniques during training to boost these capabilities, like removing easy prompts and using continuous reinforcement learning with increasingly difficult prompts. They've also introduced MetaP, a new technique that allows for setting hyperparameters on one model and applying them to others, saving time and money. It's a game-changer, especially for training monsters like Behemoth, which uses 32K GPUs and processes over 30 trillion tokens.

Performance and Comparisons

So, how do these models stack up? Zuckerberg's been clear about his vision for open-source AI leading the charge, and Llama 4 is a big step in that direction. While they might not set new performance records across the board, they're certainly near the top of their class. For instance, Llama 4 Behemoth outperforms some heavy hitters on certain benchmarks, though it's still playing catch-up with DeepSeek R1 and OpenAI's o1 series in others.

Llama 4 Behemoth

• Outperforms GPT-4.5, Gemini 2.0 Pro, and Claude Sonnet 3.7 on MATH-500 (95.0), GPQA Diamond (73.7), and MMLU Pro (82.2)

Llama 4 Maverick

• Beats GPT-4o and Gemini 2.0 Flash on most multimodal reasoning benchmarks like ChartQA, DocVQA, MathVista, and MMMU
• Competitive with DeepSeek v3.1 while using less than half the active parameters
• Benchmark scores: ChartQA (90.0), DocVQA (94.4), MMLU Pro (80.5)

Llama 4 Scout

• Matches or outperforms models like Mistral 3.1, Gemini 2.0 Flash-Lite, and Gemma 3 on DocVQA (94.4), MMLU Pro (74.3), and MathVista (70.7)
• Unmatched 10M token context length—ideal for long documents and codebases

Comparing with DeepSeek R1

When it comes to the big leagues, Llama 4 Behemoth holds its own but doesn't quite dethrone DeepSeek R1 or OpenAI's o1 series. It's slightly behind on MATH-500 and MMLU but ahead on GPQA Diamond. Still, it's clear that Llama 4 is a strong contender in the reasoning space.

Safety and Political Neutrality

Meta hasn't forgotten about safety either. They've introduced tools like Llama Guard, Prompt Guard, and CyberSecEval to keep things on the up-and-up. And they're making a point about reducing political bias, aiming for a more balanced approach, especially after Zuckerberg's noted support for Republican politics post-2024 election.

The Future with Llama 4

With Llama 4, Meta is pushing the boundaries of efficiency, openness, and performance in AI. Whether you're looking to build enterprise-level AI assistants or dive deep into AI research, Llama 4 offers powerful, flexible options that prioritize reasoning. It's clear that Meta is committed to making AI more accessible and impactful for everyone.