Remember when “open-source AI” meant settling for second-best? Yeah, that just stopped being true.
On April 5, 2025, Meta dropped Llama 4—and honestly? The timing felt weird. A Saturday release for one of the year’s biggest AI launches. But here’s what matters: Meta introduced three models with the Llama 4 release, driving major advancements in areas like speech and reasoning, and two of them are available right now. As someone who’s watched AI models evolve from expensive curiosities to everyday tools, I can tell you—this release feels different.
The Llama 4 family includes Scout, Maverick, and the still-training Behemoth. Llama 4 Scout has 17 billion active parameters with 16 experts and 109 billion total parameters, while Maverick has 17 billion active parameters with 128 experts and 400 billion total parameters. And these aren’t just bigger numbers on a spec sheet. They’re fundamentally different architectures designed for different needs.
What Actually Is Llama 4? (Without the Jargon)
Llama 4 is Meta’s fourth-generation large language model—basically, sophisticated AI software that understands and generates human-like text and images. Meta Llama 4 is a multimodal large language model that analyzes and understands text, images, and video data, with support for multiple languages from all parts of the globe. Unlike OpenAI’s GPT models or Anthropic’s Claude, which you can only access through paid APIs, Llama 4 is open-weight. That means you can download it, modify it, run it on your own servers—do whatever you need.
Think of it this way: closed models are like renting a car with strict rules about where you can drive. Open models? You own the vehicle outright.
Here’s where it gets interesting. Llama 4 Scout supports an industry-leading context window of 10 million tokens and delivers better results than Gemma 3, Gemini 2.0 Flash-Lite, and Mistral 3.1 across a broad range of widely reported benchmarks. Ten million tokens. That’s enough to process entire books, analyze months of chat history, or review massive codebases in a single pass.
But wait—you might be wondering why Meta keeps releasing these models for free. Simple: they’re playing the long game. By making Llama open, they’re building an ecosystem where thousands of developers improve, adapt, and extend the technology. It’s the Linux strategy applied to AI.
The Three Horsemen: Scout, Maverick, and Behemoth
Each Llama 4 model targets specific use cases, and understanding which one fits your needs matters more than you’d think.
Scout is your efficiency champion. It fits on a single NVIDIA H100 GPU (with quantization), making it deployable in environments where you don’t have data center budgets. Llama 4 Scout was trained on approximately 40 trillion tokens and offers a 10 million-token context window, which means it’s perfect for analyzing lengthy research papers, processing entire codebases, or maintaining deep conversation history. Universities, research labs, and startups are already using it for document analysis that would’ve been impossible six months ago.
Maverick is the workhorse. Llama 4 Maverick beats GPT-4o and Gemini 2.0 Flash across a broad range of widely reported benchmarks, while achieving comparable results to DeepSeek v3 on reasoning and coding at less than half the active parameters. On benchmarks like MMMU (image reasoning), Maverick scores 73.4, outperforming both Gemini 2.0 Flash (71.7) and GPT-4o (69.1). For coding tasks on LiveCodeBench, it hits 43.4—crushing GPT-4o’s 32.3.
I’ve tested Maverick on multimodal tasks—uploading product images with complex questions—and it consistently nails the context that text-only models miss. For e-commerce platforms or customer support systems handling image uploads, this capability alone justifies the integration effort.
Behemoth remains in training, but the preview numbers are wild. Llama 4 Behemoth has 288 billion active parameters with 16 experts and nearly 2 trillion total parameters, outperforming GPT-4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro on STEM-focused benchmarks such as MATH-500 and GPQA Diamond. On MATH-500, Behemoth scores 95.0 compared to Gemini 2.0 Pro’s 91.8 and Claude Sonnet 3.7’s 82.2. Meta’s using Behemoth as a “teacher model” to distill knowledge into Scout and Maverick through a novel training approach.
How Llama 4 Actually Works (The Interesting Parts)
Traditional AI models activate all their parameters for every query. It’s like turning on every light in a mansion to find your keys.
Llama 4 uses Mixture-of-Experts (MoE) architecture instead. The Llama 4 models are the first large language models in the Llama family to employ a mixture-of-experts architecture, where only a subset of the total parameters activate for an input token, targeting a balance of power with efficiency. When you ask Maverick a question, it routes your query to 17 billion specialized “experts” out of its 400 billion total parameters. The model picks the right combination for each task—coding experts for programming questions, vision experts for image analysis, language experts for translation.
This isn’t just clever engineering. It’s a fundamental shift that makes powerful AI affordable to deploy.
During pre-training of Llama 4 Behemoth using FP8 precision and 32,000 GPUs, Meta achieved 390 TFLOPs per GPU—that’s exceptional computational efficiency that translates directly into lower costs for anyone running these models. And Meta trained the models on more than 30 trillion tokens, double what Llama 3 used, including diverse text, image, and video datasets.
The multimodal training deserves attention too. Unlike bolting vision capabilities onto a text model (the old approach), Llama 4 uses “early fusion,” integrating text and vision from the ground up. This means it doesn’t just see images—it understands them contextually alongside text in ways that feel surprisingly human.
Real-World Applications (Where Llama 4 Actually Shines)
Theory’s great. But where does this actually matter?
Healthcare documentation: Sofya, a medical application leveraging Llama, is substantially reducing doctor time and effort, alleviating burdens on healthcare professionals. Physicians are using Llama-powered systems to generate clinical notes, summarize patient histories, and extract information from imaging reports—all while keeping sensitive data on-premise thanks to the open-weight licensing.
Customer support at scale: AT&T is using Llama 4 for support call analysis, and astronauts are using it for mission planning. When your customer uploads a blurry photo of a broken product and types “it doesn’t work,” Maverick can analyze both inputs simultaneously and route to the right solution. That’s not theoretical—companies are deploying this today.
Financial services: Risk assessment tools, fraud detection systems, and compliance monitoring all benefit from Llama 4’s ability to process long documents while maintaining context. A hedge fund can analyze SEC filings, earnings transcripts, and analyst reports together—connecting insights that would take humans days to piece together.
Education and tutoring: Intelligent tutoring systems are using Scout’s long context window to maintain detailed student interaction histories, adapting explanations based on what worked in past sessions. The 10-million-token window means the system remembers an entire semester of work.
Can it handle creative work? Absolutely. But here’s the thing nobody tells you: Llama 4 isn’t magic. It won’t replace thoughtful prompting, domain expertise, or good old-fashioned human judgment. What it will do is amplify what you already know how to do—faster and at a fraction of the cost.
The Licensing Reality (Read This Before You Deploy)
Let’s talk about the elephant in the room. “Open-source” is technically wrong here—The Meta Llama license provides free usage and modification of the Llama 4 models with certain limits, requiring a commercial license after 700 million monthly users. For 99.9% of developers, that limit won’t matter. But if you’re Facebook or TikTok? You’ll need special permission from Meta.
More concerning: Users and companies domiciled or with a principal place of business in the European Union are prohibited from using or distributing the models, likely due to regulatory complexity around the EU’s AI Act and GDPR requirements. If you’re building in Europe, this is a dealbreaker.
The Open Source Initiative disputed Meta’s “open-source” labeling because the Acceptable Use Policy restricts certain applications and Meta doesn’t disclose full training data details. The Free Software Foundation classified the license as nonfree in January 2025. Is this “open enough” for your needs? That depends on what you’re building.
Llama 4 vs. The Competition (An Honest Comparison)
How does Llama 4 stack up against GPT-4o, Claude, and Gemini?
On coding: Maverick scores 43.4 on LiveCodeBench versus GPT-4o’s 32.3 and Gemini Flash’s 34.5. That’s a meaningful difference if you’re building AI coding assistants.
On reasoning: Maverick hits 80.5 on MMLU Pro and 69.8 on GPQA Diamond, outperforming Gemini Flash (77.6 and 60.1) and GPT-4o (no reported MMLU Pro score, 53.6 on GPQA). For tasks requiring logical thinking and knowledge synthesis, Maverick competes with significantly larger closed models.
On cost: Here’s where open-source wins decisively. Through Together.ai, Maverick costs $0.27 per million input tokens versus GPT-4.5’s much higher pricing. Scout costs $0.18 per million input tokens—86% cheaper than premium alternatives for input processing. If you’re running millions of queries, this adds up fast.
On context length: Scout’s 10-million-token window dwarfs GPT-4.5’s context (which is about 1.28% of Scout’s capacity). For applications like legal document analysis or scientific research synthesis, this isn’t just an advantage—it’s a game-changer.
Where do closed models still win? Complex reasoning edge cases, consistent instruction-following in unusual scenarios, and customer support responsiveness. When things break with GPT-4, you call OpenAI support. When things break with Llama 4, you’re debugging it yourself—or hiring consultants who understand the architecture.
Should You Actually Use Llama 4?
Here’s when Llama 4 makes sense:
- You need to process sensitive data on-premise (healthcare, legal, finance)
- You’re running high-volume operations where API costs matter
- You need customization beyond what closed APIs allow
- Long-context processing is critical to your application
- You want multimodal capabilities without vendor lock-in
Here’s when it doesn’t:
- You’re prototyping and need something working today (Claude and GPT have better user-friendly interfaces)
- You lack infrastructure expertise for model deployment
- Your application requires the absolute bleeding edge in reasoning capabilities
- You’re in the EU and can’t navigate the licensing restrictions
After testing Llama 4 against closed alternatives for document analysis, image-based customer support, and code generation, my take is simple: for production systems with clear technical requirements, Llama 4 delivers. The 10-million-token context window alone opens use cases that were impossible before. But you’ll need real engineering talent to deploy it properly—this isn’t a plug-and-play API.
Getting Started (If You’re Ready)
Want to experiment? Download Scout or Maverick from Llama.com or Hugging Face. For quick testing without infrastructure, try Meta AI in WhatsApp, Messenger, or Instagram—Meta’s using Llama 4 in production across these apps in 40 countries.
For serious deployments, consider platforms like Amazon Web Services, which offers optimized Llama 4 hosting, or specialized inference providers that handle scaling, quantization, and optimization.
The real question isn’t whether Llama 4 is good—it clearly is. The question is whether open-weight AI will become the default over the next few years. As of December 2024, Llama had more than 650 million downloads, twice as many as three months prior, averaging one million downloads per day since February 2023. Those numbers suggest the industry’s already decided.
Your move.
Want to understand which tech giants are actually building with Llama 4? The companies shaping AI’s future—from NVIDIA’s GPU infrastructure to Meta’s open-source strategy—represent over $8 trillion in combined market value. Discover how the top Silicon Valley companies by market cap are positioning themselves in the AI race and what it means for developers, investors, and the future of technology.
Frequently Asked Questions
Can Llama 4 really compete with GPT-4 and Claude? Yes, in specific domains. Maverick outperforms GPT-4o on several benchmarks including coding, image reasoning, and document understanding. However, GPT-4 and Claude maintain advantages in edge-case reasoning and instruction-following for unusual queries. The gap is narrowing fast.
What hardware do I need to run Llama 4 Scout? Scout fits on a single NVIDIA H100 GPU with Int4 quantization. For testing, you might run quantized versions on A100 or even RTX 4090 GPUs with reduced context length. Maverick requires distributed infrastructure or multiple GPUs for full performance.
Is Llama 4 actually free to use commercially? Yes, unless your service exceeds 700 million monthly active users, in which case you need special licensing from Meta. For virtually all businesses, it’s free to use, modify, and deploy commercially under the Llama 4 Community License.
How does the 10-million-token context window actually help? Think entire books, months of chat history, or massive codebases processed in one pass without chunking or retrieval pipelines. For legal document analysis, scientific research, or deep code review, this eliminates the complexity of traditional RAG (retrieval-augmented generation) systems.
Can Llama 4 understand and generate images like DALL-E? Not quite. Llama 4 is natively multimodal, meaning it can understand and analyze images alongside text—but it doesn’t generate images. It excels at visual question answering, diagram interpretation, and chart analysis rather than creative image generation.
What languages does Llama 4 support? Officially supports 12 languages including English, Spanish, French, German, Arabic, Hindi, and more. It was trained on data from 200 languages total, so developers can fine-tune for additional languages while maintaining compliance with the license.
Why was Llama 4 released on a Saturday? The success of open models from Chinese AI lab DeepSeek reportedly kicked Llama development into overdrive, with Meta scrambling to decipher how DeepSeek lowered deployment costs. The unusual Saturday release timing suggests Meta moved up the launch in response to competitive pressure.
How do I choose between Scout and Maverick? Scout for: extremely long documents, research applications, maximum context length. Maverick for: conversational AI, multimodal applications, balanced performance across tasks. Scout is specialized; Maverick is generalist. Most developers start with Maverick.
