What Is LLaMA 4? Everything You Need to Know

Meta’s LLaMA 4 is the latest generation of large language models (LLMs) from Meta AI, unveiled on April 5, 2025. It represents a significant leap in Meta’s AI capabilities and open-source AI strategy. LLaMA 4 introduces a multimodal AI system that can understand and generate not just text, but also interpret images, video, and audio, converting content across these formats. In essence, LLaMA 4 is designed to be a sophisticated, versatile AI, building on the successes of its predecessors LLaMA 2 (open-sourced in 2023) and the internal LLaMA 3 series, while pushing the boundaries of scale and performance in the AI landscape.

Key Improvements in LLaMA 4

Multimodal Capabilities

Unlike earlier versions, LLaMA 4 can process and generate content from multiple types of data. For example, it can analyze an image or video clip and describe it, or even listen to audio and summarize it in text. This makes it perfect for integration with text-to-speech AI models like Resemble.

Greater Scale and Efficiency

LLaMA 4 uses a Mixture-of-Experts (MoE) architecture, meaning it has many specialized mini-models (experts) working together. This lets it scale up dramatically—up to trillions of parameters—without sacrificing speed, making it both powerful and efficient.

Impressive Context Length

One standout feature is its extended context window. The Scout model of LLaMA 4 can process inputs up to 10 million tokens long, enabling it to analyze entire books or large documents effortlessly.

Enhanced Multilingual Understanding

LLaMA 4 understands over 200 languages fluently, trained on trillions of tokens. It excels at translation, summarization, and content generation across languages, far exceeding earlier versions. This multimodal capability is common along most modern Generative AI models, but this is the first time a Generative AI model has claimed fluency in over 200 languages.

Advanced Reasoning and Coding

Meta significantly improved LLaMA 4’s reasoning and coding abilities. It performs exceptionally well on coding challenges and logical reasoning tasks, making it a valuable tool for developers and software engineers.

Key Improvements Over LLaMA 2 and 3

LLaMA 4 introduces major improvements over previous generations of Meta’s language models. To appreciate the leap, consider LLaMA 2 (released July 2023) which topped out at 70 billion parameters and was limited to text-only input/output. LLaMA 4, by contrast, is natively multimodal – it can analyze images, video, and audio in addition to text – and operates at a far greater scale thanks to a new architecture. It’s also significantly more capable in reasoning, coding, and handling long documents than earlier LLaMA models.

Here are some of the key enhancements LLaMA 4 brings compared to LLaMA 2 (and interim LLaMA 3 developments):

• Multimodal Understanding: LLaMA 4 is Meta’s first LLM that understands multiple data types. While LLaMA 2 was limited to text, LLaMA 4 can ingest images (and even video or audio) and generate or describe content across these formats. This means you can ask LLaMA 4 to analyze a picture or a snippet of video and get a meaningful response, a capability not present in LLaMA 2.
• Massive Scale via MoE: LLaMA 4 adopts a Mixture-of-Experts (MoE) architecture, unlike the dense transformer in LLaMA 2. MoE allows the model to scale up the number of parameters dramatically while only activating a subset of them for any given query. In LLaMA 4, multiple specialized “expert” sub-models handle different aspects of a task, coordinated by a routing system. This is a huge change: it lets LLaMA 4 reach into the hundreds of billions (and even trillions) of parameters in total, far beyond LLaMA 2’s size, without a proportional increase in computation for each prompt. The result is a more powerful model that remains efficient, since it uses only the experts it needs for a given input.
• Larger Model Variants: With the MoE approach, Meta delivered much larger model variants. For example, the Maverick variant of LLaMA 4 effectively has 400 billion parameters in total (spread across many experts), yet only about 17B are active at once. The smaller Scout variant uses about 17B active parameters out of 109B total. By comparison, LLaMA 2’s largest publicly released model was 70B (all active). There’s also the giant Behemoth model with nearly 2 trillion parameters (16 experts, 288B active) – an unprecedented scale for Meta. Even though Behemoth isn’t publicly released, it played a key role in training the others (serving as a teacher model for fine-tuning) and demonstrates how far Meta has pushed the model size envelope.
• Longer Context Window: Another leap is the context length (the amount of text the model can consider at once). LLaMA 2’s context window was limited (on the order of a few thousand tokens), but LLaMA 4 can handle extremely long inputs. The Scout model boasts an industry-leading 10 million token context window – effectively giving it a long-term memory for very large documents or conversations. Maverick also supports a very large context (Meta indicates around 1 million tokens for Maverick, which is still far beyond most competitors. Such vast context lengths mean LLaMA 4 can analyze entire books, codebases, or hours of transcript in one go, performing tasks like summarization or cross-referencing over truly extensive texts. This is a major improvement for use cases that involve long documents or streams of data, where LLaMA 2 would have been forced to truncate or lose earlier context.
• Training and Multilingual Mastery: Meta significantly expanded training data for LLaMA 4. Maverick was trained on a massive 30 trillion tokens dataset with advanced techniques like 8-bit floating point (FP8) precision training, whereas LLaMA 2’s training corpus was smaller (on the order of 2 trillion tokens). LLaMA 4 is also far more multilingual: it’s pretrained in 200 languages, with over 1 billion tokens for each of 100 of those languages. This vastly improves its understanding and generation in languages beyond English, surpassing LLaMA 2’s multilingual capabilities. In short, LLaMA 4 has a broader knowledge base and more diverse training, enabling it to perform well across languages and domains.
• Enhanced Reasoning and Coding Skills: LLaMA 4 was engineered to overcome the reasoning limitations of its predecessors. Meta specifically targeted improvements in math, logic, and coding. The Maverick model, for instance, is described as being great at “advanced reasoning [and] coding” tasks, whereas LLaMA 2 sometimes struggled with complex reasoning without additional fine-tuning. LLaMA 4 also incorporates feedback from a “teacher” model (Behemoth) during training via co-distillation, which likely helped it learn more robust problem-solving techniques. As a result, LLaMA 4 can handle complex step-by-step reasoning and programming queries more effectively out-of-the-box than earlier LLaMA versions.

Technical Overview

LLaMA 4 comes in three variants:

• Scout: Ideal for efficiency, running on a single GPU. It offers an extraordinary 10 million token context window, perfect for analyzing large-scale data.
• Maverick: A flagship model designed for advanced tasks and coding support, capable of handling complex multimodal inputs with a 1-million token context.
• Behemoth: An internal Meta model with nearly 2 trillion parameters used for training other models, not publicly released but showcasing the upper limits of LLaMA’s capabilities.

Real-World Use Cases

LLaMA 4’s capabilities make it suitable for various applications:

• Chatbots and Virtual Assistants: Enhance customer service with chatbots that understand visual and auditory context.
• Enterprise Document Analysis: Quickly analyze extensive documentation, making it ideal for legal, financial, or research industries.
• Software Development: Assist developers by generating and debugging code efficiently.
• Content Creation: Generate engaging content, from social media posts to detailed articles.
• Educational Tools: Create interactive, multimodal learning environments for students.

Industry and Community Reactions

The release of LLaMA 4 has generated significant excitement in the tech industry and AI research communities. Here are some of the immediate reactions and implications observed:

Open-Source Community Applause: Just as with LLaMA 2, Meta’s decision to open-source LLaMA 4 (Scout and Maverick) has been largely applauded by developers and researchers. Many see it as a win for transparency and innovation. Within hours of release, LLaMA 4 models appeared on repositories like Hugging Face, and enthusiasts began testing them against other models. The community has been particularly excited about the performance-to-accessibility ratio – getting GPT-4-like (or better) performance without needing to pay for API calls or wait in queue. Some comments on forums praised Meta for “putting powerful AI into everyone’s hands”, empowering even small startups or independent researchers to experiment at the cutting edge. Meta’s positioning of LLaMA 4 as the start of a “new era for the LLaMA ecosystem”, resonates with those who prefer open models over closed ones.

Concerns Over Licensing Terms: Not everything is rosy – Meta’s license for LLaMA 4, like LLaMA 2’s, is not a standard open-source license. It has restrictions: for instance, companies with over 700 million users must get special permission from Meta to use the model, and users in the EU are outright prohibited from using or distributing LLaMA 4 models due to compliance with upcoming AI regulations. The Open Source Initiative (OSI) has criticized such licenses, saying that calling the model “open source” isn’t accurate if these restrictions exist. This has sparked discussion in the developer community about what “open” truly means. Some worry that the EU restriction sets a precedent – it’s unusual to ban an entire region from using an AI model, and it highlights tension between tech companies and regulators. European AI developers might feel left out or forced to use VPNs/grey areas to play with LLaMA 4. Meta likely did this to avoid legal risk with the EU AI Act, but it’s a notable controversy. Nonetheless, for the vast majority of individuals and organizations (especially outside the EU and not tech giants), LLaMA 4 is effectively as accessible as any open-source project.

Competitive Pressure: LLaMA 4’s release on a Saturday (which even TechCrunch quipped about indicates Meta’s aggressive push to get it out. Insiders report that competition from other models accelerated Meta’s timeline. The success of models like those from DeepSeek (China), which managed to match LLaMA 3’s performance and do so cheaply, “kicked LLaMA development into overdrive”. In the broader industry, Meta’s move puts pressure on OpenAI, Google, and others. OpenAI now faces a world where a free model can do a lot of what their premium model does – which could affect their enterprise business if companies decide to fine-tune LLaMA 4 instead of paying OpenAI. Google, which has been more cautious about open-sourcing, sees Meta gaining mindshare among developers for AI platforms. We might see these companies respond in kind: perhaps OpenAI releasing more model details or offering cheaper GPT-4 access, or Google releasing a toned-down open version of Gemini, for example, to stay in the game. Even startups (Anthropic, etc.) need to consider how to differentiate when the community can simply build on Meta’s offering. In essence, LLaMA 4’s presence democratizes some of the AI power that was previously an exclusive advantage of the biggest players, leveling the playing field a bit.

Investor and Business Interest: On the business side, LLaMA 4 has drawn interest from enterprises looking to incorporate AI. With LLaMA 2, we saw partnerships like with Microsoft (offering LLaMA on Azure). For LLaMA 4, we can expect cloud providers and enterprise software firms to quickly integrate it. In fact, Databricks announced support for LLaMA 4 to allow companies to run it on their platform securely. The ability to run a model behind your own firewall is huge for industries like finance or healthcare that deal with sensitive data. Many companies who were experimenting with GPT-4 through APIs may switch to LLaMA 4 for production if it meets their needs, to avoid data leakage or reduce costs. Meta’s own massive investment (reportedly $65 billion in 2025 for AI infrastructure). underscores how strategic LLaMA 4 and its successors are to Meta’s future. They’re not just research demos; Meta intends these models to be foundational to many products and services, and businesses in turn recognize that Meta is serious here.

Academic and Ethical Discussion: The academic community is also reacting – some are eager to study LLaMA 4’s performance and behavior (having access to weights is great for science), while others caution about what widespread availability of such a powerful model means. There are concerns about potential misuse: e.g., could LLaMA 4 be used to generate more convincing deepfakes or disinformation since it can handle images and text together? Meta has attempted to preempt that by not enabling unrestricted image generation (LLaMA 4 doesn’t generate new images, it only interprets them) and by licensing terms. But as these models proliferate, the call for AI governance grows louder. Meta’s somewhat restrictive license actually hints at this – they’re trying to comply with forthcoming rules. We might see LLaMA 4 become a case study in the balance between open development and controlled deployment.

Overall, the community reaction to LLaMA 4 is very positive, with healthy debates on the side. It’s viewed as a milestone that a model of this sophistication is available (some say “LLaMA 4 is to 2025 what Linux was to the early 2000s” in terms of open tech empowerment). The coming months will likely see rapid adoption, and the community will flush out both the exciting possibilities and the potential pitfalls of LLaMA 4’s open use.

Implications for Businesses and Developers

LLaMA 4 empowers businesses to innovate rapidly, enabling:

• Cost-effective AI solutions without relying on external APIs.
• Customization and ownership of AI models tailored to specific industry needs.
• Compliance and data security, crucial for regulated industries.

Conclusion

LLaMA 4 is a significant step forward for AI, combining cutting-edge capabilities with accessibility. Its multimodal nature, extended context length, and improved reasoning make it one of the most powerful AI tools available today. Businesses and developers ready to leverage AI’s full potential will find LLaMA 4 to be an invaluable asset, opening the door to exciting new possibilities.

Sources:

• Meta Platforms’ announcement of LLaMA 4’s release, highlighting the multimodal capabilities and the open-source release of the Scout and Maverick models, as well as the preview of the larger Behemoth model.
• Detailed technical coverage by TechCrunch on LLaMA 4’s model architecture and performance, including the use of MoE (Mixture-of-Experts) and parameter counts for Scout (109B total, 17B active) and Maverick (400B total, 17B active). TechCrunch also reported Meta’s internal benchmarks where Maverick exceeds GPT-4 and Gemini 2.0 on several tasksand described Scout’s extraordinary 10 million token context window.
• Reporting from The Verge on the LLaMA 4 launch, which provided quotes from Mark Zuckerberg (calling LLaMA 4 Behemoth “the highest performing base model in the world”) and noted how Scout and Maverick compare to competitor models like Google’s Gemma 3, Gemini 2.0 Flash, and OpenAI’s GPT-4o. The Verge also discussed the licensing restrictions for EU users and companies with over 700M users.
• An in-depth breakdown by Analytics Vidhya of the three LLaMA 4 models (Scout, Maverick, Behemoth), including their architecture, context lengths, and benchmark achievements. This source highlighted features like Scout’s ability to run on a single GPU and Maverick’s training on a 30T token dataset, as well as Behemoth’s role in distillation.
• Reuters news piece on the LLaMA 4 release, which mentioned the development delays to improve LLaMA 4’s reasoning and the fact that Meta is investing $65B in AI infrastructure in 2025.
• Meta’s own statements (via blog and spokesperson quotes) on LLaMA 4’s alignment changes – e.g., the model being more willing to tackle contentious questions and provide balanced answers, aiming to reduce the perception of political bias in responses.

These sources collectively provide a comprehensive picture of LLaMA 4 as of its launch date, from factual specifications to the broader impact on the tech industry. Each piece of information in this article is backed by current reports and Meta’s official communications to ensure accuracy and currency as of April 5, 2025.