Meta released its latest breakthrough: Llama 4, a collection of natively multimodal AI models. The Llama 4 family consists of three distinct models designed to serve different use cases and computational requirements.

Llama 4 Scout, a 17 billion active parameter model with 16 experts, offers exceptional performance while fitting on a single NVIDIA H100 GPU.
Llama 4 Maverick, also with 17 billion active parameters but expanded to 128 experts, delivers even more impressive capabilities.

Completing the herd is Llama 4 Behemoth, a massive model with 288 billion active parameters and 16 experts. While still in training, it already demonstrates performance that rivals or even exceeds some of the most advanced AI systems from competitors like OpenAI and Google.

Its innovative architecture and design set Llama 4 apart from its predecessors and many competitors. These models are the first in the Llama series to employ a mixture-of-experts (MoE) architecture, improving computational efficiency by activating only a fraction of the total parameters for each token processed.

Additionally, Llama 4 models feature native multimodality with early fusion, seamlessly integrating text and vision capabilities into a unified model backbone. This integration represents a fundamental shift from previous approaches that required separate vision encoders awkwardly bolted onto language models.

The introduction of Llama 4 comes at a time of intense competition in the AI space, with companies like OpenAI, Google, Anthropic, and Chinese AI lab DeepSeek all vying for leadership in large language model development.

Meta’s approach of making these powerful models openly available for download—with certain licensing restrictions—continues to distinguish its strategy from more closed competitors, potentially accelerating innovation across the broader AI ecosystem.

Technical Architecture and Training of Llama 4

In traditional dense language models, every input token activates the entire parameter set, requiring substantial computational power for both training and inference. Llama 4 takes a radically different approach with its MoE architecture, where a single token activates only a fraction of the total parameters. This selective activation dramatically improves computational efficiency while maintaining or even enhancing model quality.

For example, Llama 4 Maverick has 17 billion active parameters but 400 billion total parameters. The model uses alternating dense and MoE layers for inference efficiency, with MoE layers containing 128 routed experts and a shared expert.

When processing information, each token is sent to the shared expert and to one of the 128 routed experts, meaning that while all parameters are stored in memory, only a subset is activated during operation.

This architecture yields tangible benefits:

Lower model serving costs
Reduced latency
Improved performance per computational unit

Llama 4 Maverick can run on a single NVIDIA H100 DGX host for straightforward deployment, while Llama 4 Scout can operate on a single H100 GPU when using Int4 quantization, making it accessible to a broader range of developers and organizations.

Native multimodality represents another architectural breakthrough in Llama 4. The early fusion approach enables joint pre-training with large amounts of unlabeled text, image, and video data, creating a more cohesive understanding across modalities.

The vision encoder in Llama 4 is based on MetaCLIP but trained separately in conjunction with a frozen Llama model to better adapt the encoder to the language model’s requirements.

Meta also developed a new training technique called MetaP that allows for the reliable setting of critical model hyper-parameters such as per-layer learning rates and initialization scales. These hyper-parameters transfer well across different values of batch size, model width, depth, and training tokens, enhancing the model’s adaptability and performance.

Additionally, Llama 4 enables open-source fine-tuning efforts by pre-training on 200 languages, including over 100 with more than 1 billion tokens each—representing a tenfold increase in multilingual tokens compared to Llama 3.

The training process incorporated several efficiency innovations. Meta used FP8 precision without sacrificing quality, ensuring high model FLOPs utilization. During pre-training of the Llama 4 Behemoth model using FP8 and 32,000 GPUs, Meta achieved an impressive 390 TFLOPs per GPU. The overall data mixture for training consisted of more than 30 trillion tokens—more than double the Llama 3 pre-training mixture.

It included diverse text, image, and video datasets from publicly available sources, licensed content, and information from Meta’s products and services, including publicly shared posts from Instagram and Facebook.

The Llama 4 Model Family

Llama 4 Scout stands as the most accessible member of the family, featuring 17 billion active parameters with 16 experts and 109 billion total parameters. According to Meta, Scout is “the best multimodal model in the world in its class” and more powerful than all previous generation Llama models. Scout is its industry-leading context window of 10 million tokens, enabling it to simultaneously process and reason over extremely lengthy documents or multiple images.

This extraordinary context capacity makes Scout particularly well-suited for tasks like document summarization, reasoning over large codebases, and complex research applications requiring extensive contextual understanding. In benchmark testing, Scout delivers better results than Gemma 3, Gemini 2.0 Flash-Lite, and Mistral 3.1 across a broad range of widely reported benchmarks.

Llama 4 Maverick represents the middle tier of the family, with its 128 experts and 400 billion total parameters. This configuration allows Maverick to achieve performance that rivals or exceeds much larger models from competitors.

Meta positions Maverick as “the best multimodal model in its class,” beating GPT-4o and Gemini 2.0 Flash across numerous benchmarks, while achieving comparable results to the new DeepSeek v3 on reasoning and coding tasks—with less than half the active parameters.

Maverick offers a context window of 1 million tokens and delivers a best-in-class performance-to-cost ratio, with an experimental chat version scoring an impressive ELO of 1417 on LMArena. Maverick requires more substantial hardware than Scout, needing an NVIDIA H100 DGX host for deployment, but can be run with distributed inference for maximum efficiency.

Llama 4 Behemoth, still in training at the time of release, represents Meta’s most ambitious AI model to date. With 288 billion active parameters, 16 experts, and nearly 2 trillion total parameters, Behemoth is positioned as “one of the smartest LLMs in the world.”

Early benchmark results show Behemoth outperforming GPT-4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro on several STEM-focused benchmarks such as MATH-500 and GPQA Diamond. Meta describes Behemoth as their “most powerful yet” model, designed to serve as a teacher for distilling knowledge into the smaller, more efficient Scout and Maverick models.

While Meta has not yet released Behemoth publicly, the company has shared technical details about its approach and performance characteristics, suggesting a future release once training is complete.

All three models support the same 12 core languages: Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese. However, the models have been pre-trained on a broader collection of 200 languages, enabling developers to fine-tune them for additional language support if needed.

Llama 4 Performance and Multimodal Capabilities

In reasoning and knowledge benchmarks, Llama 4 Maverick achieves a 73.4% accuracy on the Massive Multitask Language Understanding (MMMU) benchmark and 73.7% on MathVista, demonstrating sophisticated visual reasoning capabilities.

On the ChartQA benchmark with zero-shot prompting, Maverick reaches 90.0% relaxed accuracy. For document visual question answering (DocVQA), Maverick performs exceptionally well, achieving 94.4% on the test set.

For coding tasks, Llama 4 Maverick shows significant improvements. On the LiveCodeBench evaluation, it achieves a 43.4% pass@1 rate, outperforming Llama 3.3 70B (33.3%).

When compared to competitor models, Llama 4 Maverick holds its own against much larger systems. According to Meta’s internal testing, Maverick exceeds models such as OpenAI’s GPT-4o and Google’s Gemini 2.0 Flash on certain coding, reasoning, multilingual, long-context, and image benchmarks.

However, it doesn’t quite match the capabilities of more recent flagship models like Google’s Gemini 2.5 Pro, Anthropic’s Claude 3.7 Sonnet, and OpenAI’s GPT-4.5. This positioning is strategic, as Maverick offers a compelling balance of performance and efficiency, while the forthcoming Behemoth model is designed to compete directly with these top-tier systems.

The native multimodal capabilities of Llama 4 manifest across several domains. For visual recognition tasks, the models demonstrate strong performance in identifying objects, scenes, and activities within images. They can accurately describe visual content with nuanced detail, capturing both obvious elements and subtle contextual cues. This capability extends to specialized domains like chart and document analysis, where Llama 4 models achieve impressive results.

Image reasoning represents a particularly challenging aspect of multimodal AI, requiring models to not just recognize visual elements but to reason about their relationships and implications. Here, Llama 4 Maverick demonstrates sophisticated capabilities, effectively solving problems that require integrating visual and mathematical understanding, such as analyzing graphs, interpreting diagrams, and solving visually presented math problems.

The models have been tested for image understanding with up to five input images, though Meta notes that developers who wish to leverage additional image understanding capabilities beyond this should perform additional testing and mitigate potential risks. This limitation reflects the practical constraints of current multimodal systems, which must balance performance with computational efficiency and reliability.

Key Takeway

Llama 4 represents a watershed moment in the evolution of artificial intelligence, marking the beginning of what Meta calls “a new era for the Llama ecosystem.” Through innovative architectural choices—particularly the mixture-of-experts design and native multimodality—these models achieve remarkable performance while maintaining computational efficiency. The three-tiered approach of Scout, Maverick, and the forthcoming Behemoth provides options for different use cases and computational constraints, democratizing access to cutting-edge AI capabilities.

Meta’s continued commitment to open-source AI development, albeit with certain licensing restrictions, stands in contrast to the more closed approaches of many competitors. This strategy has the potential to accelerate innovation across the AI ecosystem, enabling developers worldwide to build upon and extend these powerful foundation models. As organizations and individuals explore the possibilities of Llama 4, we can expect to see novel applications emerge across domains ranging from healthcare and education to creative arts and scientific research.

Further Resources

FAQs

Q 1. What is LLaMA 4 AI?

LaMA 4 AI refers to Meta Platforms’ latest series of artificial intelligence models, including Llama 4 Scout, Llama 4 Maverick, and the forthcoming Llama 4 Behemoth. These models are designed to enhance reasoning, coding, and multimodal processing capabilities across various applications.

Q 2. What is LLaMA 4 scout?

Llama 4 Scout is a compact AI model within the Llama 4 series, optimized to operate efficiently on a single Nvidia H100 GPU. It features a 10-million-token context window and delivers superior performance in language understanding tasks compared to previous models.

Q 3. How to access LLaMA 4?

Llama 4 models can be accessed through Meta’s official channels, including their website and AI platforms. Developers and researchers may also obtain the models via repositories like Hugging Face. For detailed access instructions, refer to Meta’s official documentation.

Q 4. Is llama 4 open source?

While Meta has released Llama 4 models with open-source licenses, these licenses include restrictions, particularly for commercial entities with over 700 million users. This approach has sparked discussions about its alignment with traditional open-source principles.

Q 5. Is llama better than GPT?

GPT-4 Speed and Efficiency: Llama 2 is frequently viewed as faster and more resource-efficient than GPT-4. The larger size and complexity of GPT -4 may demand increased computational resources, which can lead to slower performance in comparison.

Llama 4: The Beginning of a New Era in AI Innovation