Meta Llama 4: A Comprehensive Overview

What is Meta Llama 4?

Meta’s Llama, initially known as LLaMA (Large Language Model Meta AI), entered the LLM landscape in February 2023. Llama 2’s release in July 2023 adopted an open permissive license, fostering wider adoption. Through continuous improvements, Llama has established itself among leading models like OpenAI, Anthropic, and Google.

The Llama family expanded on April 5, 2025, with the introduction of the Llama 4 model family, a new generation of multimodal LLMs.

Meta Llama 4 marks a significant advancement in LLM technology, incorporating multimodal capabilities to process text, images, and video. This fourth-generation model supports many languages.

A notable innovation in the Llama 4 models is the mixture-of-experts architecture. This architecture activates a subset of the total parameters for each input token, balancing power and efficiency.

While the Llama 4 community license is not officially Open Source Initiative-approved, Meta describes its Llama 4 models as open source. The license allows free usage and modification, subject to limitations. As of April 2025, the limit was 700 million monthly users.

The Llama 4 lineup includes Scout, Maverick, and Behemoth. Scout and Maverick launched concurrently, while Behemoth is in development.

• Llama 4 Scout: 17 billion active parameters, 16 experts, 109 billion total parameters, a 10 million-token context window, and an August 2024 knowledge cutoff.
• Llama 4 Maverick: 17 billion active parameters, 128 experts, 400 billion total parameters, a 1 million-token context window, and the same knowledge cutoff as Scout.
• Llama 4 Behemoth: 288 billion active parameters, 16 experts, 2 trillion total parameters, and an unspecified context window and knowledge cutoff.

Capabilities of Meta Llama 4

The Meta Llama 4 models offer a diverse range of applications:

• Native Multimodality: Concurrent understanding of text, images, and video. It derives context from diverse sources.
• Content Summarization: Efficiently summarizes information from various content types. For instance, analyzing a video, extracting scenes, and generating a summary.
• Long-Context Processing: Llama 4 Scout processes large volumes of information using its 10 million-token context window. It analyzes research papers or processes lengthy documents.
• Multilingual Modality: All Llama 4 models support multiple languages for text processing: Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese. Image understanding is limited to English.
• Text Generation: Generates coherent text, including creative writing, and adapts to various styles.
• Advanced Reasoning: Reasons through scientific and mathematical problems and arrives at conclusions.
• Code Generation: Comprehends and generates application code and assists developers.
• Base Model Functionality: Llama 4 serves as a foundation for derivative models. Researchers can fine-tune it for specific tasks.

Training Methodology of Meta Llama 4

Meta used advanced techniques to train its fourth-generation Llama family LLMs.

• Training Data: Llama 4 was trained on over 30 trillion tokens, doubling the data used for Llama 3.
• Early Fusion Multimodality: Integrates text and vision tokens into a unified model, fostering a natural understanding between visual and textual information.
• Hyperparameter Optimization: Fine-tunes model hyperparameters, such as per-layer learning rates.
• iRoPE Architecture: The interleaved attention layers without positional embeddings architecture enhances the handling of long sequences and facilitates the 10 million-token context window in Llama 4 Scout.
• MetaCLIP Vision Encoder: Translates images into token representations for improved multimodal understanding.
• GOAT Safety Training: Meta implemented the Generative Offensive Agent Tester (GOAT) to identify vulnerabilities and improve model safety.

Evolution of the Llama Models

Following ChatGPT’s launch in November 2022, companies entered the LLM market. Meta introduced its initial Llama models in early 2023 with restricted access. All subsequent models have been made available under open licenses.

• Llama 1: Launched in February 2023 with limited access.
• Llama 2: Released in July 2023 with an open license and included 7B, 13B, and 70B parameter versions.
• Llama 3: Debuted in April 2024 with 8B and 70B parameter versions.
• Llama 3.1: Launched in July 2024, adding a 405B parameter model.
• Llama 3.2: Released in October 2024 as Meta’s first fully multimodal LLM.
• Llama 3.3: Meta claimed its December 2024 release delivered the same performance as 3.1’s 405B variant, while requiring fewer computational resources.

Llama 4 in Comparison to Other Models

The AI landscape includes players such as OpenAI’s GPT-4o, Google Gemini 2.0, and open-source projects.

Llama 4’s performance is assessed using benchmarks:

• MMMU (Massive Multi-discipline Multimodal Understanding): Evaluates image reasoning.
• LiveCodeBench: Assesses coding proficiency.
• GPQA Diamond (Graduate-Level Google-Proof Q&A Diamond): Measures reasoning and knowledge.

Higher scores indicate better performance.

These benchmarks highlight Llama 4 Maverick’s strengths.

Accessing Llama 4

Meta Llama 4 Maverick and Scout are available through various channels:

• Llama.com: Download Scout and Maverick directly from llama.com.
• Meta.ai: The Meta.ai web interface provides browser-based access.
• Hugging Face: Llama 4 is accessible at https://huggingface.co/meta-llama.
• Meta AI App: Llama 4 powers Meta’s AI virtual assistant.

To delve deeper into the architecture, training methodologies, and specific use cases of Llama 4, one must consider the innovations that distinguish it from its predecessors and competitors. Llama 4’s adoption of a mixture-of-experts (MoE) architecture represents a strategic shift towards more efficient parameter utilization. In essence, an MoE model consists of multiple sub-networks, often referred to as “experts,” and a gating network that dynamically selects which experts are activated for a given input. This approach allows the model to maintain a large overall parameter count, enabling it to capture a wide range of knowledge and skills, while only activating a fraction of those parameters for any single input. This results in a significant reduction in computational cost and latency, making the model more practical for real-world applications. The specific configuration of the MoE architecture in Llama 4, including the number of experts, the size of each expert, and the design of the gating network, plays a crucial role in determining the model’s performance and efficiency.

The training data used for Llama 4 is another critical factor that contributes to its capabilities. By training on over 30 trillion tokens, Meta has equipped the model with a vast amount of knowledge about the world,covering a wide range of topics, languages, and modalities. The quality and diversity of the training data are also essential, as they influence the model’s ability to generalize to new and unseen inputs. Meta has likely employed various techniques to curate and preprocess the training data, such as filtering out noisy or irrelevant content, balancing the representation of different languages and topics, and augmenting the data with synthetic examples. The training process itself involves optimizing the model’s parameters to minimize the difference between its predictions and the ground truth labels in the training data. This is typically done using gradient descent algorithms, which iteratively adjust the parameters based on the error signal. Meta has likely employed advanced optimization techniques, such as adaptive learning rates, momentum, and weight decay, to accelerate the training process and improve the model’s convergence.

The early fusion multimodal architecture in Llama 4 is a key enabler of its ability to process and understand text, images, and video data in a unified manner. In this approach, the different modalities are combined early in the processing pipeline, allowing the model to learn cross-modal relationships and dependencies. This contrasts with late fusion approaches, where the modalities are processed separately and then combined at a later stage. Early fusion has the advantage of allowing the model to learn more fine-grained interactions between the modalities, but it also requires more sophisticated training techniques to prevent the model from being overwhelmed by the complexity of the data. Meta has likely employed techniques such as cross-modal attention and contrastive learning to effectively train the early fusion multimodal architecture in Llama 4.

The interleaved attention layers without positional embeddings (iRoPE) architecture is a novel approach to handling long sequences in Llama 4. Traditional transformer models rely on positional embeddings to encode the order of the tokens in the input sequence. However, positional embeddings can become problematic for very long sequences, as they can limit the model’s ability to attend to distant tokens. The iRoPE architecture addresses this issue by interleaving attention layers without positional embeddings, allowing the model to attend to any token in the sequence, regardless of its position. This is particularly beneficial for tasks such as analyzing extensive research papers or processing lengthy documents, where the context can span thousands or even millions of tokens. The iRoPE architecture also allows the model to generalize to sequences longer than those seen during training, which is a crucial capability for real-world applications.

The MetaCLIP vision encoder is a critical component of Llama 4’s multimodal capabilities. This encoder is responsible for translating images into token representations that can be processed by the model. The encoder is likely based on the CLIP (Contrastive Language-Image Pre-training) model, which is a state-of-the-art approach for learning visual representations from paired image-text data. The MetaCLIP vision encoder has likely been fine-tuned on a large dataset of images to improve its performance on specific tasks, such as image classification, object detection, and image captioning. The encoder’s ability to accurately and efficiently translate images into token representations is crucial for Llama 4’s ability to understand and reason about visual information.

The Generative Offensive Agent Tester (GOAT) is a novel approach to improving the safety and robustness of Llama 4. This technique involves training a generative model that can generate adversarial examples, i.e., inputs that are designed to trick the model into producing harmful or biased outputs. These adversarial examples are then used to train the model to be more resilient to such attacks. The GOAT approach is particularly effective at identifying and mitigating vulnerabilities in the model that might not be apparent from standard evaluation metrics. By continuously testing the model with adversarial examples, Meta can ensure that it is safe and reliable for real-world deployment. This proactive approach to safety is essential for building trust in LLMs and ensuring that they are used responsibly. The details of the GOAT training process, including the architecture of the generative model, the loss function used to train it, and the specific types of adversarial examples generated, are likely proprietary to Meta.

The competitive landscape for generative AI is constantly evolving, with new models and techniques emerging at a rapid pace. Llama 4 faces stiff competition from other leading LLMs, such as OpenAI’s GPT-4o and Google’s Gemini 2.0. These models have their own strengths and weaknesses, and the best model for a particular task will depend on the specific requirements of the application. GPT-4o is known for its strong performance on a wide range of NLP tasks, while Gemini 2.0 is particularly adept at handling multimodal data. Llama 4’s strengths lie in its open-source nature, its efficient architecture, and its strong performance on image reasoning, coding, and general knowledge. The benchmarks provided in the original article, such as MMMU, LiveCodeBench, and GPQA Diamond, offer a snapshot of Llama 4’s performance relative to other models on specific tasks. However, it is important to note that these benchmarks are not exhaustive and may not fully capture the nuances of each model’s capabilities.

Accessing and using Llama 4 is relatively straightforward, thanks to its open-source nature and the availability of pre-trained models on platforms such as Hugging Face. Users can download the models directly from the Meta-operated llama.com website, or they can access them through the Meta.ai web interface. The Meta AI App also provides a convenient way to interact with Llama 4 via voice or text. To effectively use Llama 4, users need to have a basic understanding of LLMs and how to interact with them. This includes understanding the different types of prompts that can be used to elicit desired responses, as well as the various parameters that can be used to control the model’s behavior, such as temperature and top-p sampling. Users also need to be aware of the limitations of the model, such as its potential to generate biased or harmful content. By understanding these limitations, users can take steps to mitigate them and ensure that the model is used responsibly. The open-source nature of Llama 4 allows users to fine-tune the model for specific tasks, which can significantly improve its performance. Fine-tuning involves training the model on a smaller, more specialized dataset, using techniques such as transfer learning and domain adaptation. This allows the model to leverage its existing knowledge while also learning new skills specific to the target task.