Multimodal LLMs: LLaMA 4 Review

What is LLaMA 4?

In early 2025, Meta AI introduced LLaMA 4, a landmark advancement in the field of large language models (LLMs) that pushes the boundaries of multimodal understanding and computational efficiency. This new model family marks Meta’s first adoption of the Mixture-of-Experts (MoE) architecture within the LLaMA series, enabling unprecedented scale and specialization without proportional increases in inference costs.

Unlike traditional monolithic transformer models that activate all parameters for every input, LLaMA 4 leverages MoE to route queries dynamically through specialized subnetworks—or “experts”—activating only a fraction of the model’s parameters per inference. This design innovation allows LLaMA 4 to combine the benefits of massive model capacity with efficient computation, making it a versatile solution for a wide range of AI applications.

The LLaMA 4 family:

LLaMA 4 Scout: A compact yet powerful model optimized for efficiency, Scout activates 17 billion parameters at runtime, supported by 16 experts. It boasts an industry-leading context window capable of processing up to 10 million tokens, far exceeding most existing LLMs. Despite its relatively modest active size, Scout surpasses the previous LLaMA 3 in accuracy and efficiency, making it ideal for applications requiring long-context understanding, such as multi-document summarization and extensive codebase analysis.

LLaMA 4 Maverick: Designed for more complex AI reasoning and coding tasks, Maverick maintains the same 17 billion active parameters but is backed by 128 experts, totaling approximately 400 billion parameters. This architecture allows Maverick to scale from single-GPU inference for one-shot tasks to multi-GPU setups for demanding workloads. Like Scout, Maverick is natively multimodal, capable of processing both text and images to enable rich, context-aware understanding.

Meta also previewed a third, unreleased model, LLaMA 4 Behemoth, which pushes the MoE concept to the extreme with 288 billion active parameters and nearly 2 trillion total parameters. While Behemoth is currently a research-focused “teacher” model, too large for practical deployment, it serves as the knowledge source for distilling the more efficient Scout and Maverick models. Behemoth has reportedly outperformed models like GPT-4.5, Claude 3.7 Sonnet (or its May 2025 successor, Claude 4 Sonnet), and Gemini 2.0 Pro on challenging STEM benchmarks such as MATH-500 and GPQA Diamond.

LLaMA 4 integrates multimodal inputs seamlessly, jointly attending to textual and visual tokens within a unified transformer framework. This capability enables applications that require rich visual grounding alongside language understanding.

Architecturally, LLaMA 4 departs from traditional LLM embeddings, instead employing interleaved attention layers and an inference-time temperature scaling technique. These innovations allow the model to maintain coherence across extremely long sequences, from a few tokens up to millions, unlocking new possibilities in document analysis and beyond.

By combining cutting-edge MoE architecture with multimodal proficiency and an open-model philosophy, LLaMA 4 sets a new standard for accessible, high-performance AI. It offers enterprises and developers a powerful platform to build next-generation applications that require both scale and efficiency.

Meta’s LLaMA 4 represents a pivotal leap in the evolution of large language models, introducing a new era of multimodal, open-model AI. Built on a mixture-of-experts (MoE) architecture, LLaMA 4 departs from the traditional monolithic transformer approach, instead routing queries through specialized expert subnetworks. This allows only a fraction of the model’s parameters to be activated per inference, delivering both scale and efficiency. Model Variants are as follows:

Architectural Innovations:

• LLaMA 4 forgoes traditional positional embeddings in favor of interleaved attention layers and an inference-time temperature scaling technique.
• These innovations enable the model to maintain coherence across context lengths ranging from a few tokens up to the multi-million range.
• Multimodal input handling is tightly integrated, allowing the model to jointly attend to both visual and textual tokens.

With its open-model philosophy and breakthrough MoE design, LLaMA 4 empowers organizations and developers to harness state-of-the-art multimodal intelligence, without the prohibitive costs or constraints of closed, monolithic models.

What’s New in LLaMA 4’s Architecture

LLaMA 4 is Meta AI’s latest open-model suite, introducing a mixture-of-experts (MoE) architecture for the first time in the LLaMA family. Unlike traditional monolithic transformers, MoE enables the model to route queries through specialized expert subnetworks, activating only a fraction of its parameters per inference. This design delivers both computational efficiency and scalability, allowing LLaMA 4 to handle massive workloads without the prohibitive costs of fully dense models.

Under the hood, LLaMA 4 incorporates several novel architectural choices. It forgoes traditional positional embeddings in favor of interleaved attention layers, paired with an inference-time temperature scaling technique. These innovations allow LLaMA 4 to maintain coherence across extremely long context windows, from just a few tokens up to the multi-million range. Multimodal input handling is also tightly integrated; images are processed in-line, enabling the model to jointly attend to both visual and textual tokens.

LLaMA 4 Scout

• A “small” but powerful model designed to fit on a single H100 GPU.
• Utilizes 17 billion active parameters with 16 experts, meaning only ~17B parameters are engaged at runtime.
• Features an industry-leading context window of 10 million tokens—orders of magnitude larger than most LLMs.
• Despite its modest active size, Scout outperforms LLaMA 3 in accuracy and is Meta’s most efficient model in its class.

LLaMA 4 Maverick

• A higher-end model targeting more complex reasoning and coding tasks.
• Also has 17B active parameters, but is backed by 128 experts for a total of ~400 billion parameters.
• Can operate on a single H100 for one-shot tasks or scale out to multi-GPU inference for heavy workloads.
• Natively multimodal—accepts image inputs (and even video frame stills) alongside text, enabling rich visual grounding.

LLaMA 4 Behemoth

• An unreleased “teacher” model, currently in preview.
• Pushes the MoE architecture further with 288 billion active parameters (16 experts) and nearly 2 trillion total parameters.
• Too large for practical deployment, Behemoth serves as a frontier model for research and as a teacher for distilling knowledge into Scout and Maverick.

By pioneering MoE at this scale and integrating seamless multimodal capabilities, LLaMA 4 gives organizations and developers a foundation for building intelligent, efficient, and highly specialized AI solutions, raising the bar for what’s possible with open models.

LLaMA 4’s Scout and Maverick variants are both the result of a rigorous, multi-stage training and distillation process, designed to maximize performance while maintaining efficiency.

Pretraining

Both models were initially pretrained on a diverse dataset of text and images, reflecting LLaMA 4’s native multimodal capabilities. This broad pretraining enables the models to understand and reason across both language and vision tasks.

Distillation from Behemoth

Scout and Maverick were derived from the giant LLaMA 4 Behemoth model through a specialized co-distillation process. Meta developed a novel distillation loss function and a dynamic data selection strategy to effectively “compress” Behemoth’s knowledge into the smaller, expert-based models. This approach allowed the 17B-active-parameter models to retain competitive performance with far fewer resources.

Fine-Tuning

After pretraining, a lightweight supervised fine-tuning (SFT) stage was applied using instruction-following data. This was followed by online reinforcement learning for alignment, and then Direct Preference Optimization (DPO). Notably, for Maverick’s fine-tuning, Meta filtered out over 50% of the SFT data to emphasize only the hardest examples, further sharpening the model’s capabilities.

As a result of this training pipeline, Scout delivers better throughput and scalability than LLaMA 3 while achieving higher accuracy. Both Scout and Maverick support multi-turn dialogue, coding assistance, and complex reasoning out of the box, with fewer of the “lost in thought” slowdowns thanks to the MoE speedups.

LLaMA 4’s launch focused on demonstrating strong performance across a spectrum of tasks, often rivaling or surpassing leading proprietary models.

LLaMA 4 Scout Benchmarks

• Scout outperforms Google’s Gemma 3 (27B open model) and Gemini 2.0 Flash-Lite on many standard benchmarks, as well as the open-source Mistral 3.1 models.
• Its massive 10M-token context window unlocks new applications like cross-document analysis and large codebase reasoning that other models cannot handle.
• Despite its modest active size (17B parameters), Scout delivers higher accuracy and throughput than LLaMA 3, making it Meta’s most efficient model in its class.

LLaMA 4 Maverick Benchmarks

• Maverick goes head-to-head with bigger closed models. Meta’s testing shows Maverick outperforms OpenAI’s GPT-4o and Google’s Gemini 2.0 Flash on several benchmarks.
• In experimental chat evaluations (e.g., LLM Arena), Maverick achieved an ELO score of 1417, edging out GPT-4o in head-to-head dialogue quality scores.
• On coding and logical reasoning tasks, Maverick is comparable to the much larger DeepSeek V3 model while using less than half of DeepSeek’s active parameters.

LLaMA 4 Behemoth Results

• The unreleased Behemoth teacher model has achieved state-of-the-art results on challenging math and QA benchmarks (e.g., MATH-500, GPQA-Diamond), outrunning GPT-4.5, Claude 3.7, and Gemini 2.0 Pro in those categories.
• Scout and Maverick, having absorbed much of Behemoth’s knowledge via distillation, also perform exceptionally well on these tests, establishing new highs for open models.

Use Cases Unlocked by LLaMA 4

• Cross-Document Analysis: The extended context window supports analysis and summarization across multiple documents, a capability out of reach for most LLMs.
• Large Codebase Reasoning: Developers can use LLaMA 4 to reason about, refactor, or document large codebases in a single pass.
• Multimodal Applications: Both Scout and Maverick’s ability to process text and images enables use cases in document analysis, visual question answering, and multimodal dialogue.
• Dialogue and Coding Assistance: Both models support multi-turn dialogue, coding assistance, and complex reasoning out of the box.

April 2025 has seen a wave of breakthrough model releases, with Meta’s LLaMA 4 arriving alongside major new offerings from Google, Anthropic, and Mistral. Here’s a technical comparison based on architecture, modalities, training methods, and benchmarks, as highlighted in the original article:

GPT-4.5 (Orion)

• GPT-4.5 as the successor to GPT-4o, with a focus on advancing unsupervised learning rather than solely Chain-of-Thought reasoning.
• It is described as a general-purpose, multimodal model with a parameter count likely exceeding 175 billion.

Google Gemini 2.5 Pro

• Architecture: Dense transformer foundation model.
• Highlights: Introduced as an “AI reasoning model” with state-of-the-art performance across a wide range of benchmarks, especially in coding and math problem-solving.
• Benchmarks: Significantly outperforms prior models (including OpenAI GPT-4.5 baseline) on AIME math tests and GPQA science questions.
• Modalities: Supports multimodal input (vision + text); achieved 81.7% on the MMMU visual reasoning benchmark.
• Unique Feature: Incorporates an “extended thinking” mode for deeper reasoning, similar to chain-of-thought prompting.

Google Gemma 3

• Architecture: 27 B-parameter model (with smaller variants), inherits improvements from Gemini 2.0.
• Highlights: Focused on efficient single-GPU/TPU deployment, with 128K token context and multimodal capabilities (text and image).
• Openness: Open-source, with quantized versions for edge use.
• Benchmarks: Performs on par with older 30B–40B models; supports 140+ languages out of the box.

Mistral 3.1

• Architecture: 24B-parameter “small” LLM, optimized for speed and cost-efficiency.
• Highlights: Runs on consumer GPUs (e.g., RTX 4090) or Mac with 32GB RAM; supports basic image analysis and document processing.
• Benchmarks: Achieves performance on par with older 30B–40B models.
• Unique Feature: Built-in support for function calling and tool use, enabling agentic workflows.
• Openness: Released openly under Apache license, targeting legal and medical domains via fine-tuning.

Anthropic Claude 3.7 “Sonnet”

• Architecture: Industry’s first “hybrid” reasoning model (parameter count undisclosed).
• Highlights: Can dynamically switch between fast and “extended thinking” modes, trading latency for improved reasoning quality.
• Benchmarks: 128K token context window; big improvements on coding and complex problem solving in “thinking” mode.
• Unique Feature: Controllable inference-time chain-of-thought; released alongside Claude Code, a command-line software engineering assistant.

Anthropic Claude 4 Series (Opus 4 & Sonnet 4) (New – May 22, 2025):

• Claude 4 Opus: Positioned as Anthropic’s most intelligent model, excelling at frontier tasks in coding, AI agents, and creative writing. It is intended for the most demanding applications and is priced higher. It has shown state-of-the-art performance on SWE-bench and strong results on MMLU and GPQA.
• Claude 4 Sonnet: An improvement over Sonnet 3.7, particularly in coding, it aims to balance high performance with efficiency for high-volume use cases. It has demonstrated strong performance on the SWE-bench and TAU-bench.
• Both models feature an “extended thinking” mode with thinking summaries, advanced tool use capabilities (with parallel tool use in beta ), and the ability for “computer use” (interacting with GUIs by looking at screens, moving cursors, etc.). They also offer strong visual data extraction and low AI hallucination rates.

LLaMA 4’s Position

• LLaMA 4 Scout maintains its strengths in the open-source domain due to its efficiency and groundbreaking 10-million-token context window, although the practical, widespread availability of this full context was still in progress. Its competitive standing against the newly updated versions of Gemma and Mistral Small requires ongoing evaluation.
• LLaMA 4 Maverick faces intensified competition. New and updated closed models like Google’s Gemini 2.5 Pro (especially with its “Deep Think” mode), Anthropic’s Claude 4 Opus and Sonnet, and potentially OpenAI’s GPT-4.5, present formidable challenges in complex reasoning and coding. While Maverick’s ELO score against the earlier GPT-4o was strong, GPT-4o itself was now part of a broader OpenAI strategy that included the powerful Codex agent.
• LLaMA 4’s native multimodality and MoE architecture remain key differentiators. However, competitors are making rapid advancements in these areas as well. Gemini’s introduction of native audio output, Claude 4’s enhanced computer vision use and agentic capabilities, and Mistral Medium 3’s multimodal features demonstrate that the gap is closing or, in some aspects, surpassed.

So far, 2025 has underscored several key trends in the design and release of large language models:

Multimodality as Standard: Multimodal capabilities are now a baseline expectation for advanced large language models. LLaMA 4, Gemini 2.x, Gemma, and Mistral 3.1 all support image inputs alongside text, enabling richer, more context-aware applications.

Efficient Specialization: There is a clear move toward efficient specialization. LLaMA 4 and DeepSeek leverage Mixture-of-Experts (MoE) architectures to boost performance without simply scaling up parameter counts. Anthropic’s Claude 3.7 introduces “hybrid reasoning,” allowing the model to switch between fast and extended thinking modes dynamically. Similarly, Small Language Models (SLMs) are gaining traction for use cases where fast inference and low computational cost matter more than massive scale, enabling deployment on edge devices or low-resource environments.

Open and Semi-Open Models: Major players like Meta, Google, and Mistral are increasingly releasing open or semi-open models. This trend is expanding access for researchers and organizations, enabling experimentation and innovation at the frontier of AI.

Performance Without Overhead: The latest models are achieving state-of-the-art results not just by increasing size, but through machine learning, architectural, and training innovations that deliver more with less—whether that’s longer context windows, better multimodal integration, or advanced reasoning strategies.

Meta’s LLaMA 4 launch in early 2025 indeed marked a significant moment, delivering impressive multimodal intelligence through an open-model philosophy and pioneering the Mixture-of-Experts architecture at an unprecedented scale within the LLaMA family. Its innovative design and training methodologies enabled it to challenge even much larger proprietary models, substantially narrowing the gap between open research and closed commercial systems. The introduction of variants like Scout, with its massive context window, and Maverick, with its potent reasoning capabilities, offered powerful new tools to researchers and developers.

However, the AI landscape is characterized by an unrelenting, almost ferocious pace of innovation. The developments witnessed in May 2025 alone were nothing short of extraordinary, with LLaMA 4 being quickly joined, and in some aspects potentially surpassed, by a torrent of advancements from every major competitor. Google’s Gemini 2.5 Pro and Flash received substantial upgrades, including the “Deep Think” reasoning mode and native audio capabilities, while Gemma 3n brought powerful multimodal AI to on-device environments.

Mistral AI unleashed a flurry of new models, from the long-context MegaBeam-7B and enterprise-focused Mistral Medium 3 to specialized tools like Codestral and a versatile Agents API. Anthropic launched its Claude 4 generation, with Opus 4 and Sonnet 4 pushing the boundaries of hybrid reasoning, coding, and agentic task completion. OpenAI, too, made significant moves with its reported GPT-4.5 model and the groundbreaking Codex software engineering agent, designed to automate complex development workflows.

What became abundantly clear by mid-2025 is that the “turning point” LLaMA 4 represented is now part of a continuous revolution. The dominant themes emerging are not just about incremental improvements in model scores but fundamental shifts in AI capabilities and interaction paradigms. The explosion of agentic AI (systems that can plan, use tools, and execute tasks) is arguably the most transformative trend, promising to redefine how humans interact with and leverage artificial intelligence.

Multimodality has deepened profoundly, moving beyond text and static images to fluidly incorporate audio, video, and complex interleaved data. Concurrently, innovations in on-device intelligence are making powerful AI more personal, private, and universally accessible.

The pace of AI model innovation has never been faster, and LLaMA 4, alongside its formidable contemporaries, is driving a new chapter in what’s possible with large-scale AI. It’s an exhilarating time to be at this cutting edge, with each passing month seemingly redefining the state of the art and broadening the horizons of AI applications.

At Aisera, we’re committed to harnessing these latest breakthroughs, including innovations like LLaMA 4, to drive intelligent automation and deliver transformative solutions for enterprises worldwide. If you’re interested in how these new models can accelerate your business, book an AI demo or explore the Agentic AI platforms to learn more.