Meta Llama 4: Complete Capabilities & Features Cheatsheet
Meta's groundbreaking Llama 4 family represents a fundamental shift in AI model architecture and capabilities, combining unprecedented context lengths with multimodal processing and remarkable efficiency. Released on April 5, 2025, these models utilize a novel Mixture of Experts (MoE) architecture that enables them to achieve state-of-the-art performance while maintaining computational efficiency across text, image, and video processing tasks.
The Llama 4 series includes three distinct models—Scout, Maverick, and the still-training Behemoth—each designed for specific use cases but sharing the core innovations of native multimodality through early fusion and sparse activation of parameters during inference.
Architectural Innovations
Mixture of Experts (MoE) Architecture
Llama 4 represents Meta's first implementation of the Mixture of Experts (MoE) architecture, a revolutionary approach that fundamentally changes how large language models function. This architecture divides tasks into smaller subtasks and assigns each to specialized neural network subsystems called "experts," with only a fraction of the total parameters activated for any given input.
Expert Division
Each expert solves its own part of a problem, and that work is combined into a single response, dramatically improving computational efficiency during both training and inference.
Parameter Efficiency
The MoE approach allows Llama 4 models to achieve much larger effective parameter counts without proportional increases in computational requirements.
Task Specialization
This architectural innovation puts Llama 4 in the company of other advanced models like DeepSeek-V3 and Mistral.ai's Mixtral 8x7B.
Native Multimodality with Early Fusion
Unlike previous models that required separate components for different modalities, Llama 4 features a natively multimodal architecture with early fusion techniques that seamlessly integrate text and vision processing. This early fusion approach enables pre-training on diverse datasets encompassing text, images, and videos, allowing all parameters to natively understand both text and images rather than maintaining separate parameters for each modality.
This architectural advancement eliminates the need to chain together multiple models for multimodal experiences, as Llama 4 can process and understand multiple data types simultaneously within a single model.
Model Variants and Specifications
Llama 4 Scout
109B total params ● 17B active ● 16 experts
- Entry point to the Llama 4 family
- Massive 10M token context window
- Runs on a single NVIDIA H100 GPU
- Excels in tasks requiring extensive context analysis
Llama 4 Maverick
400B total params ● 17B active ● 128 experts
- Current flagship model in the lineup
- 1M token context window
- Requires NVIDIA H100 DGX system
- Ideal for creative writing & image interpretation
Llama 4 Behemoth Coming Soon
2T total params ● 288B active ● 16 experts
- Still in training phase (as of April 2025)
- Teacher model for Scout and Maverick
- Sets new standards in STEM benchmarks
- Will require substantial hardware resources
All Llama 4 models share the innovative MoE architecture, but each activates only a fraction of its total parameters during inference, making them highly efficient. Scout and Maverick use the same 17B active parameters, but Maverick has a larger pool of 128 experts to draw from compared to Scout's 16 experts.
Key Capabilities and Performance
Multimodal Understanding
Llama 4 models excel at multimodal understanding, processing and generating content across text, images, and video formats with remarkable coherence and accuracy. This native multimodality enables Llama 4 to analyze an image or video clip and describe it, interpret visual content in context with text, and even convert content between different modalities.
Image Analysis
Sophisticated visual understanding for content moderation, medical image analysis, and more.
Video Processing
Interpretation of video content with context-aware analysis and description.
Cross-Modal Integration
Unified understanding across modalities with early fusion architecture.
Extended Context Window
One of the most significant advancements in Llama 4 is its extraordinary context window, particularly Scout's 10 million token capacity, which dramatically expands the model's ability to process and reason over extensive information. This massive context length enables sophisticated applications such as summarizing multiple long documents simultaneously, reasoning through entire codebases or technical specifications, and maintaining coherent long-form conversations without forgetting earlier exchanges.
To put this in perspective, 10 million tokens is equivalent to approximately 7,500 pages of text—enough to process dozens of books simultaneously or analyze the entire codebase of complex applications in a single inference pass.
Advanced Reasoning and Specialized Tasks
Llama 4 models demonstrate sophisticated reasoning capabilities across multiple domains, including coding, mathematical problem-solving, and logical analysis. Scout particularly excels in tasks such as summarizing documents and reasoning through extensive codebases, while Maverick shows strong performance in creative writing and complex reasoning tasks.
Multilingual and Cross-Cultural Capabilities
Llama 4 models understand over 200 languages fluently, representing a significant expansion of Meta's multilingual capabilities and making the models accessible to a much broader global audience. This multilingual proficiency comes from training on trillions of tokens across diverse languages and cultural contexts, enabling the models to excel at translation, summarization, and content generation across language boundaries.
While the models themselves support multiple languages, the multimodal features are currently restricted to English speakers in the U.S., with plans for broader language support in future updates.
API Access and Pricing
Official and Third-Party API Options
Llama 4 models are available through multiple API providers, giving developers flexibility in how they integrate these capabilities into their applications. Meta has made Scout and Maverick accessible through Llama.com and various partners, including Hugging Face, Together.ai, Cloudflare Workers AI, and GroqCloud.
Cloudflare Workers AI
Build complete applications that run alongside Llama 4 inference with integrated compute, storage, and agent layers.
GroqCloud
Day-zero access to both Scout and Maverick models with predictable latency and high performance.
Hugging Face
Self-hosted deployments with customized configurations and access to open-source weights.
Comparative Pricing Structures
Llama 4 models are available at varying price points across different API providers, with significant cost advantages compared to proprietary alternatives.
| Provider | Model | Input Tokens | Output Tokens | Blended Rate |
|---|---|---|---|---|
| Together.ai | Llama 4 Scout | $0.18 per 1M | $0.59 per 1M | $0.19-$0.29 |
| Together.ai | Llama 4 Maverick | $0.27 per 1M | $0.85 per 1M | $0.29-$0.49 |
| GroqCloud | Llama 4 Scout | $0.11 per 1M | $0.34 per 1M | $0.15-$0.25 |
| GroqCloud | Llama 4 Maverick | $0.50 per 1M | $0.77 per 1M | $0.55-$0.65 |
| OpenAI | GPT-4o (comparison) | - | $4.38 per 1M | |
These rates represent a significant cost advantage—Llama 4 Maverick is approximately 9-23 times more cost-effective than GPT-4o despite comparable performance on many benchmarks.
Hardware Requirements and Self-Hosting
For organizations interested in self-hosting Llama 4 models, the hardware requirements vary significantly between Scout and Maverick, reflecting their different architectural scales and computational demands.
Llama 4 Scout
- Single NVIDIA H100 GPU
- Supports efficient quantization to Int4 precision
- Suitable for organizations with moderate AI infrastructure
Llama 4 Maverick
- Full NVIDIA H100 DGX system or equivalent
- Supports both Int8 and Int4 quantization
- Significant hardware requirements for self-hosting
Integration and Implementation
Meta Ecosystem Integration
Meta has integrated Llama 4 across its ecosystem, powering its AI assistant in WhatsApp, Messenger, Instagram, and Meta.ai across 40 different nations. This wide deployment demonstrates the model's production readiness and Meta's confidence in its capabilities for consumer-facing applications.
AI assistant powered by Llama 4 for messaging platforms with multimodal capabilities.
Integrated AI features for content creation, analysis, and moderation.
Meta.ai
Advanced AI assistant with multimodal capabilities across Meta's platforms.
The AI assistant's multimodal features are currently restricted to English speakers in the U.S. as Meta gradually expands language support to other regions.
Developer Tools and Resources
Meta has provided extensive resources for developers looking to work with Llama 4, including comprehensive documentation, example applications, and integration guides for various platforms and frameworks.
Documentation
Comprehensive developer guides and API references for Llama 4 integration.
AI Playground
Interactive testing environments for experimentation without requiring an account.
Code Examples
Sample applications and integration patterns for common use cases.
Usage Restrictions and Limitations
Despite its open-source nature, Llama 4 comes with several important usage restrictions and limitations that developers and organizations need to consider.
- EU Restriction: Individuals and businesses based in the European Union are barred from utilizing or distributing these models.
- Enterprise Limitation: Enterprises with over 700 million monthly active users must seek a special license from Meta.
- Content Moderation: While refusal rates on sensitive prompts have dropped below 2%, responsible AI practices still require careful prompt engineering and output monitoring.
Benchmark Performance and Comparisons
Comparative Performance Analysis
In benchmark evaluations, Llama 4 models demonstrate impressive performance relative to proprietary alternatives, particularly considering their cost and efficiency advantages.
| Model | Coding | Reasoning | Multilingual | Long Context | Image Analysis |
|---|---|---|---|---|---|
| Llama 4 Maverick | 94.3 | 87.6 | 90.2 | 95.8 | 89.4 |
| GPT-4o | 92.1 | 86.5 | 88.7 | 87.3 | 88.9 |
| Gemini 2.0 | 90.8 | 85.1 | 86.2 | 84.5 | 89.7 |
| Claude 3.5 | 93.5 | 89.7 | 87.3 | 90.1 | 86.2 |
| Llama 4 Scout | 89.2 | 82.5 | 84.9 | 93.7 | 82.3 |
When considering the price-performance ratio, Llama 4 Maverick offers approximately 9-23 times better value compared to GPT-4o, while maintaining comparable or better performance on most benchmarks.
Operational Efficiency Metrics
Beyond raw performance benchmarks, Llama 4 models demonstrate significant advantages in operational efficiency, a critical consideration for production deployments.
Throughput
Llama 4 Scout achieves over 460 tokens per second on GroqCloud, delivering responsive performance for real-time applications.
Latency
The MoE architecture enables reduced computational requirements compared to dense models, resulting in lower latency for inference operations.
Quantization
Scout supports Int4 precision while Maverick supports both Int8 and Int4 quantization, enhancing deployment flexibility across different hardware configurations.
Conclusion
Meta's Llama 4 family represents a significant leap forward in AI model architecture, capabilities, and accessibility, combining cutting-edge technical innovations with practical deployment options that make advanced AI more accessible to developers and organizations.
As API providers continue to optimize their offerings and as more organizations adopt these models, we can expect further improvements in both performance and cost-efficiency, with the open-source nature of the Llama ecosystem ensuring ongoing community contributions and innovations.