AI Model OVERVIEW

LLaMA 4 Scout by Meta AI

LLaMA 4 Scout is Meta’s open-weight, natively multimodal AI model, designed for real-world efficiency, advanced reasoning, and massive context understanding. Scout introduces a leap forward in context length and compute efficiency, handling up to 10 million tokens in a single pass. It seamlessly processes text, images, and audio, making it ideal for complex, data-rich tasks in enterprise and research.

Mixture-of-Experts Efficiency

Activates only 17B of 109B total parameters per token, reducing compute without sacrificing performance

Early Fusion Architecture

Enables better alignment between visual and textual inputs, improving visual question answering and grounding

Key Parameters
of LLaMA 4 Scout

Scout represents a major step forward in scalable, high-context, multimodal models. Its balance of performance, flexibility, and hardware accessibility opens new possibilities for large-scale, context-rich AI applications.

Provider

Meta AI

Context Window

10,000,000 tokens

Maximum Output

Not specified

Release Date

April 2025

Multimodal

Yes

Enterprise Use Cases Evaluation

We benchmarked LLaMA 4 Scout against real-world, enterprise-grade scenarios based on anonymized client case studies. Each use case was evaluated using our Automated Agent Evaluation tool.

Correctness

9.0

Formatting

7.0

Consistency

8.0

Sentiment

9.0

Clarity

9.0

coding Use Case

Micro-Refactoring for Codebases

LLaMA 4 Scout handled the Flask API refactoring task well, accurately applying SQLAlchemy, input validation, and modular design. The explanation was clear and well-structured. However, the model missed some details like pagination, JSON encoding, and strict formatting, which slightly impacted consistency.

Strengths Observed

Technical Refactoring Proficiency
Accurately restructures code to apply ORM usage, modular design, and validation libraries

Clarity of Explanation
Presents complex changes in a clear, logical manner, making the output easy to follow

Robust Error Handling
Implements appropriate error handling and exception management using Flask best practices

Strong Sentiment Alignment
Maintains professional tone suitable for technical domains.

Limitations in This Use Case

Formatting Inconsistencies
Occasional misalignment with expected structural formats, particularly around code tagging or output layout

Partial Requirement Fulfillment
Misses secondary instructions like pagination and custom JSON encoding, suggesting gaps in comprehensively following task scopes

LLaMA 4 Scout by Meta AI

Key Parametersof LLaMA 4 Scout

Enterprise Use Cases Evaluation

Micro-Refactoring for Codebases

Ready to Deploy AI Across Your Enterprise?

Key Parameters
of LLaMA 4 Scout