Model Profile: LLaMA 4 Scout (Meta)

Tagline: Meta's next-gen MoE model with an unparalleled 10M token context for massive retrieval tasks.

📊 At a Glance

• Primary Strength: Extremely Large Context Window (10M tokens), Retrieval Augmented Generation (RAG) focus, Multimodality (Text & Vision).

• Performance Profile:

  • Intelligence: 🟡 Medium-High (for its active size)

  • Speed: 🟡 Medium (10M context processing is intensive)

  • Cost: 🟡 Medium (Efficient MoE, but 10M context is costly if fully used)

• Key Differentiator: Massive 10 million token context window, 17B active / 16 experts (~272B total) MoE architecture.

• allmates.ai Recommendation: Specialized for Mates needing to process and "remember" extremely long documents or entire knowledge bases in a single pass, ideal for advanced RAG and deep information synthesis.

📖 Overview

LLaMA 4 Scout, announced by Meta AI in mid-2025 alongside Maverick, is a next-generation, natively multimodal model distinguished by its extraordinary 10 million token context window. It utilizes a Mixture-of-Experts (MoE) architecture with approximately 17 billion active parameters (from 16 experts, totaling ~272B). Scout is optimized for tasks requiring the ingestion and understanding of vast amounts of information, such as retrieval-augmented generation over entire databases or synthesizing insights from years of documentation. While sharing the LLaMA 4 multimodal capabilities, its primary focus is leveraging its massive context.

🛠️ Key Specifications

🔀 Modalities

• Input Supported:

  • Text

  • Images

• Output Generated:

  • Text

⭐ Core Capabilities Assessment

• Reasoning & Problem Solving: ⭐⭐⭐✰✰ (Good)

  • Good reasoning, especially when leveraging its vast context for information.

• Writing & Content Creation: ⭐⭐⭐✰✰ (Good)

  • Can generate coherent text, particularly effective at summarizing or rephrasing large inputs.

• Coding & Development: ⭐⭐⭐✰✰ (Good)

  • Capable of understanding code within its massive context (e.g., entire large repositories).

• Mathematical & Scientific Tasks: ⭐⭐⭐✰✰ (Good)

  • Can process and reason over large scientific documents or datasets.

• Instruction Following: ⭐⭐✰✰✰ (Fair)

  • Follows instructions, especially those related to information retrieval and synthesis from its context.

• Factual Accuracy & Knowledge: ⭐⭐⭐⭐✰ (Very Strong)

  • Excels when information is within its 10M token context; otherwise, relies on its base training.

🚀 Performance & 💰 Cost

• Speed / Latency: Medium to Slower

  • Processing 10M tokens is inherently slow, even with MoE. Best for offline/batch tasks if full context is used.

• Pricing Tier (on allmates.ai): Medium to Premium

  • Efficient MoE, but the cost of processing 10M tokens will be substantial if fully utilized.

✨ Key Features & Strengths

• Unprecedented Context Window: 10 million tokens for ingesting massive amounts of data.

• Specialized for Retrieval: Ideal for tasks requiring understanding of entire knowledge bases.

• Native Multimodality (Vision): Can process images alongside text within its vast context.

• Efficient MoE Architecture: Manages large scale with active parameter efficiency.

• Open Availability (Likely): Part of Meta's LLaMA 4 series.

🎯 Ideal Use Cases on allmates.ai

• Advanced RAG Mates: Processing entire corporate wikis, legal libraries, or research archives in one go to answer questions.

• Deep Document Synthesis: Mates that read and synthesize information from hundreds or thousands of documents simultaneously.

• Large-Scale Codebase Analysis: Mates that can ingest and understand very large software repositories for analysis or refactoring suggestions.

• Forensic Analysis: Processing vast logs or communication records to find specific information.

• "Ask Anything" about Your Entire Dataset: When you need to query a massive, self-contained dataset provided in context.

⚠️ Limitations & Considerations

• Speed for Full Context: Utilizing the full 10M token context will be slow and resource-intensive.

• Cost for Full Context: Token costs for 10M input can be very high.

• General Reasoning vs. Maverick: While good, Maverick is likely superior for general reasoning tasks not dependent on extreme context length.

• Practicality of 10M Tokens: Most interactive use cases may not require or efficiently use such a vast context.

🏷️ Available Versions & Snapshots (on allmates.ai)

• llama-4-scout (or similar, alias pointing to the recommended version)