Llama 4 Scout 17B
Llama 4 Scout is Meta's efficiency-focused model in the Llama 4 family, built around a 17-billion-parameter architecture that supports an exceptionally large context window reaching up to 10 million tokens. This makes Scout uniquely suited for tasks that require ingesting enormous documents, codebases, or data corpora in a single pass — well beyond what most models can handle.
Meta positions Scout as the practical workhorse of the Llama 4 lineup: fast, relatively compact, and capable enough for a wide range of instruction-following tasks. Its extreme context length is its defining feature, enabling novel use cases in legal document review, long-form book analysis, and large-scale repository search.
Key Features
Context window up to 10 million tokens — among the largest available
17B parameter footprint enabling efficient inference relative to capability
Strong instruction following for general text tasks
Handles full-book or full-codebase ingestion in a single prompt
Open weights available for fine-tuning and self-hosted deployment
Multimodal architecture foundation from Llama 4 generation
Ideal Use Cases
Full-document legal or contract review across very long files
Entire codebase analysis and cross-file refactoring assistance
Long-form research synthesis from large academic corpora
Chatbots with extensive conversation history retained in context
Data extraction across bulk CSV, log, or transcript archives
Example Prompts for Llama 4 Scout 17B
Technical Specifications
| Provider | Meta |
| Category | Text |
| Modality | Text -> Text |
| Context Window | 10M tokens |
Frequently Asked Questions
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