Llama 4 Scout
Meta · Llama 4
Efficiency-focused Llama 4 tier for customizable deployments with tighter compute budgets.
Overview
Freshness note: Model capabilities, deployment options, and licensing terms can change. This profile is a point-in-time snapshot last verified on February 15, 2026.
Llama 4 Scout is an efficiency-oriented open-weights model tier aimed at teams that need customization but with lower serving costs than larger open models. It is useful for private deployment patterns with strict cost limits.
Capabilities
Scout is typically used for structured assistant tasks, summarization, extraction, and moderate reasoning workflows. It performs best when prompts and task domains are well defined.
Technical Details
As an open-weights model, Scout supports flexible serving options, including self-hosted inference and managed provider endpoints. Performance outcomes depend on runtime optimizations and evaluation quality.
Pricing & Access
There is no single universal pricing model because deployment can be self-managed or provider-hosted. Teams should model both compute and operational overhead when comparing against closed API alternatives.
Best Use Cases
Strong fit for internal copilots, domain-specific automation, and budget-constrained environments that still require control over deployment and data boundaries.
Comparisons
Compared with Llama 4 Maverick, Scout usually favors lower cost and throughput over maximum quality. Compared with GPT-5 nano or Gemini 2.5 Flash-Lite, Scout can provide more control but often needs more engineering investment to operate.