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Llama 4 Scout Instruct (17Bx16E)Multimodal intelligence. Extreme efficiency.

Try Llama 4 Scout Instruct (17Bx16E)API Documentation

What Makes Scout Different

10M Token Context

Reason Over Massive Documents

Process entire codebases, multi-document summaries, and extensive user histories in single requests.

Mixture-of-Experts

109B Knowledge, 17B Active

Intelligent routing activates only necessary experts, delivering performance with minimal compute.

Native Multimodality

Text and Vision Together

Early fusion architecture processes images and text jointly from first transformer layer for true cross-modal understanding.

Examples

See what Llama 4 Scout Instruct (17Bx16E) can create

Copy any prompt below and try it yourself in the playground.

Code Analysis

“Analyze this Python codebase for performance bottlenecks and suggest optimizations. Focus on database queries and memory allocation patterns.”

Document Summarization

“Summarize the key findings, methodology, and conclusions from these three research papers on machine learning optimization.”

Visual Reasoning

“Examine this architectural floor plan and identify potential accessibility improvements for wheelchair navigation.”

Multi-turn Chat

“Act as a technical advisor. Help debug this TypeScript error, explain the root cause, and provide best practices for similar issues.”

For Developers

A few lines of code.
Multimodal reasoning. Three lines.

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

  • Serverless: scales to zero, scales to millions
  • Pay per token, no minimums
  • Python and JavaScript SDKs, plus REST API
API Documentation 
import requests


response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

FAQ

Common questions about Llama 4 Scout Instruct (17Bx16E)

Read the docs

Llama 4 Scout is a 17 billion parameter multimodal model with mixture-of-experts architecture, delivering performance for text and image understanding. It supports a 10 million token context window, enabling reasoning over vast documents and codebases.

Scout contains 16 specialized expert networks. A routing mechanism directs each token to the most relevant experts, activating only ~17B parameters per inference while accessing the full 109B parameter knowledge base when needed.

Scout was trained from scratch on text, images, and videos together with early fusion. This means the transformer attends to both modalities jointly from the first layers, enabling superior cross-modal understanding compared to bolted-on vision modules.

Llama 4 Scout supports a 10 million token context window, dramatically increased from Llama 3's 128K. This enables processing entire codebases, multi-document analysis, and extensive user activity parsing in single requests.

Scout exceeds comparable models on coding, reasoning, long context, and image benchmarks. It's best-in-class on image grounding and retrieval tasks, with strong performance on needle-in-haystack retrieval across 10M tokens.

Ready to create?

Start generating with Llama 4 Scout Instruct (17Bx16E) on ModelsLab.

Try Llama 4 Scout Instruct (17Bx16E)API Documentation