Llama 4 Scout Instruct (17Bx16E)
Multimodal intelligence. Extreme efficiency.
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
import requestsresponse = requests.post("https://modelslab.com/api/v7/llm/chat/completions",json={"key": "YOUR_API_KEY","prompt": "","model_id": ""})print(response.json())
Ready to create?
Start generating with Llama 4 Scout Instruct (17Bx16E) on ModelsLab.