---
title: Llama 4 Scout Instruct 17Bx16E — Multimodal LLM | Model...
description: Generate text and images with Llama 4 Scout Instruct. 10M token context, native multimodality, mixture-of-experts efficiency. Try it now.
url: https://modelslab.com/llama-4-scout-instruct-17bx16e
canonical: https://modelslab.com/llama-4-scout-instruct-17bx16e
type: website
component: Seo/ModelPage
generated_at: 2026-04-15T02:05:30.975495Z
---

Available now on ModelsLab · Language Model

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

[Try Llama 4 Scout Instruct (17Bx16E)](/models/meta/meta-llama-Llama-4-Scout-17B-16E-Instruct) [API Documentation](https://docs.modelslab.com)

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](/models/meta/meta-llama-Llama-4-Scout-17B-16E-Instruct).

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 ](https://docs.modelslab.com)

PythonJavaScriptcURL

Copy

```
<code>import requests

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

FAQ

Common questions about Llama 4 Scout Instruct (17Bx16E)
---

[Read the docs ](https://docs.modelslab.com)

### What is Llama 4 Scout Instruct (17Bx16E)?

### How does the mixture-of-experts architecture work?

### How does native multimodality differ from other approaches?

### What is the context window size?

### How does Scout perform on benchmarks?

Ready to create?
---

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

[Try Llama 4 Scout Instruct (17Bx16E)](/models/meta/meta-llama-Llama-4-Scout-17B-16E-Instruct) [API Documentation](https://docs.modelslab.com)

---

*This markdown version is optimized for AI agents and LLMs.*

**Links:**
- [Website](https://modelslab.com)
- [API Documentation](https://docs.modelslab.com)
- [Blog](https://modelslab.com/blog)

---
*Generated by ModelsLab - 2026-04-15*