Llama 4 Scout 17B 16E Instruct
Llama 4 Scout 17B 16E Instruct is a natively multimodal Mixture of Experts (MoE) model with a context window of 131.1K tokens, purpose-built for processing entire codebases, multi-document corpora, and extended user activity logs in a single inference call.
import { streamText } from 'ai'
const result = streamText({ model: 'meta/llama-4-scout', prompt: 'Why is the sky blue?'})Frequently Asked Questions
How large is the context window of 131.1K tokens in practical terms?
Approximately 7.5 million words. That's roughly 25 full-length novels, a multi-year document archive, or a large enterprise codebase with source files, tests, and documentation all loaded simultaneously.
What is the iRoPE architecture and why does it matter for long context?
iRoPE stands for interleaved Rotary Position Embeddings. Most attention layers use standard RoPE, but some layers use no positional embeddings. Inference-time temperature scaling of attention further enhances length generalization. This combination lets the model generalize beyond its training context length.
How does Llama 4 Scout 17B 16E Instruct handle multi-image inputs?
Llama 4 Scout 17B 16E Instruct supports up to eight images per request. It also supports image grounding, aligning natural language prompts with specific regions or objects in images.
Is Llama 4 Scout 17B 16E Instruct suited for RAG, or does the context of 131.1K tokens replace it?
For applications where the full corpus fits within 131.1K tokens, loading everything into context can be more accurate than retrieval augmentation because it avoids retrieval errors and fragmentation. For larger corpora, RAG remains appropriate, but Llama 4 Scout 17B 16E Instruct can handle much larger retrieval chunks or multiple retrieved documents simultaneously.
How does Llama 4 Scout 17B 16E Instruct differ from Maverick? They have the same active parameter count.
Both have 17B active parameters but differ in expert count and total parameters. Llama 4 Scout 17B 16E Instruct has 16 experts and 109B total; Maverick has 128 experts and 400B total. Maverick stores more knowledge in its larger parameter budget. Llama 4 Scout 17B 16E Instruct is leaner but specialized for extreme context length. Meta designates Maverick as the general-purpose product model and Llama 4 Scout 17B 16E Instruct as the long-context specialist.
What languages does Llama 4 Scout 17B 16E Instruct support?
Like all Llama 4 models, Llama 4 Scout 17B 16E Instruct supports 200 languages with over 100 having more than 1 billion tokens each, 10x more multilingual coverage than Llama 3.