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?'})What To Consider When Choosing a Provider
Zero Data Retention
AI Gateway supports Zero Data Retention for this model via direct gateway requests (BYOK is not included). To configure this, check the documentation.Authentication
AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.
Scout's long-context capabilities introduce pricing considerations: longer prompts raise per-request costs substantially. Compare $0.17 and $0.66 against your expected context length.
When to Use Llama 4 Scout 17B 16E Instruct
Best For
Entire codebase processing:
Architecture review, cross-file refactoring suggestions, and comprehensive code search in a single inference call
Multi-document analysis:
Legal discovery across large contract sets or literature review across a research corpus where chunking loses coherence
Long-session personalization:
Parsing extensive user history or activity logs without summarization loss
Image grounding applications:
Precise visual localization across multi-image inputs
Consider Alternatives When
Standard context sufficient:
Maximum multimodal capability within a standard context window is more important, so Maverick's 128-expert architecture offers greater image and text depth
General assistant workload:
Maverick is Meta's designated product workhorse
Modest context tasks:
A smaller, cheaper model such as Llama 3.3 70B would satisfy quality requirements
Cost concerns at scale:
131.1K tokens inputs result in substantially higher per-request costs than typical short-context usage
Conclusion
Llama 4 Scout 17B 16E Instruct extends what open-weight models can handle for long-context applications. The combination of a window of 131.1K tokens and native multimodality suits it for codebase-scale reasoning, multi-document analysis, and long-session personalization tasks that were previously impractical without chunking or retrieval augmentation. Its iRoPE architecture makes it the long-context specialist within the Llama 4 generation.
FAQ
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.
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.
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.
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.
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.
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.
