llama-4-scout-instruct - AI Model Guide

Llama 4 Scout Instruct is Meta's instruction-tuned large language model featuring a 17-billion-parameter Mixture-of-Experts (MoE) architecture with 16 active experts. It is natively multimodal, supporting both text and image understanding, and is optimized for high-efficiency inference compared to dense models of similar capability.

Llama 4 Scout Instruct has 17 billion parameters and uses a Mixture-of-Experts architecture with 16 active experts. Because MoE activates only the most relevant expert subnetworks per token, it delivers strong performance while using significantly less compute than a comparable dense 17B model.

Llama 4 Scout Instruct supports a wide range of tasks including long-form content creation, instruction following, code generation and debugging, multilingual text generation, logical reasoning, summarization, and multi-turn conversational AI. Its multimodal architecture also enables image understanding alongside text tasks.

Yes. Llama 4 Scout Instruct is natively multimodal, meaning it was built at the architecture level to process both text and image inputs. This makes it suitable for tasks that require understanding visual content alongside natural language, such as image captioning, visual question answering, and multimodal content workflows.

Llama 4 Scout Instruct was created by Meta, the company behind the open Llama model family. It is available on Craisee through Replicate as a provider, making it accessible for developers, creators, and content teams without requiring custom infrastructure setup.

In Llama 4 Scout Instruct, the Mixture-of-Experts (MoE) architecture routes each input token to only the most relevant subset of expert subnetworks rather than activating all parameters. This selective activation reduces computational cost while maintaining high output quality, enabling faster inference and lower resource usage compared to dense transformer models.

Llama 4 Scout Instruct has been run over 3.7 million times on Craisee, making it one of the platform's most widely used large language models. This high usage reflects its reliability and versatility across creative, professional, and developer-focused AI generation tasks.

Llama 4 Scout Instruct stands out through its combination of a 17B-parameter MoE architecture with 16 experts, native multimodal support, and instruction-tuning optimized for complex multi-step tasks. Compared to dense models, it achieves enterprise-grade language intelligence at lower computational cost, making it efficient for high-volume production workflows.