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Meta: Llama 4 Scout

meta-llama/llama-4-scout

Released Apr 5, 2025Knowledge cutoff Aug 31, 2024327,680 context$0.08/M input tokens$0.30/M output tokens

Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input (text and image) and multilingual output (text and code) across 12 supported languages. Designed for assistant-style interaction and visual reasoning, Scout uses 16 experts per forward pass and features a context length of 10 million tokens, with a training corpus of ~40 trillion tokens.

Built for high efficiency and local or commercial deployment, Llama 4 Scout incorporates early fusion for seamless modality integration. It is instruction-tuned for use in multilingual chat, captioning, and image understanding tasks. Released under the Llama 4 Community License, it was last trained on data up to August 2024 and launched publicly on April 5, 2025.

Overview
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Performance for Llama 4 Scout

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Effective Pricing for Llama 4 Scout

Actual cost per million tokens across providers over the past hour

Apps using Llama 4 Scout

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Recent activity on Llama 4 Scout

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Uptime stats for Llama 4 Scout

Uptime stats for Llama 4 Scout across all providers

Providers for Llama 4 Scout

OpenRouter routes requests to the best providers that are able to handle your prompt size and parameters, with fallbacks to maximize uptime.

Sample code and API for Llama 4 Scout

OpenRouter normalizes requests and responses across providers for you.

OpenRouter provides an OpenAI-compatible completion API to 400+ models & providers that you can call directly, or using the OpenAI SDK. Additionally, some third-party SDKs are available.

In the examples below, the OpenRouter-specific headers are optional. Setting them allows your app to appear on the OpenRouter leaderboards.

Using third-party SDKs

For information about using third-party SDKs and frameworks with OpenRouter, please see our frameworks documentation.

See the Request docs for all possible fields, and Parameters for explanations of specific sampling parameters.