Skip to content
No models found
OpenRouter
© 2026 OpenRouter, Inc

Product

  • Chat
  • Rankings
  • Apps
  • Models
  • Providers
  • Pricing
  • Enterprise
  • Labs

Company

  • About
  • Announcements
  • CareersHiring
  • Privacy
  • Terms of Service
  • Support
  • State of AI
  • Works With OR
  • Data

Developer

  • Documentation
  • API Reference
  • SDK
  • Status

Connect

  • Discord
  • GitHub
  • LinkedIn
  • X
  • YouTube
Favicon for meta-llama

Meta: Llama 4 Scout

meta-llama/llama-4-scout

Compare

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.

Modalities

Input Price

$0.08per 1M

Output Price

$0.30per 1M

Context

10M

Weekly Tokens

13.8B

Released

Apr 5, 2025

Overview
Providers
Performance
Pricing
Benchmarks
Apps
Activity
Uptime
API

Sample code and API for Llama 4 Scout

OpenRouter normalizes requests and responses across providers for you.

1

Get your API key

Create an API key from your OpenRouter dashboard and set it as an environment variable:

2

Make your first request

Use meta-llama/llama-4-scout with the OpenRouter API:

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.

3

Enable streaming

Add "stream": true to your request body to receive responses as server-sent events:

Endpoint

POSThttps://openrouter.ai/api/v1/chat/completions
AuthorizationBearer $OPENROUTER_API_KEY
Content-Typeapplication/json
HTTP-Refereroptional — your site URL, for rankings
X-Titleoptional — your site name, for rankings
Modelmeta-llama/llama-4-scout

Parameters

NameTypeDefaultDescription
max_tokensinteger—This sets the upper limit for the number of tokens the model can generate in response.
temperaturefloat1This setting influences the variety in the model's responses.
top_pfloat1This setting limits the model's choices to a percentage of likely tokens: only the top tokens whose probabilities add up to P.
stoparray—Stop generation immediately if the model encounter any token specified in the stop array.
frequency_penaltyfloat0This setting aims to control the repetition of tokens based on how often they appear in the input.
presence_penaltyfloat0Adjusts how often the model repeats specific tokens already used in the input.
repetition_penaltyfloat1Helps to reduce the repetition of tokens from the input.
top_kinteger0This limits the model's choice of tokens at each step, making it choose from a smaller set.
seedinteger—If specified, the inferencing will sample deterministically, such that repeated requests with the same seed and parameters should return the same result.
min_pfloat0Represents the minimum probability for a token to be considered, relative to the probability of the most likely token.
response_formatmap—Forces the model to produce specific output format.
logit_biasmap—Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100.
structured_outputsboolean—If the model can return structured outputs using response_format json_schema.