Answers from the documentation; checking which model is on duty
Hi - I answer from the OpenSmartRoute documentation: routing, the API, plans and quotas, self-hosting. Ask away, or open a support ticket if you need a person.
Grounded in the docs - follow a source before acting on it.
Llama 4 Scout - Model - OpenSmartRoute
Modelv1.0.0
Llama 4 Scout
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...
Reference model card from the OpenRouter listing - model meta-llama/llama-4-scout. Prices are per million tokens as listed; ratings and installs below are from this marketplace.
Llama 4 Scout
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...
Facts
Vendor
Meta
Context window
1,310,720 tokens
Max output
16,384 tokens
Input price
$0.10 / 1M tokens
Output price
$0.30 / 1M tokens
Input
text, image
Output
text
Tool calling
yes
Structured outputs
yes
Reasoning
no
Open weights
meta-llama/Llama-4-Scout-17B-16E-Instruct
Endpoint: https://openrouter.ai/api/v1 (OpenAI-compatible), model meta-llama/llama-4-scout
Use it
Copy one of these into your project. Installing also returns the manifest and these snippets.
# after Install: the listing is in your workspace's routing pool - nothing else to configurecurl-s-X POST https://osr-api.kindrock-caaab1a2.centralindia.azurecontainerapps.io/api/v1/route -H 'Authorization: Bearer $OSR_API_KEY' -H 'Content-Type: application/json' -d '{"text": "...", "plan": true}'# or pin it on the OpenAI-compatible endpoint: {"model": "model-meta-llama-llama-4-scout", ...}
Installed into a catalogue, chosen by a router
Install Llama 4 Scout and it becomes one more candidate the router can pick - when it fits.
A listing is a routing target with a manifest: what it does, which domains it covers, what it costs and who publishes it. Once installed it sits beside your own models and tools, is scored like any other candidate for each request, and shows up in the trace when it wins. Ratings come from workspaces that installed it, one per account.
