About
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.
Pricing
| Input | $0.25 | per 1M prompt tokens |
|---|---|---|
| Output | $0.83 | per 1M generated tokens, reasoning included |
What a request costs
| Typical request | Each | Per 1,000 |
|---|---|---|
| Short chat reply1,500 in · 400 out | $0.0007 | $0.708 |
| Question over a long document40,000 in · 800 out | $0.011 | $10.74 |
| Agent tool step20,000 in · 1,200 out | $0.0060 | $6.03 |
At the developer-API rate, before any provider promotion or prompt-cache saving. In the app, the model picker shows what a message costs on your plan before you send it.
Benchmarks
Head-to-head rankings
- #126Data vizModel arenaElo 89639% wins
- #129UI componentsModel arenaElo 76726% wins
- #134Game devModel arenaElo 77627% wins
- #135CodeModel arenaElo 79127% wins
- #141WebsitesModel arenaElo 75223% wins
Scores from Artificial Analysis; bars are scaled to the best model on our list. Rankings from Design Arena head-to-head votes. Published by OpenRouter alongside its catalogue.
Call it from code
OpenAI-compatible: point any OpenAI SDK at https://api.privateer.pro/v1 and set
model. Keys are created in the app under Settings.
cURL
curl https://api.privateer.pro/v1/chat/completions \
-H "Authorization: Bearer $PRIVATEER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "meta-llama/llama-4-scout",
"messages": [{ "role": "user", "content": "Hello" }]
}'Python
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.privateer.pro/v1",
api_key=os.environ["PRIVATEER_API_KEY"],
)
reply = client.chat.completions.create(
model="meta-llama/llama-4-scout",
messages=[{"role": "user", "content": "Hello"}],
)
print(reply.choices[0].message.content)JavaScript
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.privateer.pro/v1",
apiKey: process.env.PRIVATEER_API_KEY,
});
const reply = await client.chat.completions.create({
model: "meta-llama/llama-4-scout",
messages: [{ role: "user", content: "Hello" }],
});
console.log(reply.choices[0].message.content);Parameters this model accepts
frequency_penaltylogit_biasmax_tokensmin_ppresence_penaltyrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_p