About
Qwen-Plus, based on the Qwen2.5 foundation model, is a 131K context model with a balanced performance, speed, and cost combination.
Pricing
| Input | $0.36 | per 1M prompt tokens |
|---|---|---|
| Output | $1.09 | per 1M generated tokens, reasoning included |
| Cached input | $0.07 | per 1M tokens read back from the prompt cache |
| Cache write | $0.45 | per 1M tokens written to the prompt cache |
| Input over 256K | $1.09 | the whole prompt is billed at this rate once it passes the threshold |
| Output over 256K | $3.28 | applies to replies to those prompts |
What a request costs
| Typical request | Each | Per 1,000 |
|---|---|---|
| Short chat reply1,500 in · 400 out | $0.0010 | $0.983 |
| Question over a long document40,000 in · 800 out | $0.015 | $15.43 |
| Agent tool step20,000 in · 1,200 out | $0.0086 | $8.59 |
| Very long prompt (over 256K)320,000 in · 1,500 out | $0.354 | $354.35 |
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.
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": "qwen/qwen-plus",
"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="qwen/qwen-plus",
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: "qwen/qwen-plus",
messages: [{ role: "user", content: "Hello" }],
});
console.log(reply.choices[0].message.content);Parameters this model accepts
frequency_penaltylogprobsmax_tokenspresence_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_logprobstop_p