Qwen3 Coder Next

by Qwen

Zero data retention
qwen/qwen3-coder-next
Input
$0.17/1M tokens
Output
$1.12/1M tokens
Context
262Ktokens
Max output
236Ktokens
Knows to
-
Released
4 Feb 2026

About

Qwen3-Coder-Next is an open-weight causal language model optimized for coding agents and local development workflows.

Pricing

Input$0.17per 1M prompt tokens
Output$1.12per 1M generated tokens, reasoning included
Cached input$0.10per 1M tokens read back from the prompt cache

What a request costs

Typical requestEachPer 1,000
Short chat reply1,500 in · 400 out$0.0007$0.700
Question over a long document40,000 in · 800 out$0.0076$7.62
Agent tool step20,000 in · 1,200 out$0.0047$4.70

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

Intelligence9.2#228 of 285
Coding36.2#113 of 163
Agentic0.9#116 of 127

Scores from Artificial Analysis; bars are scaled to the best model on our list. 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": "qwen/qwen3-coder-next",
    "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/qwen3-coder-next",
    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/qwen3-coder-next",
  messages: [{ role: "user", content: "Hello" }],
});
console.log(reply.choices[0].message.content);

Parameters this model accepts

frequency_penaltylogit_biaslogprobsmax_tokenspresence_penaltyrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_logprobstop_p

API reference → · How API billing works →

Prices are USD at the developer-API rate, as of 16 Oct 2026, rebuilt daily from the providers’ live catalogues. ← All models