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Qwen3 Coder Next

Qwen · Text · Context 256K · max output 256K tokens

qwen3-coder-next Try

Pricing

Per 1 million tokens
MarkasAIVendor list price
InputRp 4.587Rp 5.367
OutputRp 22.930Rp 26.835
CacheRp 4.587
Cache writeRp 4.587

MarkasAI sale prices in Rupiah, read from the same catalog as GET /v1/models. Prices can change and apply to later requests.

Specifications

Model ID
qwen3-coder-next
Provider
Qwen
Type
Text
Endpoint
POST /v1/chat/completions
Context window
256,000 tokens
Output limit per request
256,000 tokens

About this model

Qwen3-Coder-Next is an open-weight causal language model optimized for coding agents and local development workflows. It uses a sparse MoE design with 80B total parameters and only 3B activated per...

Quoted from the provider's public catalog, in English.

Call example

curl https://api.markas.ai.id/v1/chat/completions \
  -H "Authorization: Bearer $MARKASAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3-coder-next",
    "messages": [{"role": "user", "content": "What is an API?"}],
    "max_tokens": 300
  }'
import os
from openai import OpenAI

client = OpenAI(base_url="https://api.markas.ai.id/v1", api_key=os.environ["MARKASAI_API_KEY"])

resp = client.chat.completions.create(
    model="qwen3-coder-next",
    messages=[{"role": "user", "content": "What is an API?"}],
    max_tokens=300,
)
print(resp.choices[0].message.content)
import OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.markas.ai.id/v1", apiKey: process.env.MARKASAI_API_KEY });

const resp = await client.chat.completions.create({
  model: "qwen3-coder-next",
  messages: [{ role: "user", content: "What is an API?" }],
  max_tokens: 300,
});
console.log(resp.choices[0].message.content);

Keep your API key in the MARKASAI_API_KEY environment variable. Read the endpoint docs

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