Qwen 3 Coder next-generation coding model
Qwen 3 Coder Next delivers state-of-the-art performance for software engineering tasks, optimized for complex reasoning and multi-file context management within a 128,000 token window. Built on the APACHE-2.0 license, this FP16 model integrates seamlessly into existing pipelines via our standard API endpoints, allowing you to execute your first completion request within minutes. Benchmarks indicate superior accuracy in both English and Arabic coding tasks compared to previous iterations, making it ideal for researchers validating multilingual logic flows. The 3.5x credit multiplier reflects its advanced reasoning density, ensuring high-quality output for production-grade applications.
Enterprise teams require reliability and linguistic precision, particularly for Arabic language support in technical documentation and code comments. Qwen 3 Coder Next is production-ready, offering native fluency that meets rigorous regional standards without requiring additional fine-tuning. Transparent pricing structures accommodate various operational budgets, with costs calculated clearly in major regional currencies including KWD, SAR, and AED directly within your dashboard. Whether scaling a startup prototype or deploying enterprise solutions, the Starter tier access ensures immediate availability. Explore our comprehensive guide for detailed benchmark tables and integration snippets to accelerate your deployment workflow today.
from openai import OpenAI
client = OpenAI(
base_url="https://llmapi.resayil.io/v1/",
api_key="YOUR_API_KEY"
)
response = client.chat.completions.create(
model="qwen3-coder-next",
messages=[
{"role": "user", "content": "Hello!"}
]
)
print(response.choices[0].message.content)const response = await fetch(
"https://llmapi.resayil.io/v1/chat/completions",
{
method: "POST",
headers: {
"Content-Type": "application/json",
"Authorization": "Bearer YOUR_API_KEY"
},
body: JSON.stringify({
model: "qwen3-coder-next",
messages: [
{ role: "user", content: "Hello!" }
]
})
}
);
const data = await response.json();
console.log(data.choices[0].message.content);curl https://llmapi.resayil.io/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
"model": "qwen3-coder-next",
"messages": [
{"role": "user", "content": "Hello!"}
]
}'