Overview

Qwen 3 Coder Next represents a significant leap in automated software engineering, designed specifically for complex code generation and refactoring tasks. With a massive 128,000 token context window, this model maintains coherence across entire repositories, allowing developers to submit full file structures for analysis without truncation. Running on FP16 quantization, it delivers high-precision outputs suitable for production pipelines while maintaining efficient inference speeds. Integration is streamlined for immediate use, enabling API builders to execute their first successful call within minutes using standard endpoints. The Apache 2.0 license ensures flexibility for commercial deployment, removing legal barriers often associated with proprietary weights.

For research teams and enterprise decision-makers, Qwen 3 Coder Next offers a balanced ratio of performance to operational cost. The model carries a 3.5x credit multiplier relative to the base rate, providing premium capabilities accessible from the starter tier. It demonstrates robust bilingual proficiency, handling technical documentation and code comments in both Arabic and English with high accuracy, which is critical for diverse development teams. Benchmark evaluations indicate superior performance in logic synthesis and debugging compared to previous generations, making it a reliable choice for scaling AI-assisted workflows. This combination of extended context, licensing freedom, and multilingual support establishes a solid foundation for building next-generation developer tools.

Specifications

Display Name Qwen 3 Coder Next

Family Qwen

Category Code

Parameters unknown

Context Window 128,000 tokens

Quantization FP16

License APACHE-2.0

Min Tier Starter

Status Available

Pricing

3.5×

credits per token

1K 3,500 Credits

10K 35,000 Credits

100K 350,000 Credits

View Pricing Plans

Code Examples

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!"}
    ]
  }'

Use Cases

Automated code generation for modern web applications

Refactoring legacy systems into modern cloud architectures

Debugging complex errors across multiple source files

Generating comprehensive unit tests for specific functions

Analyzing large codebases within extended context window

In-Depth Guide

Full Guide

Complete Guide to Qwen 3 Coder Next — LLM Resayil

Qwen 3 Coder Next