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const { OpenAI } = require('openai');
const api = new OpenAI({
baseURL: 'https://api.ai.cc/v1',
apiKey: '',
});
const main = async () => {
const result = await api.chat.completions.create({
model: 'qwen3-coder-480b-a35b-instruct',
messages: [
{
role: 'system',
content: 'You are an AI assistant who knows everything.',
},
{
role: 'user',
content: 'Tell me, why is the sky blue?'
}
],
});
const message = result.choices[0].message.content;
console.log(`Assistant: ${message}`);
};
main();
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.ai.cc/v1",
api_key="",
)
response = client.chat.completions.create(
model="qwen3-coder-480b-a35b-instruct",
messages=[
{
"role": "system",
"content": "You are an AI assistant who knows everything.",
},
{
"role": "user",
"content": "Tell me, why is the sky blue?"
},
],
)
message = response.choices[0].message.content
print(f"Assistant: {message}")
-
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Product Detail
Qwen3-Coder is an advanced AI model meticulously engineered for specialized text-to-text coding and programming tasks. Designed for seamless integration, it adeptly manages complex development workflows, powered by an exceptionally large context window of 262K tokens, enabling unparalleled depth in understanding and generating code.
✨ Specifications
Performance Benchmarks
- 🚀 Context Window: A massive 262K tokens, facilitating extensive input and execution of long-horizon tasks without losing context.
- 💡 Task Focus: Primarily optimized for sophisticated text-to-text transformations, excelling in coding instructions, and precise code generation across diverse languages.
- 🔗 Integration Support: Engineered for robust integration, allowing effortless embedding into various development and automation environments.
Performance Metrics Overview
An in-depth evaluation showcases Qwen3-Coder's superior performance across a spectrum of agentic tasks, including intricate coding challenges, efficient browser navigation, and intelligent tool usage. Benchmarked against industry leaders like DeepSeek-V3, as well as proprietary models such as Claude and GPT-4.1, Qwen3-Coder demonstrates remarkable proficiency. Scores from established benchmarks like SWE-bench, WebArena, and BECL-v3 validate its capabilities in problem-solving, accurate code generation, and seamless interaction with external tools, highlighting its consistent strength across specialized and general tasks.
Key Capabilities
- 🛠️ Advanced Coding: Excels in generating, refactoring, and following instructions for sophisticated multi-file codebases, converting complex textual inputs into executable code.
- 🧠 High-Context Reasoning: Leverages its large context window to handle complex sequences and multi-step instructions, supporting extensive and continuous workflows.
- 🔌 Integration Ready: Purpose-built to support various integration frameworks, facilitating automation, API usage, and embedding into larger software ecosystems.
💰 API Pricing
- Input: $1.575 per million tokens
- Output: $7.875 per million tokens
🚀 Optimal Use Cases
- 📊 Large-Scale Code Generation: Exceptionally effective for generating and managing significant volumes of code with deep contextual awareness and precision.
- ⚙️ Software Development Automation: Streamlines processes such as autonomous pull requests, multi-file editing, and comprehensive codebase refactoring.
- 💡 Complex Instruction Execution: Adeptly translates intricate textual commands into executable code and structured programming outputs, enhancing developer productivity.
📝 Code Sample
📈 Comparison with Other Leading Models
Qwen3-Coder stands out in the competitive landscape of AI models, offering distinct advantages tailored for coding excellence:
- Vs. Claude 4 Opus: While Claude 4 Opus features a Mixture-of-Experts architecture with 480 billion parameters (35 billion active) and a 256K token context window (extendable to 1 million), excelling in agentic coding and tool use comparable to Claude 4 Sonnet, Qwen3-Coder provides a specialized focus on deep code understanding and generation with its own robust context handling for complex development tasks.
- Vs. Gemini 2.0: Qwen3-Coder prioritizes advanced agentic coding capabilities, dynamic tool, and browser automation. In contrast, Gemini 2.0 typically focuses more on broad knowledge recall and creative content generation across various modalities.
- Vs. ChatGPT-4.1: Qwen3-Coder is singularly specialized for coding tasks, featuring strong integration with agentic workflows, long-context understanding, and extensive tool automation. ChatGPT-4.1, while powerful, is a more general-purpose model with a static knowledge cutoff and a lesser emphasis on specialized agentic code generation.
⚠️ Limitations & API Integration
While Qwen3-Coder offers exceptional agentic capabilities and robust long-context handling, its sophisticated architecture demands high computational resources and specialized infrastructure for optimal deployment. Like other large-scale agentic coding models, it may still encounter challenges with extremely novel or ambiguous coding tasks. Therefore, integrating human oversight remains beneficial for ensuring safety, correctness, and fine-tuning complex outputs.
API Integration: Qwen3-Coder is seamlessly accessible via the AI/ML API. Comprehensive documentation is readily available for developers.
❓ Frequently Asked Questions (FAQs)
What makes Qwen3-Coder exceptional for coding tasks?
Qwen3-Coder is a massive-scale, coding-specific AI model with 480 billion parameters and advanced architectural optimizations. Its exceptional capabilities stem from specialized training on extensive code repositories, sophisticated understanding of programming patterns, and enhanced instruction-following, making it state-of-the-art in code generation, debugging, and technical problem-solving.
What programming languages and development tasks does Qwen3-Coder handle best?
The model excels across a wide range of programming languages including Python, JavaScript, Java, C++, Go, and Rust. It demonstrates particular strength in complex algorithm implementation, system architecture design, code optimization, refactoring, debugging, API development, and cross-platform compatibility solutions.
How does its large context window benefit coding applications?
The 262K token context window provides significant benefits for coding applications, including deeper understanding of complex code patterns, better context retention across large codebases, more accurate code completion and generation, sophisticated error detection, and enhanced ability to work with multiple files and dependencies, supporting long and multi-step workflows seamlessly.
What are the practical applications for enterprise and professional developers?
Practical applications include enterprise-scale code generation and review, legacy system modernization, complex software architecture design, automated testing, technical documentation generation, code migration, and advanced debugging of sophisticated systems. Its scale makes it ideal for large organizations with complex development needs.
How does Qwen3-Coder compare to other large coding models?
Qwen3-Coder demonstrates competitive advantages in code accuracy, reliability, understanding of complex requirements, handling of enterprise-scale projects, multilingual code support, and seamless integration with development workflows. While requiring substantial computational resources, its performance on complex coding tasks often justifies the investment for high-quality, reliable code generation.
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