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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: 'qwen3-235b-a22b-thinking-2507',
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();
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: 'qwen3-235b-a22b-thinking-2507',
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();
-
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Product Detail
Unveiling Qwen3-Thinking: A Powerful AI for Complex Tasks
Qwen3-Thinking stands as a state-of-the-art text-to-text AI model, meticulously engineered for exceptional performance in complex reasoning, diverse multilingual tasks, and extensive large-context processing. Leveraging Alibaba Cloud’s robust infrastructure, this model is specifically optimized to navigate intricate workflows demanding profound analytical capabilities and intelligent decision-making.
Technical Specifications & Performance
🔧 Core Benchmarks:
- Context Window: 131K tokens — empowering deep understanding of extensive content.
- Tasks: Text-to-text generation — versatile for a myriad of applications.
- Architecture: Transformer-based, featuring 235-billion parameters for unparalleled intelligence.
📈 Enhanced Performance Metrics:
Qwen3-Thinking delivers substantial improvements in reasoning capabilities, achieving state-of-the-art results across critical domains such as logic, mathematics, and coding. This iteration also showcases superior general abilities, including advanced instruction following and high-quality text generation. Its refined long-context understanding and expanded "thinking length" make it the optimal choice for highly intricate reasoning tasks.
🔎 Key Capabilities: Driving Innovation
- Complex Reasoning: Solves multi-step logical challenges in mathematics, science, and analytics with exceptional precision.
- Multilingual Proficiency: Offers fluent comprehension and generation across 119 languages and dialects, including challenging low-resource variants.
- Large-Context Processing: Capable of analyzing documents up to 131K tokens for efficient summarization, knowledge extraction, and comprehensive document synthesis.
- Tool Integration: Seamlessly supports advanced function calling and structured JSON output for sophisticated automation.
💰 API Pricing Structure:
- Input: $0.2415 per million tokens
- Output: $2.415 per million tokens
💡 Optimal Use Cases: Where Qwen3-Thinking Shines
- Scientific Research: Accelerating processing of research papers, complex data interpretation, and rigorous hypothesis testing.
- Multilingual Applications: Facilitating advanced translation, cross-language content generation, and precise localization efforts.
- Enterprise Analytics: Extracting critical insights from vast volumes of technical reports, legal contracts, or complex regulatory documents.
- Education: Powering sophisticated tutoring systems for subjects like mathematics, physics, and advanced programming.
💻 Code Sample
💬 Comparison with Other Leading Models:
Vs. Claude 4 Opus
Qwen3-Thinking prioritizes high precision in complex tasks with an impressive 256K token context window (expandable). In contrast, Claude 4 Opus excels in coding accuracy and API automation, offering a 200K token context and a leading 72.5% SWE-bench score, ideal for stable analytical and generative tasks.
Vs. Gemini 2.5 Flash
While Qwen3-Thinking distinguishes itself with superior long-context support and advanced agentic workflows, Gemini 2.5 Flash is optimized for speed and cost-efficiency, featuring a 128K token context and a 63.8% SWE-bench result.
Vs. OpenAI o3-mini
Qwen3-Thinking focuses on accelerating agentic workflows and intelligent tool usage. In contrast, OpenAI o3-mini effectively handles general-purpose tasks, supports a 128K token context, and achieves 69.1% on SWE-bench, aiming for broader applications without deep agentic integration.
⚠ Limitations: Important Considerations
Although Qwen3-Thinking offers outstanding capabilities, particularly in long-context processing and agentic task execution, its deployment necessitates significant computational resources and specialized infrastructure. Like other large models, it may encounter challenges with highly novel or ambiguous tasks, benefiting greatly from human involvement for quality control, safety, and result verification. The model's inherent complexity can also lead to increased operational costs.
ⓘ Frequently Asked Questions (FAQ)
Q1: What is Qwen3-Thinking designed for?
Qwen3-Thinking is an advanced text-to-text AI model optimized for complex reasoning, multilingual tasks, and large-context processing, excelling in intricate workflows requiring deep analytical capabilities.
Q2: What is the maximum context window Qwen3-Thinking can handle?
Qwen3-Thinking supports a large context window of up to 131K tokens, enabling it to analyze extensive documents for summarization, knowledge extraction, and synthesis.
Q3: How does Qwen3-Thinking perform in multilingual scenarios?
The model boasts high multilingual proficiency, fluent in 119 languages and dialects, including various low-resource dialects, making it highly versatile for global applications.
Q4: What are the primary advantages of using Qwen3-Thinking for complex reasoning tasks?
It offers significant improvements in reasoning capabilities across logic, math, and coding, combined with enhanced long-context understanding, making it ideal for highly complex multi-step problems and analytical tasks.
Q5: What are the main limitations of Qwen3-Thinking?
Its primary limitations include requiring significant computational resources and specialized infrastructure, potential challenges with extremely novel tasks, and higher operational costs due to its complexity and scale.
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