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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: 'alibaba/qwen3-vl-plus',
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="alibaba/qwen3-vl-plus",
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
💡 Unveiling Qwen3 VL Plus: A Multimodal Powerhouse
Qwen3 VL Plus represents the third generation of the advanced Qwen series, meticulously engineered for a deep integration of text and image understanding. This state-of-the-art multimodal model excels across diverse applications, from visual question answering and comprehensive scene description to robust object recognition and sophisticated OCR text reading. Its unparalleled reasoning capabilities, based on complex visual inputs, position it as an ideal solution for advanced analytics, intuitive dialog assistants, and a wide array of visual scenarios.
🔧 Technical Specifications
- ⚙ Architecture: Featuring both Dense and Mixture-of-Experts (MoE) variants, available in Instruct and Thinking editions for versatile deployment.
- 📚 Context Length: Native support for an expansive 262,144K tokens, enabling processing of extremely long inputs.
- 🖼️ Multimodal Inputs: Seamlessly processes Text, Images, and Video, with enhanced spatial and temporal reasoning.
- 📜 Advanced OCR Support: Robust recognition across 32 languages, even under challenging conditions like low light, blur, and tilt.
- 🔗 Enhanced Image-Text Alignment: Powered by the DeepStack feature fusion for capturing fine-grained details and sharper multimodal correspondence.
🏆 Performance Benchmarks
- 🌐 Global Leadership: Holds a leading position in global multimodal benchmarks, consistently outperforming competitors like Gemini 2.5 Flash and Claude Sonnet 4.5.
- 🚀 State-of-the-Art Results: Demonstrates superior performance in visual question answering, object detection, and video understanding tasks.
- 🎓 Competitive Edge: Achieves competitive or superior scores on multimodal reasoning and perception tests against proprietary baselines.
🔑 Key Features
- 👁 Superior Visual Perception: Supports complex scene interpretation, spatial reasoning, and advanced 3D grounding.
- 📌 Seamless Text-Vision Fusion: Enables lossless understanding and generation of multimodal content.
- 📜 Advanced OCR Capabilities: Capable of detecting rare and specialized characters across various languages.
- 📺 Long Context & Video Comprehension: Supports multi-hour content analysis with high recall accuracy.
- 🧠 Multimodal Reasoning: Enhanced for challenging tasks in STEM, mathematics, and logical causal analysis.
- 💻 Visual Agent Functionality: Allows programmatic operation of graphical interfaces and invocation of external tools.
💰 Qwen3 VL Plus API Pricing
- Input: $0.21 per 1M tokens
- Output: $1.68 per 1M tokens
🔍 Real-World Use Cases
- Interactive AI: Visual question answering and dialog systems integrating text and image inputs.
- Analytics & Surveillance: Precise scene recognition and description for advanced analytics and monitoring applications.
- Document Processing: Robust OCR and document parsing across multiple languages and challenging imaging conditions.
- Education & Research: Multimodal reasoning tasks in education, scientific research, and technical domains like STEM.
- Automated Operations: Automated UI operations and complex task execution in PC and mobile environments.
💻 Code Sample
<snippet data-name="open-ai.chat-completion" data-model="alibaba/qwen3-vl-plus"></snippet>📈 Qwen3 VL Plus: A Comparative Edge
vs Gemini 2.5 Flash: Qwen3 VL Plus outperforms Gemini 2.5 Flash on key perception benchmarks and offers broader language and OCR support.
vs Claude Sonnet 4.5: Qwen3-VL-Plus achieves superior visual question answering accuracy and better video temporal localization capabilities.
vs Qwen3 32B: Qwen3 VL Plus provides enhanced multimodal reasoning and substantially longer context windows for complex tasks.
vs Claude Opus 4.1: Claude Opus 4.1 is priced significantly higher (30x-60x) and optimized for conservative multi-file software engineering workflows. In contrast, Qwen3-VL-Plus offers superior visual question answering, scene analysis, and long video reasoning, making it more versatile for multimodal analytic and dialog assistant scenarios.
📝 Frequently Asked Questions (FAQ)
Q: What makes Qwen3 VL Plus a state-of-the-art multimodal model?
A: It integrates deep understanding of both text and images with advanced reasoning capabilities, excelling in tasks like visual question answering, OCR, and video comprehension, powered by its Dense/MoE architecture and 262K token context length.
Q: How does Qwen3 VL Plus handle complex visual inputs like videos and challenging OCR scenarios?
A: With enhanced spatial & temporal reasoning for video and robust OCR support for 32 languages, it performs exceptionally well even in low light, blur, or tilt conditions, thanks to its DeepStack feature fusion.
Q: What are the primary use cases for the Qwen3 VL Plus API?
A: Its versatility makes it ideal for visual question answering, scene recognition for analytics, advanced document parsing, multimodal reasoning in STEM, and automated UI operations in various environments.
Q: How does the pricing of Qwen3 VL Plus compare to its performance?
A: Priced at $0.21 per 1M input tokens and $1.68 per 1M output tokens, it offers a highly competitive rate for its leading multimodal capabilities and superior performance across global benchmarks.
Q: Can Qwen3 VL Plus be used for technical and scientific analysis?
A: Absolutely. Its multimodal reasoning is specifically enhanced for STEM, math, and logical causal analysis tasks, making it a powerful tool for research and technical domains.
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