Qwen AI API

API ID 13018

Alibaba Qwen LLM API for text generation, reasoning, and vision analysis. OpenAI-compatible chat completions with up to 1M token context window.

1,544 ms avg response

API Documentation

Endpoints

Request

List Models API

Retrieve a list of all available Qwen models with their specifications.

  • Model IDs and names
  • Context window sizes
  • Input/output capabilities
Endpoint ID: 26130
GET https://api-sneakers-database.zylalabs.com/api/13018/qwen+ai+api/26130/list+available+models
INPUT PARAMETERS

Free test requests remaining: 3 of 3.

This endpoint does not require any input parameters.

API EXAMPLE RESPONSE
JSON
{
  "object": "list",
  "data": [
    {
      "id": "qwen-turbo",
      "object": "model",
      "created": 1699999999,
      "owned_by": "alibaba"
    },
    {
      "id": "qwen-plus",
      "object": "model",
      "created": 1699999999,
      "owned_by": "alibaba"
    },
    {
      "id": "qwen-max",
      "object": "model",
      "created": 1699999999,
      "owned_by": "alibaba"
    }
  ]
}
List Available Models — CODE SNIPPETS

curl --location --request GET 'https://zylalabs.com/api/13018/qwen+ai+api/26130/list+available+models' --header 'Authorization: Bearer YOUR_API_KEY' 


    
Request

The "qwen-chat-completions" endpoint is designed to facilitate dynamic conversational AI interactions by generating contextual and coherent chat responses based on user input. By processing a prompt containing user queries or statements, this endpoint leverages advanced natural language processing techniques to return a well-structured completion that maintains the flow of conversation, making it suitable for applications such as customer support bots, virtual assistants, and interactive storytelling platforms. The returned data typically includes a string response generated in real-time, which can be seamlessly integrated into chat interfaces or other communication tools to enhance user engagement and provide informative replies.

Endpoint ID: 26341
POST https://api-sneakers-database.zylalabs.com/api/13018/qwen+ai+api/26341/chat
INPUT PARAMETERS

chat — Endpoint Features

Object Description
Request Body Required Json

Free test requests remaining: 3 of 3.


INPUT PARAMETERS

API EXAMPLE RESPONSE
JSON
{"choices":[{"finish_reason":"stop","index":0,"message":{"content":"Hello! How can I help you today?","reasoning_content":"Thinking Process:\n\n1.  **Analyze the input:** The user said \"Hello\".\n2.  **Determine the intent:** This is a standard greeting.\n3.  **Formulate a response:**\n    *   Acknowledge the greeting.\n    *   Offer assistance.\n    *   Keep it polite, friendly, and open-ended.\n    *   *Examples:* \"Hello! How can I help you today?\", \"Hi there! What's on your mind?\", \"Greetings! How can I assist you?\"\n4.  **Select the best response:** \"Hello! How can I help you today?\" is standard, polite, and effective.\n5.  **Output the response.**","role":"assistant"}}],"created":1783012848,"id":"chatcmpl-ef622ad5-24a5-94ab-8696-828946aa1578","model":"qwen3.7-max","object":"chat.completion","usage":{"completion_tokens":162,"completion_tokens_details":{"reasoning_tokens":148},"prompt_tokens":11,"prompt_tokens_details":{"cached_tokens":0},"total_tokens":173}}
Chat — CODE SNIPPETS

curl --location --request POST 'https://zylalabs.com/api/13018/qwen+ai+api/26341/chat' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{
  "model": "qwen3.7-max",
  "messages": [
    {
      "role": "user",
      "content": "Hello"
    }
  ]
}'

    

API Access Key & Authentication

After signing up, every developer is assigned a personal API access key, a unique combination of letters and digits provided to access to our API endpoint. To authenticate with the Qwen AI API simply include your bearer token in the Authorization header.

Headers
Header Description
Authorization Required Should be Bearer access_key. See "Your API Access Key" above when you are subscribed.

No long-term commitment. Upgrade, downgrade, or cancel anytime. Free Trial includes up to 50 requests.

(Save 2 months with annual billing 🎉)

🚀 Enterprise Plan

Starts at
$ 10,000/Year


  • Custom Volume
  • Custom Rate Limit
  • Specialized Customer Support
  • Real-Time API Monitoring
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Overview

API for Alibaba Qwen large language models supporting text generation, reasoning, and vision. OpenAI-compatible endpoints with context up to 1M tokens.

Models: qwen-max, qwen-plus, qwen-turbo, qwen-vl-max, qwen-vl-plus.

Features: chat completions, system prompts, function calling, streaming, image input. Authentication via Bearer token.

Qwen AI API FAQs

The List Available Models endpoint returns a list of model specifications, including model IDs, names, and context window sizes. The Vision Chat Image Analysis endpoint returns image analysis results, including descriptions, visual question answers, and usage statistics like token counts.

For the List Available Models, key fields include "id," "object," "created," and "owned_by." In the Vision Chat Image Analysis response, important fields are "id," "model," "choices," and "usage," which details token counts for prompts and completions.

The List Available Models response is structured as an object containing a "data" array of model objects. The Vision Chat Image Analysis response includes an "id," "model," and a "choices" array, where each choice contains a "message" with the analysis content and a "usage" object detailing token usage.

The Vision Chat Image Analysis endpoint accepts parameters such as the image file (in supported formats) and any specific questions related to the image. Users can customize requests by specifying the type of analysis or questions they want answered.

Typical use cases include generating text based on prompts, analyzing images for descriptions or answering questions about visual content, and extracting text from images using OCR capabilities. These functionalities can be applied in various fields like education, content creation, and accessibility.

Data accuracy is maintained through rigorous training of the Qwen models on diverse datasets and continuous updates to improve performance. The models are designed to provide reliable outputs based on extensive testing and validation processes.

Users can utilize the returned data by parsing the response structure to extract relevant information, such as model capabilities or image analysis results. For instance, they can use the "content" field in the Vision Chat response to display image descriptions or answers in applications.

Users can expect structured responses with consistent fields across requests. For example, the Vision Chat responses will typically include a detailed analysis in the "content" field and token usage metrics, allowing users to gauge the complexity of their queries and the model's performance.

General FAQs

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