Call Qwen models via OpenAI API

Reference, synced 2026-06-13.

flowchart TD
  n0["Models"]
  n1["Overview"]
  n2["Products"]
  n3["Solutions"]
  n4["Pricing"]
  n5["Resources"]
  n6["Partners"]
  n7["Support"]
  n8["Language"]
  n0 --> n1
  n1 --> n2
  n2 --> n3
  n3 --> n4
  n4 --> n5
  n5 --> n6
  n6 --> n7
  n7 --> n8

Free access Accelerate Delivery with Fixed-Cost Agentic CodingWatch how it works

Models

Empowering AI innovation for both enterprises and developers with Alibaba Cloud’s best-in-class Qwen models, AI-native apps, and AI solutions.

Alibaba Cloud Model Studio \ Enterprise-grade large model service and application development platform.

Try Visual Model \ Supports image understanding, image generation, and video generation.

Models

HappyHorse-1.0-T2V \ Cinematic creative generation, ultimate dynamic details Qwen3-VL-Plus \ Native VL, spatial reasoning, 1M-context video analysis Wan2.7-VideoEdit \ Supports both localized and global editing with prompt

Qwen3.6-Plus \ Native multimodal, 1M context, agentic coding Wan2.7-Image-Pro \ Interactive editing, long-text rendering, precise prompt following Qwen-Plus \ Balanced intelligence, efficient inference, production-ready performance

Qwen-Image-2.0 \ Professional infographics, exquisite photorealism Z-Image-Turbo \ Ultra-fast image generation, high throughput, cost-optimized inference Qwen3-Coder-Next \ Multi-turn tool interactions, future-ready development support

Wan2.7-T2V \ High-fidelity T2V, 15s duration, advanced camera control Wan2.7-I2V \ Cinematic I2V with emotional depth and visceral impact Wan2.7-R2V \ Up to 5 mixed image/video inputs and audio timbre cloning

GenAI Application

Qoder \ Intelligent coding assistant, available for enterprise-dedicated deployment. Qoder CN \ AI-powered coding assistant that boosts developer productivity with intelligent code completion, AI chat, multi-file editing, and task automation.

AI Service

Model Experience \ Experience full-scale, multimodal model capabilities online. Platform for AI \ An AI-native algorithm engineering platform for end-to-end modeling, training, and inference service deployment. Fine-tune Video Generation Model \ Customize Wan’s text-to-video capabilities through model fine-tuning to meet your unique requirements.

AI Use Case

AI Savings Plan Hot \ Save up to 47% on AI costs. Limited-time offer tailored to your usage. AI Video Creation \ Elevate your professional video production with Wan 2.6.

AI Token Plan \ One plan. Multiple models. Big Savings with a Fixed Subscription. AI Image Creation \ All-in-one creative suite for copywriting, image generation, and poster design.

Overview

As a global full-stack AI leader, Alibaba Cloud aims to make computing accessible to everyone and help worldwide customers accelerate innovation.

Why Alibaba Cloud

About Alibaba Cloud \ AI Powered Cloud Technology Our Global Network \ Explore our global presence and deployment regions around the world Our Global Offices \ With offices in 4 continents, we're always close to where it matters.

Asia Accelerator \ Accelerate Success in Asia with Alibaba Cloud Go Global \ Benefits of our Global Alliance Trust Center \ Empowering enterprises with a secure, compliant, and globally trusted cloud infrastructure

Customers and Insights

Olympic Games \ Alibaba Cloud Powers Olympic Games with AI-powered cloud technology Case Studies \ Learn how customers are scaling their businesses on Alibaba Cloud Analyst Reports \ Learn what the top industry analyst firms are saying about Alibaba Cloud

What's New

Events and Webinars \ Quick access to upcoming and on-demand events Product Updates \ Stay informed of the latest innovations Press Room \ Latest news and media releases

Products

Featured ProductsAI & Machine Learning Computing Container Storage Networking & CDN Security Middleware Database Analytics ComputingMedia ServicesEnterprise Services & Cloud CommunicationDomain Names and WebsitesEnd User ComputingServerlessDeveloper ToolsMigration & O&M ManagementApsara Stack

Featured Products

Alibaba Cloud Model Studio \ Supercharge your AI journey effortlessly with industry-leading GenAI models ApsaraDB RDS \ Store and manage your business data, with automated monitoring and backups Certificate Management Service (Original SSL Certificate) \ Create a safe and secure connection between your website and users

Elastic Compute Service (ECS) \ Host websites anywhere and scale enterprise workloads Container Service for Kubernetes (ACK) \ Run and scale containerized applications on managed Kubernetes infrastructure Object Storage Service (OSS) \ Store large amounts of data in the cloud and access it anywhere, anytime

Simple Application Server (SAS) \ All-in-one services for fast deployment Elastic IP Address (EIP) \ Manage your public IPs independently to improve internet network quality Domain Names and Website \ Get the perfect domain name to suit your every need

Solutions

Solutions by Industry Technical Solutions AI WebsitesNetworking Security and ComplianceData and AnalyticsEnterprise Service and ApplicationCloud MigrationCloud NativeHybrid CloudSMB solutions

Financial Services \ Innovate faster with Alibaba Cloud Games \ Grow your game rapidly with high global availability

New Retail \ Alibaba Cloud enables digital retail transformation to fuel growth and realize an omnichannel customer experience throughout the consumer journey. Media and Entertainment \ Ready your content for today's media market with a digitalized media journey

Supply Chain \ Power your supply chain with intelligent, efficient, and reliable solutions Sports \ Digitizing the sports industry with intelligent tech

Sustainability \ Achieve a sustainable future with low-carbon and energy-efficient technologies

Pricing

Flexible options like pay-as-you-go and clear billing rules to meet diverse business needs.

Overview & Tools

Pricing Calculator \ Get an instant pricing estimate based on your usage and needs Free Trial \ Try our 80+ cloud products for free.

Pricing Options \ Get the most out of Alibaba Cloud with flexible pricing

Optimize your cost

Migrate & Save \ Superior Performance At Lower Pricing. Save up to 50%. Promotion Center \ Unlock the latest Alibaba Cloud offers & promos

Resources

Official documentation, extensive tools, training resources, and a community to grow and innovate in the cloud.

Technical Resources

Documentation \ Product guides and FAQs Architecture Center \ Design reliable, secure, and efficient cloud architecture. Intelligent Solution Explorer \ Find the right solution for you, powered by AI

Blog \ Latest cloud insights and developer trends Whitepapers \ Research that explores the how and why behind our technology

Training&Certification

Alibaba Cloud Academy \ Build cloud skills and earn certifications with expert-led training.

Developer Hub

Alibaba Cloud Project Hub \ Explore real-world projects built by developers using our platform. Our Developer MVPs \ Celebrating the developers who lead, build, and inspire our community

Partners

Partner-first strategy offering collaborative product, sales, and service models, plus high-quality partner solutions that complement Alibaba Cloud’s capabilities.

Marketplace

AI Alliance for ISVs \ Partner with us to build and grow AI solutions together ISV Benefits \ Unlock resources, market access, and go-to-market support as an ISV partner

Alibaba Cloud Marketplace \ Explore ready-to-deploy solutions from our partners and ISVs

Find a Partner

Partner Hub \ Find your ideal partner in no time

Become a Partner

Partner Network \ A partner portal for Alibaba Cloud Channel, Technology, MSP partner and other partner programs

Support

Full-lifecycle support and expert services, from cloud advisory and migration to operations.

Support & Professional Services

Professional Services \ Expert-led services to design, migrate, and optimize your cloud journey Support Plans \ Flexible support for every stage — from startup to enterprise

Partner Support Program \ Priority technical support for partners, with dedicated managers and faster issue resolution

Contact us

Connect With Us \

Talk to a sales expert and get a custom quote for your business

Language

English
简体中文
繁體中文
日本語
Bahasa Indonesia

Locale

Visit aliyun.com

Documentation

Alibaba Cloud Model Studio

User Guide (Models) User Guide (Application) API Reference (Models) API Reference (Application)

Search for Help Content

Getting Started

The Beginner's Guide

Well-Architected Framework

AI & Machine Learning

Platform For AI

Alibaba Cloud Model Studio

DashVector

Artificial Intelligence Recommendation

OpenSearch

Image Search

Machine Translation

Intelligent Speech Interaction

Optimization Solver

Intelligent Computing LINGJUN

Computing

Elastic Compute Service

Elastic GPU Service

Elastic Container Instance

Dedicated Host

Compute Nest

Simple Application Server

Cloud Box

Auto Scaling

Elastic High Performance Computing

Batch Compute (Deprecated)

Function Compute

Serverless App Engine

ENS

Elastic Desktop Service

App Streaming

WUYING Terminal

Cloud Phone

Edge Network Acceleration

Alibaba Cloud Linux

AgentBay

Container

Container Service for Kubernetes

Container Compute Service

Container Registry

Storage

Object Storage Service

Cloud Parallel File Storage

File Storage NAS

Tablestore

Storage Capacity Unit

Simple Log Service

Cloud Backup

Intelligent Media Management

Drive and Photo Service

Data Transport

Cloud Storage Gateway

Data Online Migration

Hybrid Cloud Storage Array

Storage Services Overview

Backup and Disaster Recovery Center

Networking and CDN

Server Load Balancer

Elastic IP Address

Internet Shared Bandwidth

Data Transfer Plan

Virtual Private Cloud

NAT Gateway

PrivateLink

Alibaba Cloud DNS PrivateZone

Network Intelligence Service

Cloud Data Transfer

IPv6 Gateway

Anycast Elastic IP Address

Cloud Enterprise Network

Global Accelerator

VPN Gateway

Smart Access Gateway

Express Connect

CDN

Edge Security Acceleration

Cloud Network Well-architected Design Guidelines

Security

Anti-DDoS

Web Application Firewall

Cloud Firewall

Security Center

Bastionhost

Secure Access Service Edge

Certificate Management Service

Key Management Service

Data Security Center

Identity as a Service

Fraud Detection

AI Guardrails

Captcha

Blockchain as a Service

ID Verification

Managed Security Service

Middleware

Enterprise Distributed Application Service

Microservices Engine

Alibaba Cloud Service Mesh

SchedulerX

ApsaraMQ for RocketMQ

ApsaraMQ for Kafka

ApsaraMQ for RabbitMQ

ApsaraMQ for MQTT

Simple Message Queue (formerly MNS)

CloudFlow

EventBridge

Application Real-Time Monitoring Service

Managed Service for Prometheus

Managed Service for Grafana

Managed Service for OpenTelemetry

Performance Testing

STAROps

Databases

ApsaraDB Console

PolarDB

ApsaraDB RDS

ApsaraDB for OceanBase (Deprecated)

Tair (Redis® OSS-Compatible)

Lindorm

Time Series Database

ApsaraDB for MongoDB

ApsaraDB for HBase

ApsaraDB for Memcache

ApsaraDB for MyBase

AnalyticDB

ApsaraDB for ClickHouse

ApsaraDB for SelectDB

Data Transmission Service

Database Autonomy Service

Data Management

Database Gateway - Deprecated

ApsaraDB for Cassandra - Deprecated

Analytics Computing

MaxCompute

Hologres

Realtime Compute for Apache Flink

Elasticsearch

Vector Retrieval Service for Milvus

E-MapReduce

Data Lake Formation

DataV

Quick BI

Quick Audience

Quick Tracking

DataWorks

DataHub

Dataphin

Media Services

ApsaraVideo VOD

ApsaraVideo Live

Intelligent Media Services

ApsaraVideo Media Processing

Apsara Video SDK

Enterprise Services & Cloud Communication

Energy Expert

CloudQuotation

Salesforce on Alibaba Cloud

GoChina ICP Filing Assistant

Marketplace

Alibaba Mail

Direct Mail

Short Message Service

Voice Service

Phone Number Verification Service

Cell Phone Number Service

Chat App Message Service

Financial Intelligence Engine

Domain Names and Websites

Domain Names

ICP Filing

Alibaba Cloud DNS

End User Computing

Elastic Desktop Service

App Streaming

WUYING Terminal

Cloud Phone

AgentBay

Internet of Things

IoT Platform

Serverless

Serverless App Engine

CloudFlow

EventBridge

Simple Message Queue (formerly MNS)

Function Compute

Developer Tools

OpenAPI Explorer

Alibaba Cloud SDK

Cloud Shell

Resource Orchestration Service

Alibaba Cloud CLI

BSS OpenAPI

Terraform

Pulumi

Ticket System API

Mobile Platform as a Service

Alibaba Cloud DevOps

API Gateway

Cloud Control API

AI Coding Assistant Lingma

Cloud Skills Portal

Migration & O&M Management

CloudOps Orchestration Service

Cloud Monitor

Intelligent Advisor

Cloud Governance Center

ActionTrail

Cloud Config

Resource Access Management

Resource Management

Cloud Architect Design Tools

Migration Hub

Server Migration Center

Service Catalog

Logic Composer

Quota Center

CloudSSO

HTTPDNS

Solutions

SAP

SuperApp

OpenLake

Membership Service

Expenses and Costs

Account Center

Support

Legal

Tech Share Terms and Conditions

After Sales Support

China Gateway Program

Service Level Objectives

Management Console

Security Control

Model Studio provides an OpenAI-compatible interface for Qwen models. To migrate from OpenAI, update your API key, BASE_URL, and model name.

OpenAI compatibility

BASE_URL

Configure the BASE_URL to connect to Model Studio through the OpenAI-compatible interface. The BASE_URL is the network endpoint for the model service.

When using the OpenAI SDK or other OpenAI-compatible SDKs, configure the BASE_URL as follows:

http

Singapore: https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1
US (Virginia): https://dashscope-us.aliyuncs.com/compatible-mode/v1
China (Beijing): https://dashscope.aliyuncs.com/compatible-mode/v1
China (Hong Kong): https://{WorkspaceId}.cn-hongkong.maas.aliyuncs.com/compatible-mode/v1

When making HTTP requests, configure the full endpoint as follows:

http

Singapore: POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions
US (Virginia): POST https://dashscope-us.aliyuncs.com/compatible-mode/v1/chat/completions
China (Beijing): POST https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions
China (Hong Kong): POST https://{WorkspaceId}.cn-hongkong.maas.aliyuncs.com/compatible-mode/v1/chat/completions

Important

The legacy Singapore domain https://dashscope-intl.aliyuncs.comand China (Hong Kong) domain https://cn-hongkong.dashscope.aliyuncs.comwill be discontinued. Migrate to https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com (Singapore) and https://{WorkspaceId}.cn-hongkong.maas.aliyuncs.com (China (Hong Kong)) as soon as possible.

Supported models

Supported models include Qwen large language models (commercial and open source), Qwen-VL, Qwen-Coder, Qwen-Omni, and Qwen-Math, DeepSeek, Kimi, GLM, MiniMax.

Call Qwen models via the OpenAI SDK

Prerequisites

Install Python.
Install the latest OpenAI SDK.

shell

# If the following command fails, replace pip with pip3.
pip install -U openai

Activate Model Studio and get an API key. See Obtain an API key.
Configure the API key as an environment variable to reduce exposure risk. Configure the API key as an environment variable. You can also set it directly in code, but this increases exposure risk.
Select a model from the List of supported models.

Usage

Non-streaming call example

python

from openai import OpenAI
import os

def get_response():
    client = OpenAI(
        # API keys differ by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
        api_key=os.getenv("DASHSCOPE_API_KEY"),  # If you have not configured an environment variable, replace this line with your Model Studio API key: api_key="sk-xxx"
        # The following is the base_url for the Singapore region.
        base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
    )
    completion = client.chat.completions.create(
        model="qwen-plus",  # This example uses qwen-plus. You can change the model name as needed. For a list of models, see https://www.alibabacloud.com/help/en/model-studio/getting-started/models
        messages=[{'role': 'system', 'content': 'You are a helpful assistant.'},\
                  {'role': 'user', 'content': 'Who are you?'}]
        )
    print(completion.model_dump_json())

if __name__ == '__main__':
    get_response()

Output:

json

{
    "id": "chatcmpl-xxx",
    "choices": [\
        {\
            "finish_reason": "stop",\
            "index": 0,\
            "logprobs": null,\
            "message": {\
                "content": "I am a large-scale pre-trained model from Alibaba Cloud. My name is Qwen.",\
                "role": "assistant",\
                "function_call": null,\
                "tool_calls": null\
            }\
        }\
    ],
    "created": 1716430652,
    "model": "qwen-plus",
    "object": "chat.completion",
    "system_fingerprint": null,
    "usage": {
        "completion_tokens": 18,
        "prompt_tokens": 22,
        "total_tokens": 40
    }
}

Streaming call example

python

from openai import OpenAI
import os

def get_response():
    client = OpenAI(
        # API keys differ by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
        # If you have not configured an environment variable, replace the following line with your Model Studio API key: api_key="sk-xxx"
        api_key=os.getenv("DASHSCOPE_API_KEY"),
        # The following is the base_url for the Singapore region.
        base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",

    )
    completion = client.chat.completions.create(
        model="qwen-plus",  # This example uses qwen-plus. You can change the model name as needed. For a list of models, see https://www.alibabacloud.com/help/en/model-studio/getting-started/models
        messages=[{'role': 'system', 'content': 'You are a helpful assistant.'},\
                  {'role': 'user', 'content': 'Who are you?'}],
        stream=True,
        # The following setting displays token usage information in the last line of the streaming output.
        stream_options={"include_usage": True}
        )
    for chunk in completion:
        print(chunk.model_dump_json())

if __name__ == '__main__':
    get_response()

Output:

json

{"id":"chatcmpl-xxx","choices":[{"delta":{"content":"","function_call":null,"role":"assistant","tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1719286190,"model":"qwen-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[{"delta":{"content":"I am","function_call":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1719286190,"model":"qwen-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[{"delta":{"content":" a large","function_call":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1719286190,"model":"qwen-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[{"delta":{"content":" language model","function_call":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1719286190,"model":"qwen-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[{"delta":{"content":" from Alibaba","function_call":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1719286190,"model":"qwen-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[{"delta":{"content":" Cloud, and my","function_call":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1719286190,"model":"qwen-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[{"delta":{"content":" name is Qwen.","function_call":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1719286190,"model":"qwen-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[{"delta":{"content":"","function_call":null,"role":null,"tool_calls":null},"finish_reason":"stop","index":0,"logprobs":null}],"created":1719286190,"model":"qwen-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":null}
{"id":"chatcmpl-xxx","choices":[],"created":1719286190,"model":"qwen-plus","object":"chat.completion.chunk","system_fingerprint":null,"usage":{"completion_tokens":16,"prompt_tokens":22,"total_tokens":38}}

Function call example

The following code demonstrates multi-turn function calling with weather and time query tools.

python

from openai import OpenAI
from datetime import datetime
import json
import os

client = OpenAI(
    # API keys differ by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
    # If you have not configured an environment variable, replace the following line with your Model Studio API key: api_key="sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # The following is the base_url for the Singapore region.
    base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
)

# Define the list of tools. The model refers to the name and description of the tools when selecting which one to use.
tools = [\
    # Tool 1: Get the current time.\
    {\
        "type": "function",\
        "function": {\
            "name": "get_current_time",\
            "description": "Useful when you want to know the current time.",\
            # Since no input parameters are needed to get the current time, the 'parameters' object is empty.\
            "parameters": {}\
        }\
    },\
    # Tool 2: Get the weather for a specified city.\
    {\
        "type": "function",\
        "function": {\
            "name": "get_current_weather",\
            "description": "Useful when you want to query the weather for a specified city.",\
            "parameters": {\
                "type": "object",\
                "properties": {\
                    # A location is required to query the weather, so a 'location' parameter is defined.\
                    "location": {\
                        "type": "string",\
                        "description": "A city or district, such as Beijing, Hangzhou, or Yuhang."\
                    }\
                },\
                "required": [\
                    "location"\
                ]\
            }\
        }\
    }\
]

# Simulate a weather query tool. Example result: "It is rainy in Beijing today."
def get_current_weather(location):
    return f"It is rainy in {location} today. "

# A tool to query the current time. Example result: "Current time: 2024-04-15 17:15:18."
def get_current_time():
    # Get the current date and time.
    current_datetime = datetime.now()
    # Format the current date and time.
    formatted_time = current_datetime.strftime('%Y-%m-%d %H:%M:%S')
    # Return the formatted current time.
    return f"Current time: {formatted_time}."

# Define the model response function.
def get_response(messages):
    completion = client.chat.completions.create(
        model="qwen-plus",  # This example uses qwen-plus. You can change the model name as needed. For a list of models, see https://www.alibabacloud.com/help/en/model-studio/getting-started/models
        messages=messages,
        tools=tools
        )
    return completion.model_dump()

def call_with_messages():
    print('\n')
    messages = [\
            {\
                "content": input('Please enter: '),  # Example prompts: "What time is it now?" "What time will it be in an hour?" "What is the weather like in Beijing?"\
                "role": "user"\
            }\
    ]
    print("-"*60)
    # First turn of the model call.
    i = 1
    first_response = get_response(messages)
    assistant_output = first_response['choices'][0]['message']
    print(f"\nLLM output in turn {i}: {first_response}\n")
    if  assistant_output['content'] is None:
        assistant_output['content'] = ""
    messages.append(assistant_output)
    # If the model determines that a tool call is not needed, it prints the assistant's reply directly.
    if assistant_output['tool_calls'] == None:
        print(f"No tool call is needed. I can reply directly: {assistant_output['content']}")
        return
    # If a tool call is needed, the code continues to make model calls until a final answer is generated.
    while assistant_output['tool_calls'] != None:
        # If the model determines that the weather query tool needs to be called, run the weather query tool.
        if assistant_output['tool_calls'][0]['function']['name'] == 'get_current_weather':
            tool_info = {"name": "get_current_weather", "role":"tool"}
            # Extract the location parameter.
            location = json.loads(assistant_output['tool_calls'][0]['function']['arguments'])['location']
            tool_info['content'] = get_current_weather(location)
        # If the model determines that the time query tool needs to be called, run the time query tool.
        elif assistant_output['tool_calls'][0]['function']['name'] == 'get_current_time':
            tool_info = {"name": "get_current_time", "role":"tool"}
            tool_info['content'] = get_current_time()
        print(f"Tool output: {tool_info['content']}\n")
        print("-"*60)
        messages.append(tool_info)
        assistant_output = get_response(messages)['choices'][0]['message']
        if  assistant_output['content'] is None:
            assistant_output['content'] = ""
        messages.append(assistant_output)
        i += 1
        print(f"LLM output in turn {i}: {assistant_output}\n")
    print(f"Final answer: {assistant_output['content']}")

if __name__ == '__main__':
    call_with_messages()

If you enter What's the weather like in Hangzhou and Beijing? What time is it now?, the program returns the following output:

Parameters

Input parameters compatible with the OpenAI API:

Parameter	Type	Default	Description


Parameter	Type	Default	Description
model	string	-	The model to use. See List of supported models.
messages	array	-	The conversation history between the user and the model. Each element in the array has the format `{"role": , "content": }`. The available roles are system, user, and assistant. The `system` role is supported only in `messages[0]`. In general, the `user` and `assistant` roles must alternate, and the role of the last element in `messages` must be `user`.
top_p (optional)	float	-	Nucleus sampling threshold. A value of 0.8 retains the smallest set of tokens whose cumulative probability exceeds 0.8. Range: (0, 1.0). Higher values increase randomness; lower values increase determinism.
temperature (optional)	float	-	Controls output randomness. A higher value produces more diverse output; a lower value produces more deterministic output. Range: [0, 2). Do not set to 0.
presence_penalty (optional)	float	-	Controls the repetition of tokens in the generated sequence. A higher `presence_penalty` value reduces token repetition. The value must be in the range of [-2.0, 2.0]. Note This parameter is supported only by commercial Qwen models and open source models from qwen1.5 and later.
n (optional)	integer	1	The number of responses to generate. The value must be in the range of `1-4`. For scenarios that require multiple responses, such as creative writing or ad copy, you can set a larger value for `n`. > Setting a larger `n` value does not increase input token usage but increases output token usage. > This parameter is currently supported only for the `qwen-plus` model, and its value is fixed to 1 when the `tools` parameter is passed.
max_tokens (optional)	integer	-	Maximum number of tokens the model can generate. Output limits vary by model. Check the supported models list above.
seed (optional)	integer	-	Random number seed for generation. `seed` supports unsigned 64-bit integers.
stream (optional)	boolean	False	Enables streaming output. When enabled, the API returns a generator that you iterate through. Each chunk is an incremental part of the response.
stop (optional)	string or array	None	The `stop` parameter provides precise control over the generation process by stopping generation when the model is about to output a specified string or token ID. `stop` can be a string or an array. - string type Stops generation when the model is about to generate the specified stop word. For example, if `stop` is set to "hello", the model will stop when it is about to generate "hello". - array type Elements can be token IDs, strings, or arrays of token IDs. Generation stops when the next token matches a stop list entry. Examples use the `qwen-turbo` tokenizer: 1. Elements are token IDs: The token IDs 108386 and 104307 correspond to the tokens "hello" and "weather" respectively. If `stop` is set to `[108386,104307]`, the model will stop when it is about to generate "hello" or "weather". 2. Elements are strings: If `stop` is set to `["hello","weather"]`, the model will stop when it is about to generate "hello" or "weather". 3. Elements are arrays of token IDs: The token IDs 108386 and 103924 correspond to the tokens "hello" and "ah", and the token IDs 35946 and 101243 correspond to "I" and "am fine". If `stop` is set to `[[108386, 103924],[35946, 101243]]`, the model stops when it is about to generate "hello ah" or "I am fine". Note When `stop` is an array, its elements must be of the same type. You cannot mix token IDs and strings, for example, `["hello", 104307]`.
tools (optional)	array	None	Tools the model can call. In a function call flow, the model selects one tool from this library. Each tool in the `tools` array has the following structure: - `type`: Tool type. Currently only `function` is supported. - `function`: An object with the following keys: `name`, `description`, and `parameters`: - `name`: Function name. Only letters, numbers, underscores, and hyphens; max 64 characters. - `description`: Function description. The model uses this to decide when and how to call the function. - `parameters`: An object describing the tool's parameters as valid JSON Schema. If the `parameters` object is empty, the function takes no input parameters. In a function call flow, set the `tools` parameter both when initiating the call and when submitting tool results. Supported models: `qwen-turbo`, `qwen-plus`, and `qwen-max`. Note The `tools` parameter cannot be used with `stream=True`.
stream_options (optional)	object	None	Displays token usage during streaming. Effective only when `stream` is `True`. Set `stream_options={"include_usage":True}` to include token counts.
\

Response parameters\

| Parameter | Type | Description | Remarks |
| --- | --- | --- | --- |

| | | | |
| --- | --- | --- | --- |
| Parameter | Type | Description | Remarks |
| id | string | A unique, system-generated ID for the request. | - |
| model | string | The model used for the request. | - |
| system_fingerprint | string | Currently unused. Returns an empty string. | - |
| choices | array | A list of generated chat completions. | - |
| choices[i].finish_reason | string | The reason the model stopped generating tokens. Possible values are: - null: The generation is still in progress. - stop: The model generated a stop sequence specified in the request. - length: The maximum number of tokens specified in the request was reached. |
| choices[i].message | object | A message object generated by the model. |
| choices[i].message.role | string | The role of the message author. This value is always assistant. |
| choices[i].message.content | string | The model-generated message content. |
| choices[i].index | integer | The index of the choice in the choices list. The default value is 0. |
| created | integer | The creation time of the chat completion, as a Unix timestamp in seconds. | - |
| usage | object | Token usage statistics for the request. | - |
| usage.prompt_tokens | integer | The number of tokens in the input prompt. | - |
| usage.completion_tokens | integer | The number of tokens in the generated completion. | - |
| usage.total_tokens | integer | The total number of tokens used in the request (prompt_tokens + completion_tokens). | - |
\

Call with the langchain_openai SDK\

Prerequisites\

Install Python.

\
Install the langchain_openai SDK.

\

shell\

# If the following command fails, replace pip with pip3.\
pip install -U langchain_openai\
```\
\
\
- Activate Model Studio and get an API key. See Obtain an API key.\
\
- Configure the API key as an environment variable to reduce exposure risk. Configure the API key as an environment variable. You can also set it in code, **but this increases exposure risk**.\
\
- Select a model from the Supported model list.\
\
\
### **Usage**\
\
#### **Non-streaming output**\
\
Use the `invoke` method for non-streaming output:\
\
```python\
from langchain_openai import ChatOpenAI\
import os\
\
def get_response():\
    llm = ChatOpenAI(\
        # API keys differ by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key\
        api_key=os.getenv("DASHSCOPE_API_KEY"),  # If you have not configured an environment variable, replace this line with your Model Studio API key: api_key="sk-xxx"\
        base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1", # This is the base_url for the Singapore region.\
        model="qwen-plus"  # This example uses qwen-plus. You can change the model name as needed. For a list of models, see https://www.alibabacloud.com/help/en/model-studio/getting-started/models\
        )\
    messages = [\
        {"role":"system","content":"You are a helpful assistant."},\
        {"role":"user","content":"Who are you?"}\
    ]\
    response = llm.invoke(messages)\
    print(response.json())\
\
if __name__ == "__main__":\
    get_response()\
```\
\
Output:\
\
```json\
{\
    "content": "I am a large language model from Alibaba Cloud. My name is Tongyi Qwen.",\
    "additional_kwargs": {},\
    "response_metadata": {\
        "token_usage": {\
            "completion_tokens": 16,\
            "prompt_tokens": 22,\
            "total_tokens": 38\
        },\
        "model_name": "qwen-plus",\
        "system_fingerprint": "",\
        "finish_reason": "stop",\
        "logprobs": null\
    },\
    "type": "ai",\
    "name": null,\
    "id": "run-xxx",\
    "example": false,\
    "tool_calls": [],\
    "invalid_tool_calls": []\
}\
```\
\
#### **Streaming output**\
\
Use the `stream` method for streaming output. No additional `stream` parameter is required.\
\
```python\
from langchain_openai import ChatOpenAI\
import os\
\
def get_response():\
    llm = ChatOpenAI(\
        # API keys differ by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key\
        api_key=os.getenv("DASHSCOPE_API_KEY"),  # If you have not configured an environment variable, replace this line with your Model Studio API key: api_key="sk-xxx"\
        base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",   # This is the base_url for the Singapore region.\
        model="qwen-plus",   # This example uses qwen-plus. You can change the model name as needed. For a list of models, see https://www.alibabacloud.com/help/en/model-studio/getting-started/models\
        stream_usage=True\
        )\
    messages = [\
        {"role":"system","content":"You are a helpful assistant."},\
        {"role":"user","content":"Who are you?"},\
    ]\
    response = llm.stream(messages)\
    for chunk in response:\
        print(chunk.model_dump_json())\
\
if __name__ == "__main__":\
    get_response()\
```\
\
Output:\
\
```json\
{"content": "", "additional_kwargs": {}, "response_metadata": {}, "type": "AIMessageChunk", "name": null, "id": "run-xxx", "example": false, "tool_calls": [], "invalid_tool_calls": [], "usage_metadata": null, "tool_call_chunks": []}\
{"content": "I", "additional_kwargs": {}, "response_metadata": {}, "type": "AIMessageChunk", "name": null, "id": "run-xxx", "example": false, "tool_calls": [], "invalid_tool_calls": [], "usage_metadata": null, "tool_call_chunks": []}\
{"content": " am", "additional_kwargs": {}, "response_metadata": {}, "type": "AIMessageChunk", "name": null, "id": "run-xxx", "example": false, "tool_calls": [], "invalid_tool_calls": [], "usage_metadata": null, "tool_call_chunks": []}\
{"content": " a", "additional_kwargs": {}, "response_metadata": {}, "type": "AIMessageChunk", "name": null, "id": "run-xxx", "example": false, "tool_calls": [], "invalid_tool_calls": [], "usage_metadata": null, "tool_call_chunks": []}\
{"content": " large", "additional_kwargs": {}, "response_metadata": {}, "type": "AIMessageChunk", "name": null, "id": "run-xxx", "example": false, "tool_calls": [], "invalid_tool_calls": [], "usage_metadata": null, "tool_call_chunks": []}\
{"content": " language model", "additional_kwargs": {}, "response_metadata": {}, "type": "AIMessageChunk", "name": null, "id": "run-xxx", "example": false, "tool_calls": [], "invalid_tool_calls": [], "usage_metadata": null, "tool_call_chunks": []}\
{"content": " from", "additional_kwargs": {}, "response_metadata": {}, "type": "AIMessageChunk", "name": null, "id": "run-xxx", "example": false, "tool_calls": [], "invalid_tool_calls": [], "usage_metadata": null, "tool_call_chunks": []}\
{"content": " Alibaba Cloud. My name is Tongyi", "additional_kwargs": {}, "response_metadata": {}, "type": "AIMessageChunk", "name": null, "id": "run-xxx", "example": false, "tool_calls": [], "invalid_tool_calls": [], "usage_metadata": null, "tool_call_chunks": []}\
{"content": " Qwen.", "additional_kwargs": {}, "response_metadata": {}, "type": "AIMessageChunk", "name": null, "id": "run-xxx", "example": false, "tool_calls": [], "invalid_tool_calls": [], "usage_metadata": null, "tool_call_chunks": []}\
{"content": "", "additional_kwargs": {}, "response_metadata": {"finish_reason": "stop"}, "type": "AIMessageChunk", "name": null, "id": "run-xxx", "example": false, "tool_calls": [], "invalid_tool_calls": [], "usage_metadata": null, "tool_call_chunks": []}\
{"content": "", "additional_kwargs": {}, "response_metadata": {}, "type": "AIMessageChunk", "name": null, "id": "run-xxx", "example": false, "tool_calls": [], "invalid_tool_calls": [], "usage_metadata": {"input_tokens": 22, "output_tokens": 16, "total_tokens": 38}, "tool_call_chunks": []}\
```\
\
For input parameters, see Input parameters.\
\
## **HTTP API calls**\
\
Call Model Studio via HTTP. Responses follow the same structure as the OpenAI API.\
\
### **Prerequisites**\
\
- Activate Model Studio and get an API key. See Obtain an API key.\
\
- Configure the API key as an environment variable to reduce exposure risk. Configure an API key as an environment variable. You can also set it in code, **but this increases exposure risk**.\
\
\
### **API request**\
\
```http\
Singapore: POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions\
US (Virginia): POST https://dashscope-us.aliyuncs.com/compatible-mode/v1/chat/completions\
China (Beijing): POST https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions\
China (Hong Kong): POST https://{WorkspaceId}.cn-hongkong.maas.aliyuncs.com/compatible-mode/v1/chat/completions\
```\
\
### **Request example**\
\
Call the API with `cURL`.\
\
**Note**\
\
If you have not configured the API key as an environment variable, replace $DASHSCOPE\_API\_KEY with your API key.\
\
#### **Non-streaming output**\
\
curl\
\
curl\
\
```curl\
# This is the base_url for the Singapore region.\
curl --location 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions' \\
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \\
--header 'Content-Type: application/json' \\
--data '{\
    "model": "qwen-plus",\
    "messages": [\
        {\
            "role": "system",\
            "content": "You are a helpful assistant."\
        },\
        {\
            "role": "user",\
            "content": "Who are you?"\
        }\
    ]\
}'\
```\
\
Output:\
\
```json\
{\
    "choices": [\
        {\
            "message": {\
                "role": "assistant",\
                "content": "I am a large language model from Alibaba Cloud. My name is Qwen."\
            },\
            "finish_reason": "stop",\
            "index": 0,\
            "logprobs": null\
        }\
    ],\
    "object": "chat.completion",\
    "usage": {\
        "prompt_tokens": 11,\
        "completion_tokens": 16,\
        "total_tokens": 27\
    },\
    "created": 1715252778,\
    "system_fingerprint": "",\
    "model": "qwen-plus",\
    "id": "chatcmpl-xxx"\
}\
```\
\
#### **Streaming output**\
\
To enable streaming output, set the `stream` parameter to `true` in the request body.\
\
```curl\
# This is the base_url for the Singapore region.\
curl --location 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions' \\
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \\
--header 'Content-Type: application/json' \\
--data '{\
    "model": "qwen-plus",\
    "messages": [\
        {\
            "role": "system",\
            "content": "You are a helpful assistant."\
        },\
        {\
            "role": "user",\
            "content": "Who are you?"\
        }\
    ],\
    "stream":true\
}'\
```\
\
Output:\
\
```json\
data: {"choices":[{"delta":{"content":"","role":"assistant"},"index":0,"logprobs":null,"finish_reason":null}],"object":"chat.completion.chunk","usage":null,"created":1715931028,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-3bb05cf5cd819fbca5f0b8d67a025022"}\
\
data: {"choices":[{"finish_reason":null,"delta":{"content":"I am "},"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715931028,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-3bb05cf5cd819fbca5f0b8d67a025022"}\
\
data: {"choices":[{"delta":{"content":"a large "},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715931028,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-3bb05cf5cd819fbca5f0b8d67a025022"}\
\
data: {"choices":[{"delta":{"content":"language "},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715931028,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-3bb05cf5cd819fbca5f0b8d67a025022"}\
\
data: {"choices":[{"delta":{"content":"model from "},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715931028,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-3bb05cf5cd819fbca5f0b8d67a025022"}\
\
data: {"choices":[{"delta":{"content":"Alibaba Cloud. "},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715931028,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-3bb05cf5cd819fbca5f0b8d67a025022"}\
\
data: {"choices":[{"delta":{"content":"My name is Qwen."},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715931028,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-3bb05cf5cd819fbca5f0b8d67a025022"}\
\
data: {"choices":[{"delta":{"content":""},"finish_reason":"stop","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715931028,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-3bb05cf5cd819fbca5f0b8d67a025022"}\
\
data: [DONE]\
```\
\
See Input parameter configuration.\
\
### **Error response example**\
\
If a request fails, the response includes an error code and message explaining the failure.\
\
```json\
{\
    "error": {\
        "message": "Incorrect API key provided. ",\
        "type": "invalid_request_error",\
        "param": null,\
        "code": "invalid_api_key"\
    }\
}\
```\
\
## **Status codes**\
\
| **Error code** | **Description** |\
| --- | --- |\
\
|     |     |\
| --- | --- |\
| **Error code** | **Description** |\
| 400 - Invalid request error | Invalid request. See the error message for details. |\
| 401 - Incorrect API key provided | The provided API key is invalid. |\
| 429 - Rate limit reached for requests | The request rate has exceeded the limit, such as for queries per second (QPS) or queries per minute (QPM). |\
| 429 - You exceeded your current quota, please check your plan and billing details | Quota exceeded or your account has an overdue payment. |\
| 500 - The server had an error while processing your request | The server encountered an internal error. |\
| 503 - The engine is currently overloaded, please try again later | The service is temporarily overloaded. Please try again later. |\
\
Previous: Qwen-OCRNext: OpenAI-compatible - Responses\
\
Is this page helpful?\
\

\

\
OpenAI compatibility\
\
BASE\_URL\
\
Supported models\
\
Call Qwen models via the OpenAI SDK\
\
Prerequisites\
\
Usage\
\
Parameters\
\
Response parameters\
\
Call with the langchain\_openai SDK\
\
Prerequisites\
\
Usage\
\
HTTP API calls\
\
Prerequisites\
\
API request\
\
Request example\
\
Error response example\
\
Status codes\
\
\
\
 [](https://www.alibabacloud.com/help/en/model-studio/compatibility-of-openai-with-dashscope#top "Back to Top")\
\
Contact Us\
\
\
\
## Sales Support\
\
Live-chat with our sales team or get in touch with a business development professional in your region.\
\
Contact Sales\
\
## Technical Support\
\
Open a ticket and get quick help from our technical team.\
\
Open a Ticket >\
\
## Connect & Report Abuse\
\
We look forward to your suggestion.\
\
Post a Suggestion > Report Abuse >\
\
\\
\\
Hi, I'm Alibaba Cloud AI Assistant!\\
\\
I can help with questions and solutions.\
\
Why Alibaba Cloud\
\
About Alibaba Cloud\
\
Asia Accelerator\
\
Our Global Network\
\
Global Offices\
\
Trust Center\
\
Case Studies\
\
Analyst Reports\
\
Products & Pricings\
\
Pricing Calculator\
\
ECS\
\
SAS\
\
Model Studio\
\
Database\
\
Security\
\
SMS\
\
Solutions\
\
Financial Services\
\
Retail Services\
\
Media Services\
\
Gaming Services\
\
ISV Solutions\
\
Engage\
\
Developer Community\
\
Partner Network\
\
Startups\
\
Marketplace\
\
Join Alibaba Cloud\
\
Resources & Support\
\
Developer Learning Hub\
\
Documentation Center\
\
Training & Certification\
\
Service Notices\
\
Submit a Ticket\
\
Security Report\
\
Qwen Cloud\
\
Careers About Us Privacy Policy Legal Integrity Compliance Reporting Channel Service Notices Links\
\
© 2009-2026 Copyright by Alibaba Cloud All rights reserved\
\
- Facebook\
- Linkedin\
- Twitter\
- YouTube\
- TikTok\
- contact.us@alibabacloud.com\
- Call Us Now\
- Discord\
\
[](https://privacy.truste.com/privacy-seal/validation?rid=e83a96cf-eaa2-4c82-8d85-883196fcb42c)\
\
[](https://privacy.truste.com/privacy-seal/validation?rid=51be162a-c152-402b-84d2-d0a724de679a)\
\
© 2009-2026 Copyright by Alibaba Cloud All rights reserved\
\
Careers About Us Privacy Policy Legal Integrity Compliance Reporting Channel Service Notices Links\
\
```\
\

Call Qwen models via OpenAI API ​

Models ​

Models ​

GenAI Application ​

AI Service ​

AI Use Case ​

Overview ​

Why Alibaba Cloud ​

Customers and Insights ​

What's New ​

Products ​

Featured Products ​

Related Programs ​

Solutions ​

Pricing ​

Overview & Tools ​

Optimize your cost ​

Resources ​

Technical Resources ​

Training&Certification ​

Developer Hub ​

Partners ​

Marketplace ​

Find a Partner ​

Become a Partner ​

Support ​

Support & Professional Services ​

Contact us ​

Language ​

Locale ​

OpenAI compatibility ​

BASE_URL ​

Supported models ​

Call Qwen models via the OpenAI SDK ​

Prerequisites ​

Usage ​

Non-streaming call example ​

Streaming call example ​

Function call example ​

Parameters ​

Response parameters\ ​

Call with the langchain_openai SDK\ ​

Prerequisites\ ​

Call Qwen models via OpenAI API

Models

Models

GenAI Application

AI Service

AI Use Case

Overview

Why Alibaba Cloud

Customers and Insights

What's New

Products

Featured Products

Related Programs

Solutions

Pricing

Overview & Tools

Optimize your cost

Resources

Technical Resources

Training&Certification

Developer Hub

Partners

Marketplace

Find a Partner

Become a Partner

Support

Support & Professional Services

Contact us

Language

Locale

OpenAI compatibility

BASE_URL

Supported models

Call Qwen models via the OpenAI SDK

Prerequisites

Usage

Non-streaming call example

Streaming call example

Function call example

Parameters

Response parameters\

Call with the langchain_openai SDK\

Prerequisites\