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Qwen-Coder Model Capabilities
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Qwen-Coder is a language model designed for code-related tasks. Use the API to invoke the model for code generation and code completion. You can also interact with external systems through tool calling.
QuickStart
API usage prerequisites: Obtain an API key and configure your API key in environment variables (to be deprecated, merged into API key configuration). If you invoke the API through an SDK, install the OpenAI or DashScope SDK.
The following example shows how to invoke the qwen3-coder-next model to write a Python function that finds prime numbers.
OpenAI-compatible Chat Completions API
DashScope
Python
Node.js
curl
Request example
python
import os
from openai import OpenAI
client = OpenAI(
# API keys differ by region. Get your API key: https://www.alibabacloud.com/help/en/model-studio/get-api-key
# If you have not set an environment variable, replace the next line with your Model Studio API key: api_key="sk-xxx",
api_key=os.getenv("DASHSCOPE_API_KEY"),
base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
)
completion = client.chat.completions.create(
model="qwen3-coder-next",
messages=[\
{'role': 'system', 'content': 'You are a helpful assistant.'},\
{'role': 'user', 'content': 'Write a Python function find_prime_numbers that takes an integer n as input and returns a list of all prime numbers less than n. Do not output any non-code content or Markdown code blocks.'}],
)
print(completion.choices[0].message.content)Response
plaintext
def find_prime_numbers(n):
if n <= 2:
return []
primes = []
for num in range(2, n):
is_prime = True
for i in range(2, int(num ** 0.5) + 1):
if num % i == 0:
is_prime = False
break
if is_prime:
primes.append(num)
return primesRequest example
nodejs
import OpenAI from "openai";
const client = new OpenAI(
{
// API keys differ by region. Get your API key: https://www.alibabacloud.com/help/en/model-studio/get-api-key
// If you have not set an environment variable, replace the next line with your Model Studio API key: apiKey: "sk-xxx",
apiKey: process.env.DASHSCOPE_API_KEY,
// To use models in the Beijing region, change baseURL to: https://dashscope.aliyuncs.com/compatible-mode/v1
baseURL: "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1"
}
);
async function main() {
const completion = await client.chat.completions.create({
model: "qwen3-coder-next",
messages: [\
{ role: "system", content: "You are a helpful assistant." },\
{ role: "user", content: "Write a Python function find_prime_numbers that takes an integer n as input and returns a list of all prime numbers less than n. Do not output any non-code content or Markdown code blocks." }\
],
});
console.log(completion.choices[0].message.content);
}
main();Response
plaintext
def find_prime_numbers(n):
if n <= 2:
return []
primes = []
for num in range(2, n):
is_prime = True
for i in range(2, int(num ** 0.5) + 1):
if num % i == 0:
is_prime = False
break
if is_prime:
primes.append(num)
return primesRequest example
To use models in the Beijing region, change the URL to: https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions
curl
curl -X POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen3-coder-next",
"messages": [\
{\
"role": "system",\
"content": "You are a helpful assistant."\
},\
{\
"role": "user",\
"content": "Write a Python function find_prime_numbers that takes an integer n as input and returns a list of all prime numbers less than n. Do not output any non-code content or Markdown code blocks."\
}\
]
}'Response
json
{
"model": "qwen3-coder-next",
"id": "chatcmpl-3123d5cb-01b8-9a90-98cc-5bffbb369xxx",
"choices": [\
{\
"message": {\
"content": "def find_prime_numbers(n):\n if n <= 2:\n return []\n \n primes = []\n for num in range(2, n):\n is_prime = True\n for i in range(2, int(num ** 0.5) + 1):\n if num % i == 0:\n is_prime = False\n break\n if is_prime:\n primes.append(num)\n \n return primes",\
"role": "assistant"\
},\
"index": 0,\
"finish_reason": "stop"\
}\
],
"created": 1770108104,
"object": "chat.completion",
"usage": {
"total_tokens": 155,
"completion_tokens": 89,
"prompt_tokens": 66
}
}Python
Java
curl
Request example
python
import dashscope
import os
dashscope.base_http_api_url = 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1'
messages = [\
{\
"role": "system",\
"content": "You are a helpful assistant."\
},\
{\
"role": "user",\
"content": "Write a Python function find_prime_numbers that takes an integer n as a parameter and returns a list of all prime numbers less than n. Do not output non-code content or Markdown code blocks."\
}\
]
response = dashscope.Generation.call(
# API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
# If you have not configured the environment variable, replace the following line with your Model Studio API key: api_key = "sk-xxx",
api_key=os.getenv("DASHSCOPE_API_KEY"),
model="qwen3-coder-next",
messages=messages,
result_format="message"
)
if response.status_code == 200:
print(response.output.choices[0].message.content)
else:
print(f"HTTP return code: {response.status_code}")
print(f"Error code: {response.code}")
print(f"Error message: {response.message}")Response
python
def find_prime_numbers(n):
if n <= 2:
return []
primes = []
for num in range(2, n):
is_prime = True
for i in range(2, int(num ** 0.5) + 1):
if num % i == 0:
is_prime = False
break
if is_prime:
primes.append(num)
return primesRequest example
java
import java.util.Arrays;
import com.alibaba.dashscope.aigc.generation.Generation;
import com.alibaba.dashscope.aigc.generation.GenerationResult;
import com.alibaba.dashscope.aigc.generation.GenerationParam;
import com.alibaba.dashscope.common.Message;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.InputRequiredException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.protocol.Protocol;
public class Main {
public static GenerationResult callWithMessage()
throws NoApiKeyException, ApiException, InputRequiredException {
String apiKey = System.getenv("DASHSCOPE_API_KEY");
Generation gen = new Generation(Protocol.HTTP.getValue(), "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1");
Message sysMsg = Message.builder()
.role(Role.SYSTEM.getValue())
.content("You are a helpful assistant.").build();
Message userMsg = Message.builder()
.role(Role.USER.getValue())
.content("Write a Python function find_prime_numbers that takes an integer n as a parameter and returns a list of all prime numbers less than n. Do not output non-code content or Markdown code blocks.").build();
GenerationParam param = GenerationParam.builder()
.apiKey(apiKey)
.model("qwen3-coder-next")
.messages(Arrays.asList(sysMsg, userMsg))
.resultFormat(GenerationParam.ResultFormat.MESSAGE)
.build();
return gen.call(param);
}
public static void main(String[] args){
try {
GenerationResult result = callWithMessage();
System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent());
} catch (ApiException | NoApiKeyException | InputRequiredException e) {
System.err.println("Request exception: " + e.getMessage());
e.printStackTrace();
}
}
}Response
python
def find_prime_numbers(n):
if n <= 2:
return []
primes = []
for num in range(2, n):
is_prime = True
for i in range(2, int(num ** 0.5) + 1):
if num % i == 0:
is_prime = False
break
if is_prime:
primes.append(num)
return primesRequest example
To use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation
curl
curl -X POST "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1/services/aigc/text-generation/generation" \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen3-coder-next",
"input":{
"messages":[\
{\
"role": "system",\
"content": "You are a helpful assistant."\
},\
{\
"role": "user",\
"content": "Write a Python function find_prime_numbers that takes an integer n as a parameter and returns a list of all prime numbers less than n. Do not output non-code content or Markdown code blocks."\
}\
]
},
"parameters": {
"result_format": "message"
}
}'Response
json
{
"output": {
"choices": [\
{\
"message": {\
"content": "def find_prime_numbers(n):\n if n <= 2:\n return []\n \n primes = []\n for num in range(2, n):\n is_prime = True\n for i in range(2, int(num ** 0.5) + 1):\n if num % i == 0:\n is_prime = False\n break\n if is_prime:\n primes.append(num)\n \n return primes",\
"role": "assistant"\
},\
"finish_reason": "stop"\
}\
]
},
"usage": {
"total_tokens": 155,
"input_tokens": 66,
"output_tokens": 89
},
"request_id": "dd78b1cf-8029-46bb-9bea-b794ded7bxxx"
}Model Selection
Recommended choice:
qwen3-coder-nextdelivers high code quality, fast response times, and cost efficiency, making it ideal for most scenarios. It supports multi-turn tool calling, improves repository-level code understanding, and enhances stability and adaptability for Agentic coding tools.Model selection reference: We recommend using the latest general-purpose models as alternatives to Qwen-Coder models. See Model List to choose the model that best fits your scenario.
For information about model names, context, prices, snapshot versions, and more, see Model List ( Commercial Edition | Open Source Edition). For information about concurrent rate limiting, see Rate Limiting ( Commercial Edition | Open Source Edition).
Core capabilities
Tool calling
You can provide the model with a set of tools to enable interaction with external environments—for example, reading or writing files, calling APIs, or managing databases. The model determines whether and how to call these tools based on your instructions. For more information, see Function Calling.
The complete tool calling flow includes the following steps:
Define tools and send a request: Include a list of tools in your request and ask the model to complete a task that requires those tools.
Execute the tools: Parse the
tool_callsreturned by the model and call the corresponding tool functions that you have implemented locally to perform the requested task.Return the execution results: Package the tool execution results in the required format and send them back to the model. The model then uses those results to complete the final task.
The following example shows how to guide the model to generate code and save it to a local file using the write_file tool.
OpenAI compatible-Chat Completions API
DashScope
Python
Node.js
curl
python
import os
import json
from openai import OpenAI
client = OpenAI(
# API keys vary by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
# If you have not configured the environment variable, replace the following line with your Alibaba Cloud Model Studio API key: api_key="sk-xxx",
api_key=os.getenv("DASHSCOPE_API_KEY"),
base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
)
tools = [\
{\
"type": "function",\
"function": {\
"name": "write_file",\
"description": "Writes content to a specified file. Creates the file if it does not exist.",\
"parameters": {\
"type": "object",\
"properties": {\
"path": {\
"type": "string",\
"description": "The relative or absolute path of the object file"\
},\
"content": {\
"type": "string",\
"description": "The string content to write to the file"\
}\
},\
"required": ["path", "content"]\
}\
}\
}\
]
# Tool function implementation
def write_file(path: str, content: str) -> str:
"""Writes content to a file."""
try:
# Ensure the directory exists
os.makedirs(os.path.dirname(path),
exist_ok=True) if os.path.dirname(path) else None
with open(path, 'w', encoding='utf-8') as f:
f.write(content)
return f"Success: File '{path}' has been written."
except Exception as e:
return f"Error: An exception occurred while writing the file - {str(e)}"
messages = [{"role": "user", "content": "Write a Python quick sort script and name it quick_sort.py"}]
completion = client.chat.completions.create(
model="qwen3-coder-next",
messages=messages,
tools=tools
)
assistant_output = completion.choices[0].message
if assistant_output.content is None:
assistant_output.content = ""
messages.append(assistant_output)
# If no tool call is needed, output the content directly
if assistant_output.tool_calls is None:
print(f"No tool call needed. Direct response: {assistant_output.content}")
else:
# Enter the tool calling loop
while assistant_output.tool_calls is not None:
for tool_call in assistant_output.tool_calls:
tool_call_id = tool_call.id
func_name = tool_call.function.name
arguments = json.loads(tool_call.function.arguments)
print(f"Calling tool [{func_name}], arguments: {arguments}")
# Execute the tool
tool_result = write_file(**arguments)
# Construct the tool return message
tool_message = {
"role": "tool",
"tool_call_id": tool_call_id,
"content": tool_result,
}
print(f"Tool returns: {tool_message['content']}")
messages.append(tool_message)
# Call the model again to get a summarized natural language response
response = client.chat.completions.create(
model="qwen3-coder-next",
messages=messages,
tools=tools
)
assistant_output = response.choices[0].message
if assistant_output.content is None:
assistant_output.content = ""
messages.append(assistant_output)
print(f"Final model response: {assistant_output.content}")Result
plaintext
Calling tool [write_file], arguments: {'content': 'def quick_sort(arr):\\n if len(arr) <= 1:\\n return arr\\n pivot = arr[len(arr) // 2]\\n left = [x for x in arr if x < pivot]\\n middle = [x for x in arr if x == pivot]\\n right = [x for x in arr if x > pivot]\\n return quick_sort(left) + middle + quick_sort(right)\\n\\nif __name__ == \\"__main__\\":\\n example_list = [3, 6, 8, 10, 1, 2, 1]\\n print(\\"Original list:\\", example_list)\\n sorted_list = quick_sort(example_list)\\n print(\\"Sorted list:\\", sorted_list)', 'path': 'quick_sort.py'}
Tool returns: Success: File 'quick_sort.py' has been written.
Final model response: OK. I have created the file `quick_sort.py` for you, which contains the Python implementation of quick sort. You can run this file to see the sample output. Let me know if you need any further modifications or explanations!nodejs
import OpenAI from "openai";
import fs from "fs/promises";
import path from "path";
const client = new OpenAI({
// API keys vary by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
// If you have not configured the environment variable, replace the following line with your Model Studio API key: apiKey: "sk-xxx",
apiKey: process.env.DASHSCOPE_API_KEY,
baseURL: "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1"
});
const tools = [\
{\
"type": "function",\
"function": {\
"name": "write_file",\
"description": "Writes content to a specified file. Creates the file if it does not exist.",\
"parameters": {\
"type": "object",\
"properties": {\
"path": {\
"type": "string",\
"description": "The relative or absolute path of the object file"\
},\
"content": {\
"type": "string",\
"description": "The string content to write to the file"\
}\
},\
"required": ["path", "content"]\
}\
}\
}\
];
// Tool function implementation
async function write_file(filePath, content) {
try {
// For security reasons, the file writing feature is disabled by default. To use it, uncomment the following lines and ensure the path is secure.
// const dir = path.dirname(filePath);
// if (dir) {
// await fs.mkdir(dir, { recursive: true });
// }
// await fs.writeFile(filePath, content, "utf-8");
return `Success: File '${filePath}' has been written`;
} catch (error) {
return `Error: An exception occurred while writing the file - ${error.message}`;
}
}
const messages = [{"role": "user", "content": "Write a Python quick sort script and name it quick_sort.py"}];
async function main() {
const completion = await client.chat.completions.create({
model: "qwen3-coder-next",
messages: messages,
tools: tools
});
let assistant_output = completion.choices[0].message;
// Ensure content is not null
if (!assistant_output.content) assistant_output.content = "";
messages.push(assistant_output);
// If no tool call is needed, output the content directly
if (!assistant_output.tool_calls) {
console.log(`No tool call needed. Direct response: ${assistant_output.content}`);
} else {
// Enter the tool calling loop
while (assistant_output.tool_calls) {
for (const tool_call of assistant_output.tool_calls) {
const tool_call_id = tool_call.id;
const func_name = tool_call.function.name;
const args = JSON.parse(tool_call.function.arguments);
console.log(`Calling tool [${func_name}], arguments: `, args);
// Execute the tool
const tool_result = await write_file(args.path, args.content);
// Construct the tool return message
const tool_message = {
"role": "tool",
"tool_call_id": tool_call_id,
"content": tool_result
};
console.log(`Tool returns: ${tool_message.content}`);
messages.push(tool_message);
}
// Call the model again to get a summarized natural language response
const response = await client.chat.completions.create({
model: "qwen3-coder-next",
messages: messages,
tools: tools
});
assistant_output = response.choices[0].message;
if (!assistant_output.content) assistant_output.content = "";
messages.push(assistant_output);
}
console.log(`Final model response: ${assistant_output.content}`);
}
}
main();Result
plaintext
Calling tool [write_file], arguments: {
content: 'def quick_sort(arr):\\n if len(arr) <= 1:\\n return arr\\n pivot = arr[len(arr) // 2]\\n left = [x for x in arr if x < pivot]\\n middle = [x for x in arr if x == pivot]\\n right = [x for x in arr if x > pivot]\\n return quick_sort(left) + middle + quick_sort(right)\\n\\nif __name__ == \\"__main__\\":\\n example_list = [3, 6, 8, 10, 1, 2, 1]\\n print(\\"Original list:\\", example_list)\\n sorted_list = quick_sort(example_list)\\n print(\\"Sorted list:\\", sorted_list)',
path: 'quick_sort.py'
}
Tool returns: Success: File 'quick_sort.py' has been written.
Final model response: The `quick_sort.py` file has been successfully created. It contains the Python implementation of quick sort. You can run the file to view the sorting result of the sample list. Let me know if you need further modifications or explanations!This example shows the first step of the tool calling flow: sending a request and receiving the model’s intent to call a tool.
To use models in the Beijing region, change the URL to: https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions
curl
curl -X POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen3-coder-next",
"messages": [\
{\
"role": "user",\
"content": "Write a Python quick sort script and name it quick_sort.py"\
}\
],
"tools": [\
{\
"type": "function",\
"function": {\
"name": "write_file",\
"description": "Writes content to a specified file. Creates the file if it does not exist.",\
"parameters": {\
"type": "object",\
"properties": {\
"path": {\
"type": "string",\
"description": "The relative or absolute path of the object file"\
},\
"content": {\
"type": "string",\
"description": "The string content to write to the file"\
}\
},\
"required": ["path", "content"]\
}\
}\
}\
]
}'Result
json
{
"choices": [\
{\
"message": {\
"content": "",\
"role": "assistant",\
"tool_calls": [\
{\
"index": 0,\
"id": "call_0ca7505bb6e44471a40511e5",\
"type": "function",\
"function": {\
"name": "write_file",\
"arguments": "{\"content\": \"def quick_sort(arr):\\\\n if len(arr) <= 1:\\\\n return arr\\\\n pivot = arr[len(arr) // 2]\\\\n left = [x for x in arr if x < pivot]\\\\n middle = [x for x in arr if x == pivot]\\\\n right = [x for x in arr if x > pivot]\\\\n return quick_sort(left) + middle + quick_sort(right)\\\\n\\\\nif __name__ == \\\\\\\"__main__\\\\\\\":\\\\n example_list = [3, 6, 8, 10, 1, 2, 1]\\\\n print(\\\\\\\"Original list:\\\\\\\", example_list)\\\\n sorted_list = quick_sort(example_list)\\\\n print(\\\\\\\"Sorted list:\\\\\\\", sorted_list)\", \"path\": \"quick_sort.py\"}"\
}\
}\
]\
},\
"finish_reason": "tool_calls",\
"index": 0,\
"logprobs": null\
}\
],
"object": "chat.completion",
"usage": {
"prompt_tokens": 494,
"completion_tokens": 193,
"total_tokens": 687,
"prompt_tokens_details": {
"cached_tokens": 0
}
},
"created": 1761620025,
"system_fingerprint": null,
"model": "qwen3-coder-next",
"id": "chatcmpl-20e96159-beea-451f-b3a4-d13b218112b5"
}Python
Java
curl
python
import os
import json
import dashscope
dashscope.base_http_api_url = 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1'
tools = [\
{\
"type": "function",\
"function": {\
"name": "write_file",\
"description": "Writes content to a specified file. Creates the file if it does not exist.",\
"parameters": {\
"type": "object",\
"properties": {\
"path": {\
"type": "string",\
"description": "The relative or absolute path of the object file"\
},\
"content": {\
"type": "string",\
"description": "The string content to write to the file"\
}\
},\
"required": ["path", "content"]\
}\
}\
}\
]
# Tool function implementation
def write_file(path: str, content: str) -> str:
"""Writes content to a file."""
try:
# For security reasons, the file writing feature is disabled by default. To use it, uncomment the following lines and ensure the path is secure.
# os.makedirs(os.path.dirname(path),exist_ok=True) if os.path.dirname(path) else None
# with open(path, 'w', encoding='utf-8') as f:
# f.write(content)
return f"Success: File '{path}' has been written."
except Exception as e:
return f"Error: An exception occurred while writing the file - {str(e)}"
messages = [{"role": "user", "content": "Write a Python quick sort script and name it quick_sort.py"}]
response = dashscope.Generation.call(
# If you have not configured the environment variable, replace the following line with your Model Studio API key: api_key="sk-xxx",
api_key=os.getenv('DASHSCOPE_API_KEY'),
model='qwen3-coder-next',
messages=messages,
tools=tools,
result_format='message'
)
if response.status_code == 200:
assistant_output = response.output.choices[0].message
messages.append(assistant_output)
# If no tool call is needed, output the content directly
if "tool_calls" not in assistant_output or not assistant_output["tool_calls"]:
print(f"No tool call needed. Direct response: {assistant_output['content']}")
else:
# Enter the tool calling loop
while "tool_calls" in assistant_output and assistant_output["tool_calls"]:
for tool_call in assistant_output["tool_calls"]:
func_name = tool_call["function"]["name"]
arguments = json.loads(tool_call["function"]["arguments"])
tool_call_id = tool_call.get("id")
print(f"Calling tool [{func_name}], arguments: {arguments}")
# Execute the tool
tool_result = write_file(**arguments)
# Construct the tool return message
tool_message = {
"role": "tool",
"content": tool_result,
"tool_call_id": tool_call_id
}
print(f"Tool returns: {tool_message['content']}")
messages.append(tool_message)
# Call the model again to get a summarized natural language response
response = dashscope.Generation.call(
api_key=os.getenv('DASHSCOPE_API_KEY'),
model='qwen3-coder-next',
messages=messages,
tools=tools,
result_format='message'
)
if response.status_code == 200:
print(f"Final model response: {response.output.choices[0].message.content}")
assistant_output = response.output.choices[0].message
messages.append(assistant_output)
else:
print(f"Error during summary response execution: {response}")
break
else:
print(f"Execution error: {response}")Result
plaintext
Calling tool [write_file], arguments: {'content': 'def quick_sort(arr):\\n if len(arr) <= 1:\\n return arr\\n pivot = arr[len(arr) // 2]\\n left = [x for x in arr if x < pivot]\\n middle = [x for x in arr if x == pivot]\\n right = [x for x in arr if x > pivot]\\n return quick_sort(left) + middle + quick_sort(right)\\n\\nif __name__ == \\"__main__\\":\\n example_list = [3, 6, 8, 10, 1, 2, 1]\\n print(\\"Original list:\\", example_list)\\n sorted_list = quick_sort(example_list)\\n print(\\"Sorted list:\\", sorted_list)', 'path': 'quick_sort.py'}
Tool returns: Success: File 'quick_sort.py' has been written.
Final model response: The `quick_sort.py` file has been successfully created. It contains the Python implementation of quick sort. You can run the file to view the sorting result of the sample list. Let me know if you need further modifications or explanations!java
import com.alibaba.dashscope.aigc.generation.Generation;
import com.alibaba.dashscope.aigc.generation.GenerationParam;
import com.alibaba.dashscope.aigc.generation.GenerationResult;
import com.alibaba.dashscope.common.Message;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.protocol.Protocol;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.InputRequiredException;
import com.alibaba.dashscope.tools.FunctionDefinition;
import com.alibaba.dashscope.tools.ToolCallBase;
import com.alibaba.dashscope.tools.ToolCallFunction;
import com.alibaba.dashscope.tools.ToolFunction;
import com.alibaba.dashscope.utils.JsonUtils;
import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.ObjectMapper;
import java.io.File;
import java.nio.charset.StandardCharsets;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
public class Main {
/**
* Writes content to a file.
* @param arguments A JSON string passed by the model, containing the parameters required by the tool.
* @return A string representing the result after the tool is executed.
*/
public static String writeFile(String arguments) {
try {
ObjectMapper objectMapper = new ObjectMapper();
JsonNode argsNode = objectMapper.readTree(arguments);
String path = argsNode.get("path").asText();
String content = argsNode.get("content").asText();
// For security reasons, the file writing feature is disabled by default. To use it, uncomment the following lines and ensure the path is secure.
// File file = new File(path);
// File parentDir = file.getParentFile();
// if (parentDir != null && !parentDir.exists()) {
// parentDir.mkdirs();
// }
// Files.write(Paths.get(path), content.getBytes(StandardCharsets.UTF_8));
return "Success: File '" + path + "' has been written";
} catch (Exception e) {
return "Error: An exception occurred while writing the file - " + e.getMessage();
}
}
public static void main(String[] args) {
try {
// Define the tool parameter schema
String writePropertyParams =
"{\"type\":\"object\",\"properties\":{\"path\":{\"type\":\"string\",\"description\":\"The relative or absolute path of the object file\"},\"content\":{\"type\":\"string\",\"description\":\"The string content to write to the file\"}},\"required\":[\"path\",\"content\"]}";
FunctionDefinition writeFileFunction = FunctionDefinition.builder()
.name("write_file")
.description("Writes content to a specified file. Creates the file if it does not exist.")
.parameters(JsonUtils.parseString(writePropertyParams).getAsJsonObject())
.build();
Generation gen = new Generation(Protocol.HTTP.getValue(), "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1");
String userInput = "Write a Python quick sort script and name it quick_sort.py";
List<Message> messages = new ArrayList<>();
messages.add(Message.builder().role(Role.USER.getValue()).content(userInput).build());
// First call to the model
GenerationParam param = GenerationParam.builder()
.model("qwen3-coder-next")
.apiKey(System.getenv("DASHSCOPE_API_KEY"))
.messages(messages)
.tools(Arrays.asList(ToolFunction.builder().function(writeFileFunction).build()))
.resultFormat(GenerationParam.ResultFormat.MESSAGE)
.build();
GenerationResult result = gen.call(param);
Message assistantOutput = result.getOutput().getChoices().get(0).getMessage();
messages.add(assistantOutput);
// If no tool call is needed, output the content directly
if (assistantOutput.getToolCalls() == null || assistantOutput.getToolCalls().isEmpty()) {
System.out.println("No tool call needed. Direct response: " + assistantOutput.getContent());
} else {
// Enter the tool calling loop
while (assistantOutput.getToolCalls() != null && !assistantOutput.getToolCalls().isEmpty()) {
for (ToolCallBase toolCall : assistantOutput.getToolCalls()) {
ToolCallFunction functionCall = (ToolCallFunction) toolCall;
String funcName = functionCall.getFunction().getName();
String arguments = functionCall.getFunction().getArguments();
System.out.println("Calling tool [" + funcName + "], arguments: " + arguments);
// Execute the tool
String toolResult = writeFile(arguments);
// Construct the tool return message
Message toolMessage = Message.builder()
.role("tool")
.toolCallId(toolCall.getId())
.content(toolResult)
.build();
System.out.println("Tool returns: " + toolMessage.getContent());
messages.add(toolMessage);
}
// Call the model again to get a summarized natural language response
param.setMessages(messages);
result = gen.call(param);
assistantOutput = result.getOutput().getChoices().get(0).getMessage();
messages.add(assistantOutput);
}
System.out.println("Final model response: " + assistantOutput.getContent());
}
} catch (NoApiKeyException | InputRequiredException e) {
System.err.println("Error: " + e.getMessage());
} catch (Exception e) {
e.printStackTrace();
}
}
}Result
plaintext
Calling tool [write_file], arguments: {"content": "def quick_sort(arr):\\n if len(arr) <= 1:\\n return arr\\n pivot = arr[len(arr) // 2]\\n left = [x for x in arr if x < pivot]\\n middle = [x for x in arr if x == pivot]\\n right = [x for x in arr if x > pivot]\\n return quick_sort(left) + middle + quick_sort(right)\\n\\nif __name__ == \\\"__main__\\\":\\n example_array = [3, 6, 8, 10, 1, 2, 1]\\n print(\\\"Original array:\\\", example_array)\\n sorted_array = quick_sort(example_array)\\n print(\\\"Sorted array:\\\", sorted_array)", "path": "quick_sort.py"}
Tool returns: Success: File 'quick_sort.py' has been written.
Final model response: The Python code file `quick_sort.py` for quick sort has been successfully created for you. It contains a `quick_sort` function and a usage example. You can run it in your terminal or editor to test the quick sort feature.This example shows the first step of the tool calling flow: sending a request and receiving the model’s intent to call a tool.
To use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation
curl
curl --location "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1/services/aigc/text-generation/generation" \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header "Content-Type: application/json" \
--data '{
"model": "qwen3-coder-next",
"input": {
"messages": [{\
"role": "user",\
"content": "Write a Python quick sort script and name it quick_sort.py"\
}]
},
"parameters": {
"result_format": "message",
"tools": [\
{\
"type": "function",\
"function": {\
"name": "write_file",\
"description": "Writes content to a specified file. Creates the file if it does not exist.",\
"parameters": {\
"type": "object",\
"properties": {\
"path": {\
"type": "string",\
"description": "The relative or absolute path of the object file"\
},\
"content": {\
"type": "string",\
"description": "The string content to write to the file"\
}\
},\
"required": ["path", "content"]\
}\
}\
}\
]
}
}'Response
json
{
"output": {
"choices": [\
{\
"finish_reason": "tool_calls",\
"message": {\
"role": "assistant",\
"tool_calls": [\
{\
"function": {\
"name": "write_file",\
"arguments": "{\"content\": \"def quick_sort(arr):\\\\n if len(arr) <= 1:\\\\n return arr\\\\n pivot = arr[len(arr) // 2]\\\\n left = [x for x in arr if x < pivot]\\\\n middle = [x for x in arr if x == pivot]\\\\n right = [x for x in arr if x > pivot]\\\\n return quick_sort(left) + middle + quick_sort(right)\\\\n\\\\nif __name__ == \\\\\\\"__main__\\\\\\\":\\\\n example_list = [3, 6, 8, 10, 1, 2, 1]\\\\n print(\\\\\\\"Original list:\\\\\\\", example_list)\\\\n sorted_list = quick_sort(example_list)\\\\n print(\\\\\\\"Sorted list:\\\\\\\", sorted_list), \"path\": \"quick_sort.py\"}"\
},\
"index": 0,\
"id": "call_645b149bbd274e8bb3789aae",\
"type": "function"\
}\
],\
"content": ""\
}\
}\
]
},
"usage": {
"total_tokens": 684,
"output_tokens": 193,
"input_tokens": 491,
"prompt_tokens_details": {
"cached_tokens": 0
}
},
"request_id": "d2386acd-fce3-9d0f-8015-c5f3a8bf9f5c"
}Code completion
Qwen-Coder supports two code completion methods. Choose the one that best fits your needs:
Partial mode: This method works with all Qwen-Coder models and regions. It supports prefix-based completion, is simple to implement, and is recommended for most use cases.
Completions API: This method supports only the
qwen2.5-coderseries models in the Chinese mainland (Beijing) region. It supports both prefix-based and fill-in-the-middle completion.
Partial mode
This feature lets the model automatically complete the rest of your code based on the part you have already written—the prefix.
To use this feature, add a message with the role set to assistant to the messages list and set partial: true. The assistant message's content is the code prefix you provide. For more information, see Partial mode.
OpenAI compatible
DashScope
Python
Node.js
curl
Request example
python
import os
from openai import OpenAI
client = OpenAI(
# If you have not configured the environment variable, replace the following line with your Alibaba Cloud Model Studio API key: api_key="sk-xxx",
api_key=os.getenv("DASHSCOPE_API_KEY"),
base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
)
completion = client.chat.completions.create(
model="qwen3-coder-next",
messages=[{\
"role": "user",\
"content": "Please write a Python script to generate prime numbers up to 100. Do not output non-code content or Markdown code blocks."\
},\
{\
"role": "assistant",\
"content": "def generate_prime_number",\
"partial": True\
}]
)
print(completion.choices[0].message.content)Result
plaintext
(n):
primes = []
for i in range(2, n+1):
is_prime = True
for j in range(2, int(i**0.5)+1):
if i % j == 0:
is_prime = False
break
if is_prime:
primes.append(i)
return primes
prime_numbers = generate_prime_number(100)
print(prime_numbers)Request example
nodejs
import OpenAI from "openai";
const client = new OpenAI(
{
// If you have not configured the environment variable, replace the following line with your Model Studio API key: apiKey: "sk-xxx",
apiKey: process.env.DASHSCOPE_API_KEY,
baseURL: "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1"
}
);
async function main() {
const completion = await client.chat.completions.create({
model: "qwen3-coder-next",
messages: [\
{ role: "user", content: "Please write a Python script to generate prime numbers up to 100. Do not output non-code content or Markdown code blocks." },\
{ role: "assistant", content: "def generate_prime_number", partial: true}\
],
});
console.log(completion.choices[0].message.content);
}
main();Result
plaintext
(n):
primes = []
for i in range(2, n+1):
is_prime = True
for j in range(2, int(i**0.5)+1):
if i % j == 0:
is_prime = False
break
if is_prime:
primes.append(i)
return primes
prime_numbers = generate_prime_number(100)
print(prime_numbers)Request example
To use models in the Beijing region, change the URL to: https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions
curl
curl -X POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen3-coder-next",
"messages": [{\
"role": "user",\
"content": "Please write a Python script to generate prime numbers up to 100. Do not output non-code content or Markdown code blocks."\
},\
{\
"role": "assistant",\
"content": "def generate_prime_number",\
"partial": true\
}]
}'Result
json
{
"choices": [\
{\
"message": {\
"content": "(n):\n primes = []\n for num in range(2, n + 1):\n is_prime = True\n for i in range(2, int(num ** 0.5) + 1):\n if num % i == 0:\n is_prime = False\n break\n if is_prime:\n primes.append(num)\n return primes\n\nprime_numbers = generate_prime_number(100)\nprint(prime_numbers)",\
"role": "assistant"\
},\
"finish_reason": "stop",\
"index": 0,\
"logprobs": null\
}\
],
"object": "chat.completion",
"usage": {
"prompt_tokens": 38,
"completion_tokens": 93,
"total_tokens": 131,
"prompt_tokens_details": {
"cached_tokens": 0
}
},
"created": 1761634556,
"system_fingerprint": null,
"model": "qwen3-coder-next",
"id": "chatcmpl-c108050a-bb6d-4423-9d36-f64aa6a32976"
}Python
curl
Request example
python
from http import HTTPStatus
import dashscope
import os
dashscope.base_http_api_url = 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1'
messages = [{\
"role": "user",\
"content": "Please write a Python script to generate prime numbers up to 100. Do not output non-code content or Markdown code blocks."\
},\
{\
"role": "assistant",\
"content": "def generate_prime_number",\
"partial": True\
}]
response = dashscope.Generation.call(
# If you have not configured the environment variable, replace the following line with your Model Studio API key: api_key="sk-xxx",
api_key=os.getenv('DASHSCOPE_API_KEY'),
model='qwen3-coder-next',
messages=messages,
result_format='message',
)
if response.status_code == HTTPStatus.OK:
print(response.output.choices[0].message.content)
else:
print(f"HTTP return code: {response.status_code}")
print(f"Error code: {response.code}")
print(f"Error message: {response.message}")Result
plaintext
(n):
primes = []
for i in range(2, n+1):
is_prime = True
for j in range(2, int(i**0.5)+1):
if i % j == 0:
is_prime = False
break
if is_prime:
primes.append(i)
return primes
prime_numbers = generate_prime_number(100)
print(prime_numbers)Request example
To use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation
curl
curl -X POST "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1/services/aigc/text-generation/generation" \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen3-coder-next",
"input":{
"messages":[{\
"role": "user",\
"content": "Please write a Python script to generate prime numbers up to 100. Do not output non-code content or Markdown code blocks."\
},\
{\
"role": "assistant",\
"content": "def generate_prime_number",\
"partial": true\
}]
},
"parameters": {
"result_format": "message"
}
}'Result
json
{
"output": {
"choices": [\
{\
"message": {\
"content": "(n):\n prime_list = []\n for i in range(2, n+1):\n is_prime = True\n for j in range(2, int(i**0.5)+1):\n if i % j == 0:\n is_prime = False\n break\n if is_prime:\n prime_list.append(i)\n return prime_list\n\nprime_numbers = generate_prime_number(100)\nprint(prime_numbers)",\
"role": "assistant"\
},\
"finish_reason": "stop"\
}\
]
},
"usage": {
"total_tokens": 131,
"output_tokens": 92,
"input_tokens": 39,
"prompt_tokens_details": {
"cached_tokens": 0
}
},
"request_id": "9917f629-e819-4519-af44-b0e677e94b2c"
}Completions API
Important
The Completions API applies only to models in the Chinese mainland (Beijing) region. You must use an API key from the Chinese mainland (Beijing) region.
Supported models:
qwen-coder-turbo
The Completions API uses special fim (Fill-in-the-Middle) tokens in the prompt to guide the model to perform completion.
Prefix-based completion
Prefix- and suffix-based completion
Prompt template:
plaintext
<|fim_prefix|>{prefix_content}<|fim_suffix|><|fim_prefix|>and<|fim_suffix|>are special tokens that guide the model to complete the text. Do not modify them.Replace
{prefix_content}with the prefix information, such as the function name, input parameters, and usage instructions.
Python
Node.js
curl
Python
python
import os
from openai import OpenAI
client = OpenAI(
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
api_key=os.getenv("DASHSCOPE_API_KEY")
)
completion = client.completions.create(
model="qwen-coder-turbo",
prompt="<|fim_prefix|>def quick_sort(arr):<|fim_suffix|>",
)
print(completion.choices[0].text)Node.js
nodejs
import OpenAI from "openai";
const client = new OpenAI(
{
// If you have not configured the environment variable, replace the following line with your Alibaba Cloud Model Studio API key: apiKey: "sk-xxx",
apiKey: process.env.DASHSCOPE_API_KEY,
baseURL: "https://dashscope.aliyuncs.com/compatible-mode/v1"
}
);
async function main() {
const completion = await client.completions.create({
model: "qwen-coder-turbo",
prompt: "<|fim_prefix|>def quick_sort(arr):<|fim_suffix|>",
});
console.log(completion.choices[0].text)
}
main();curl
curl
curl -X POST https://dashscope.aliyuncs.com/compatible-mode/v1/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen-coder-turbo",
"prompt": "<|fim_prefix|>def quick_sort(arr):<|fim_suffix|>"
}'Prompt template:
plaintext
<|fim_prefix|>{prefix_content}<|fim_suffix|>{suffix_content}<|fim_middle|><|fim_prefix|>,<|fim_suffix|>, and<|fim_middle|>are special tokens that guide the model to complete the text. Do not modify them.Replace
{prefix_content}with the prefix information, such as the function name, input parameters, and usage instructions.Replace
{suffix_content}with the suffix information, such as the function’s return parameters.
Python
Node.js
curl
Python
python
import os
from openai import OpenAI
client = OpenAI(
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
api_key=os.getenv("DASHSCOPE_API_KEY")
)
prefix_content = """def reverse_words_with_special_chars(s):
'''
Reverses each word in a string, preserving the position of non-alphabetic characters and word order.
Example:
reverse_words_with_special_chars("Hello, world!") -> "olleH, dlrow!"
Parameters:
s (str): The input string, which may contain punctuation.
Returns:
str: The processed string with words reversed but non-alphabetic characters in their original positions.
'''
"""
suffix_content = "return result"
completion = client.completions.create(
model="qwen-coder-turbo",
prompt=f"<|fim_prefix|>{prefix_content}<|fim_suffix|>{suffix_content}<|fim_middle|>",
)
print(completion.choices[0].text)Node.js
nodejs
import OpenAI from 'openai';
const client = new OpenAI({
baseURL: "https://dashscope.aliyuncs.com/compatible-mode/v1",
apiKey: process.env.DASHSCOPE_API_KEY
});
const prefixContent = `def reverse_words_with_special_chars(s):
'''
Reverses each word in a string, preserving the position of non-alphabetic characters and word order.
Example:
reverse_words_with_special_chars("Hello, world!") -> "olleH, dlrow!"
Parameters:
s (str): The input string, which may contain punctuation.
Returns:
str: The processed string with words reversed but non-alphabetic characters in their original positions.
'''
`;
const suffixContent = "return result";
async function main() {
const completion = await client.completions.create({
model: "qwen-coder-turbo",
prompt: `<|fim_prefix|>${prefixContent}<|fim_suffix|>${suffixContent}<|fim_middle|>`
});
console.log(completion.choices[0].text);
}
main();curl
curl
curl -X POST https://dashscope.aliyuncs.com/compatible-mode/v1/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen-coder-turbo",
"prompt": "<|fim_prefix|>def reverse_words_with_special_chars(s):\n\"\"\"\nReverses each word in a string, preserving the position of non-alphabetic characters and word order.\n Example:\n reverse_words_with_special_chars(\"Hello, world!\") -> \"olleH, dlrow!\"\n Parameters:\n s (str): The input string, which may contain punctuation.\n Returns:\n str: The processed string with words reversed but non-alphabetic characters in their original positions.\n\"\"\"\n<|fim_suffix|>return result<|fim_middle|>"
}'Going live
To optimize Qwen-Coder usage efficiency and reduce costs, follow these recommendations:
Enable streaming output: Setting
stream=Truestreams intermediate results, which reduces the risk of timeouts and improves the user experience.Lower the temperature parameter: Code generation typically requires deterministic and accurate output. Reduce the
temperatureparameter to minimize randomness in generated code.Use models that support context caching: In scenarios involving large amounts of repeated prefixes—such as code completion and code review—use models that support context caching. This significantly reduces overhead.
Limit the number of tools: To ensure efficient and cost-effective model calls, pass no more than 20 tools in a single request using the
toolsparameter. Including many tool descriptions consumes excessive input tokens, increasing costs, slowing response times, and reducing the model’s ability to select the correct tool. For more information, see Function Calling.
Billing and rate limiting
Basic billing: You are billed based on the number of input Tokens and output Tokens for each request. Unit prices vary by model. For detailed pricing, see the Model List.
Special billing items:
Tiered billing: The
qwen3-coderseries uses tiered billing. When the number of input tokens for a single request reaches a specific tier, all input and output tokens for that request are billed at the unit price for that tier.Tool calling (Function Calling): When using tool calling, the tool descriptions defined in the
toolsparameter count toward the totalTokencount and incur charges.
Rate limiting: API calls are subject to dual limits: requests per minute (RPM) and tokens per minute (TPM). For more information, see or Rate limiting.
Free quota**(Singapore region only)**: The free quota is valid for 90 days, starting from the date Alibaba Cloud Model Studio is activated or your model request is approved. During this period, each Qwen-Coder model provides a free quota for new users of 1 million tokens.
API reference
For information about the input and output parameters of the Qwen code model, see Qwen.
FAQ
Why do developer tools such as Qwen Code and Claude Code use many tokens?
When an external developer tool calls the Qwen-Coder model to process issues, it may invoke the API multiple times, consuming many tokens. For guidance on monitoring and reducing token usage, see the Qwen Code and Claude Code documentation. Enable the Stop When Free Quota Is Exhausted feature to avoid unexpected charges after your free quota is used up.
You can also purchase an AI coding package, which offers a fixed monthly request quota for a flat fee and works with AI tools. For more information, see Coding Plan Overview.
How do I view model call volume?
One hour after you call a model, go to the Monitoring ( Singapore or Beijing) page. Set the query conditions, such as the time range and workspace. Then, in the Models area, find the target model and click Monitor in the Actions column to view the model's call statistics. For more information, see the Monitoring document.
Data is updated hourly. During peak periods, there may be an hour-level latency.
How can I make the model output only code without any explanatory text?
You can achieve this using the following methods:
Prompt constraints: Provide clear instructions in the prompt. For example: “Return only code. Do not include any explanations, comments, or markdown tags.”
Set a
stopsequence: Use phrases such asstop=["\n# Explanation:", "Description", "Explanation:", "Note:"]to stop the model before it starts generating explanatory text. For more information, see Qwen API Reference.
How do I use the 1,000 free calls per day for Qwen Code?
This quota is available exclusively for the Qwen Code tool and must be used through that tool. It is calculated separately from the free quota for new users you receive when you activate Alibaba Cloud Model Studio. These quotas do not conflict. For more information, see How do I use the 1,000 free calls per day?
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Is this page helpful?
QuickStart
Model Selection
Core capabilities
Tool calling
Code completion
Going live
Billing and rate limiting
API reference
FAQ
Why do developer tools such as Qwen Code and Claude Code use many tokens?
How do I view model call volume?
How can I make the model output only code without any explanatory text?
How do I use the 1,000 free calls per day for Qwen Code?
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