Visual reasoning models output their thinking process before answering. Use them for complex visual tasks: math problems, chart analysis, or video understanding.
Showcase
The component above is for demonstration purposes only and does not send a real request.
Supported models
Qwen3.7
Hybrid-thinking models: qwen3.7-plus, qwen3.7-plus-2026-05-26
Qwen3.6
Hybrid-thinking models: qwen3.6-plus, qwen3.6-plus-2026-04-02, qwen3.6-flash, qwen3.6-flash-2026-04-16, qwen3.6-35b-a3b
Qwen3.5
Hybrid-thinking models: qwen3.5-plus, qwen3.5-plus-2026-02-15, qwen3.5-flash, qwen3.5-flash-2026-02-23, qwen3.5-397b-a17b, qwen3.5-122b-a10b, qwen3.5-27b, qwen3.5-35b-a3b
Qwen3-VL
Hybrid-thinking models: qwen3-vl-plus, qwen3-vl-plus-2025-12-19, qwen3-vl-plus-2025-09-23, qwen3-vl-flash, qwen3-vl-flash-2025-10-15
Thinking-only models: qwen3-vl-235b-a22b-thinking, qwen3-vl-32b-thinking, qwen3-vl-30b-a3b-thinking, qwen3-vl-8b-thinking
QVQ
Thinking-only models: qvq-max series, qvq-plus series
Kimi
Hybrid-thinking models: kimi-k2.6, kimi-k2.5
Stepfun
Hybrid-thinking model: stepfun/step-3.7-flash
MiniMax
Hybrid-thinking model: MiniMax/MiniMax-M3
Usage guide
Thinking process: Model Studio provides two types of visual reasoning models: hybrid-thinking and thinking-only.
Hybrid-thinking models: Control thinking with the
enable_thinkingparameter:Set to
true: outputs thinking process first, then the final response (default for Qwen3.6 and Qwen3.5 series).Set to
false: outputs response directly (default forqwen3-vl-plus,qwen3-vl-flashseries).
Thinking-only models: These models always generate a thinking process before providing a response, and this behavior cannot be disabled.
Output method: Use streaming to prevent timeouts from long thinking processes.
Qwen3.6, Qwen3.5, Qwen3-VL, kimi-k2.6, kimi-k2.5 and stepfun/step-3.7-flash support both streaming and non-streaming methods.
The QVQ series supports only streaming output.
System prompt recommendations:
Single-turn/simple conversations: Do not set
System Message. Pass instructions (such as role, format) throughUser Messagefor best inference results.Complex applications (agents, tool calls): Use
System Messageto define model role, capabilities, and behavioral framework.
Getting started
Prerequisites
SDK users: install the latest version (DashScope Python SDK ≥1.24.6, DashScope Java SDK ≥2.21.10).
The following examples call qvq-max to solve a math problem from an image. These examples use streaming to print the thinking process and the final response separately.
OpenAI compatible
Python
from openai import OpenAI
import os
# Initialize the OpenAI client
client = OpenAI(
# If not configured, replace with: api_key="sk-xxx"
api_key = os.getenv("DASHSCOPE_API_KEY"),
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
)
reasoning_content = "" # Define the full thinking process
answer_content = "" # Define the full response
is_answering = False # Check if the thinking process has ended and the response has started
# Create a chat completion request
completion = client.chat.completions.create(
model="qvq-max", # Example uses qvq-max. Replace with other model names as needed.
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"
},
},
{"type": "text", "text": "How do I solve this problem?"},
],
},
],
stream=True,
# Uncomment the following to return token usage in the last chunk
# stream_options={
# "include_usage": True
# }
)
print("\n" + "=" * 20 + "Thinking process" + "=" * 20 + "\n")
for chunk in completion:
# If chunk.choices is empty, print the usage
if not chunk.choices:
print("\nUsage:")
print(chunk.usage)
else:
delta = chunk.choices[0].delta
# Print the thinking process
if hasattr(delta, 'reasoning_content') and delta.reasoning_content != None:
print(delta.reasoning_content, end='', flush=True)
reasoning_content += delta.reasoning_content
else:
# Start responding
if delta.content != "" and is_answering is False:
print("\n" + "=" * 20 + "Full response" + "=" * 20 + "\n")
is_answering = True
# Print the response process
print(delta.content, end='', flush=True)
answer_content += delta.content
# print("=" * 20 + "Full thinking process" + "=" * 20 + "\n")
# print(reasoning_content)
# print("=" * 20 + "Full response" + "=" * 20 + "\n")
# print(answer_content)Node.js
import OpenAI from "openai";
import process from 'process';
// Initialize the OpenAI client
const openai = new OpenAI({
apiKey: process.env.DASHSCOPE_API_KEY, // Read from environment variable
baseURL: 'https://dashscope.aliyuncs.com/compatible-mode/v1'
});
let reasoningContent = '';
let answerContent = '';
let isAnswering = false;
let messages = [
{
role: "user",
content: [
{ type: "image_url", image_url: { "url": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg" } },
{ type: "text", text: "Solve this problem" },
]
}]
async function main() {
try {
const stream = await openai.chat.completions.create({
model: 'qvq-max',
messages: messages,
stream: true
});
console.log('\n' + '='.repeat(20) + 'Thinking process' + '='.repeat(20) + '\n');
for await (const chunk of stream) {
if (!chunk.choices?.length) {
console.log('\nUsage:');
console.log(chunk.usage);
continue;
}
const delta = chunk.choices[0].delta;
// Handle the thinking process
if (delta.reasoning_content) {
process.stdout.write(delta.reasoning_content);
reasoningContent += delta.reasoning_content;
}
// Handle the formal response
else if (delta.content) {
if (!isAnswering) {
console.log('\n' + '='.repeat(20) + 'Full response' + '='.repeat(20) + '\n');
isAnswering = true;
}
process.stdout.write(delta.content);
answerContent += delta.content;
}
}
} catch (error) {
console.error('Error:', error);
}
}
main();HTTP
curl
curl --location 'https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
"model": "qvq-max",
"messages": [
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"
}
},
{
"type": "text",
"text": "Solve this problem"
}
]
}
],
"stream":true,
"stream_options":{"include_usage":true}
}'DashScope
Note
QVQ model via DashScope:
incremental_outputdefaults totrue(cannot disable; streaming only).result_formatdefaults to"message"(cannot be set to"text").
Python
import os
from dashscope import MultiModalConversation
messages = [
{
"role": "user",
"content": [
{"image": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"},
{"text": "How do I solve this problem?"}
]
}
]
response = MultiModalConversation.call(
# If the environment variable is not configured, replace the following line with your Model Studio API key: api_key="sk-xxx",
api_key=os.getenv('DASHSCOPE_API_KEY'),
model="qvq-max", # Example uses qvq-max. Replace with other model names as needed.
messages=messages,
stream=True,
)
# Define the full thinking process
reasoning_content = ""
# Define the full response
answer_content = ""
# Check if the thinking process has ended and the response has started
is_answering = False
print("=" * 20 + "Thinking process" + "=" * 20)
for chunk in response:
# If both the thinking process and the response are empty, ignore
message = chunk.output.choices[0].message
reasoning_content_chunk = message.get("reasoning_content", None)
if (chunk.output.choices[0].message.content == [] and
reasoning_content_chunk == ""):
pass
else:
# If it is currently the thinking process
if reasoning_content_chunk != None and chunk.output.choices[0].message.content == []:
print(chunk.output.choices[0].message.reasoning_content, end="")
reasoning_content += chunk.output.choices[0].message.reasoning_content
# If it is currently the response
elif chunk.output.choices[0].message.content != []:
if not is_answering:
print("\n" + "=" * 20 + "Full response" + "=" * 20)
is_answering = True
print(chunk.output.choices[0].message.content[0]["text"], end="")
answer_content += chunk.output.choices[0].message.content[0]["text"]
# To print the full thinking process and response, uncomment and run the following code
# print("=" * 20 + "Full thinking process" + "=" * 20 + "\n")
# print(f"{reasoning_content}")
# print("=" * 20 + "Full response" + "=" * 20 + "\n")
# print(f"{answer_content}")Java
// DashScope SDK version >= 2.19.0
import java.util.*;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import io.reactivex.Flowable;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversation;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationParam;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationResult;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.exception.InputRequiredException;
import java.lang.System;
import com.alibaba.dashscope.utils.Constants;
public class Main {
private static final Logger logger = LoggerFactory.getLogger(Main.class);
private static StringBuilder reasoningContent = new StringBuilder();
private static StringBuilder finalContent = new StringBuilder();
private static boolean isFirstPrint = true;
private static void handleGenerationResult(MultiModalConversationResult message) {
String re = message.getOutput().getChoices().get(0).getMessage().getReasoningContent();
String reasoning = Objects.isNull(re)?"":re; // Default value
List<Map<String, Object>> content = message.getOutput().getChoices().get(0).getMessage().getContent();
if (!reasoning.isEmpty()) {
reasoningContent.append(reasoning);
if (isFirstPrint) {
System.out.println("====================Thinking process====================");
isFirstPrint = false;
}
System.out.print(reasoning);
}
if (Objects.nonNull(content) && !content.isEmpty()) {
Object text = content.get(0).get("text");
finalContent.append(content.get(0).get("text"));
if (!isFirstPrint) {
System.out.println("\n====================Full response====================");
isFirstPrint = true;
}
System.out.print(text);
}
}
public static MultiModalConversationParam buildMultiModalConversationParam(MultiModalMessage Msg) {
return MultiModalConversationParam.builder()
// If not configured, replace with: .apiKey("sk-xxx")
.apiKey(System.getenv("DASHSCOPE_API_KEY"))
// Example uses qvq-max. Replace with other model names as needed.
.model("qvq-max")
.messages(Arrays.asList(Msg))
.incrementalOutput(true)
.build();
}
public static void streamCallWithMessage(MultiModalConversation conv, MultiModalMessage Msg)
throws NoApiKeyException, ApiException, InputRequiredException, UploadFileException {
MultiModalConversationParam param = buildMultiModalConversationParam(Msg);
Flowable<MultiModalConversationResult> result = conv.streamCall(param);
result.blockingForEach(message -> {
handleGenerationResult(message);
});
}
public static void main(String[] args) {
try {
MultiModalConversation conv = new MultiModalConversation();
MultiModalMessage userMsg = MultiModalMessage.builder()
.role(Role.USER.getValue())
.content(Arrays.asList(Collections.singletonMap("image", "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"),
Collections.singletonMap("text", "Solve this problem")))
.build();
streamCallWithMessage(conv, userMsg);
// Print the final result
// if (reasoningContent.length() > 0) {
// System.out.println("\n====================Full response====================");
// System.out.println(finalContent.toString());
// }
} catch (ApiException | NoApiKeyException | UploadFileException | InputRequiredException e) {
logger.error("An exception occurred: {}", e.getMessage());
}
System.exit(0);
}
}HTTP
curl
curl -X POST https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H 'Content-Type: application/json' \
-H 'X-DashScope-SSE: enable' \
-d '{
"model": "qvq-max",
"input":{
"messages":[
{
"role": "user",
"content": [
{"image": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"},
{"text": "Solve this problem"}
]
}
]
}
}'Core capabilities
Enable or disable the thinking process
For scenarios requiring detailed thinking (problem-solving, report analysis), enable thinking mode using the enable_thinking parameter as shown below.
OpenAI compatible
enable_thinking and thinking_budget are non-standard OpenAI parameters. The parameter passing method varies by language:
Python SDK: You must pass them through the
extra_bodydictionary.Node.js SDK: You can pass them directly as top-level parameters.
from openai import OpenAI
import os
# Initialize the OpenAI client
client = OpenAI(
# If you have not configured an environment variable, replace the following line with your Alibaba Cloud Model Studio API key: api_key="sk-xxx",
# API keys differ by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
api_key=os.getenv("DASHSCOPE_API_KEY"),
# The following is the base URL for the Beijing region. If you are using a model in the US (Virginia) region, replace the base_url with https://dashscope-us.aliyuncs.com/compatible-mode/v1
# The URL below is for the China (Beijing) region. URLs vary by region.
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)
reasoning_content = "" # Define the full thinking process
answer_content = "" # Define the full response
is_answering = False # Check if the thinking process has ended and the response has started
enable_thinking = True
# Create a chat completion request
completion = client.chat.completions.create(
model="qwen3.5-plus",
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"
},
},
{"type": "text", "text": "How do I solve this problem?"},
],
},
],
stream=True,
# The enable_thinking parameter enables the thinking process. The thinking_budget parameter sets the maximum number of tokens for the reasoning process.
# For qwen3.5-plus, qwen3-vl-plus, and qwen3-vl-flash, you can use enable_thinking to enable or disable thinking (qwen3.5-plus is enabled by default). For models with the 'thinking' suffix, such as qwen3-vl-235b-a22b-thinking, enable_thinking can only be set to true. This parameter does not apply to other Qwen-VL models.
extra_body={
'enable_thinking': enable_thinking},
# Uncomment the following to return token usage in the last chunk
# stream_options={
# "include_usage": True
# }
)
if enable_thinking:
print("\n" + "=" * 20 + "Thinking process" + "=" * 20 + "\n")
for chunk in completion:
# If chunk.choices is empty, print the usage
if not chunk.choices:
print("\nUsage:")
print(chunk.usage)
else:
delta = chunk.choices[0].delta
# Print the thinking process
if hasattr(delta, 'reasoning_content') and delta.reasoning_content != None:
print(delta.reasoning_content, end='', flush=True)
reasoning_content += delta.reasoning_content
else:
# Start responding
if delta.content != "" and is_answering is False:
print("\n" + "=" * 20 + "Full response" + "=" * 20 + "\n")
is_answering = True
# Print the response process
print(delta.content, end='', flush=True)
answer_content += delta.content
# print("=" * 20 + "Full thinking process" + "=" * 20 + "\n")
# print(reasoning_content)
# print("=" * 20 + "Full response" + "=" * 20 + "\n")
# print(answer_content)import OpenAI from "openai";
// Initialize the OpenAI client
const openai = new OpenAI({
// If no environment variable configured: api_key="sk-xxx",
// API keys differ by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
apiKey: process.env.DASHSCOPE_API_KEY,
// The following is the base URL for the Beijing region. If you are using a model in the US (Virginia) region, replace the base_url with https://dashscope-us.aliyuncs.com/compatible-mode/v1
// The URL below is for the China (Beijing) region. URLs vary by region.
baseURL: 'https://dashscope.aliyuncs.com/compatible-mode/v1'
});
let reasoningContent = '';
let answerContent = '';
let isAnswering = false;
let enableThinking = true;
let messages = [
{
role: "user",
content: [
{ type: "image_url", image_url: { "url": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg" } },
{ type: "text", text: "Solve this problem" },
]
}]
async function main() {
try {
const stream = await openai.chat.completions.create({
model: 'qwen3.5-plus',
messages: messages,
stream: true,
// Note: In Node.js SDK, non-standard parameters (like enableThinking) pass as top-level properties, not in extra_body.
enable_thinking: enableThinking
});
if (enableThinking){console.log('\n' + '='.repeat(20) + 'Thinking process' + '='.repeat(20) + '\n');}
for await (const chunk of stream) {
if (!chunk.choices?.length) {
console.log('\nUsage:');
console.log(chunk.usage);
continue;
}
const delta = chunk.choices[0].delta;
// Handle the thinking process
if (delta.reasoning_content) {
process.stdout.write(delta.reasoning_content);
reasoningContent += delta.reasoning_content;
}
// Handle the formal response
else if (delta.content) {
if (!isAnswering) {
console.log('\n' + '='.repeat(20) + 'Full response' + '='.repeat(20) + '\n');
isAnswering = true;
}
process.stdout.write(delta.content);
answerContent += delta.content;
}
}
} catch (error) {
console.error('Error:', error);
}
}
main();# ======= IMPORTANT =======
# API keys differ by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
# The following is the base URL for the Beijing region. If you are using a model in the US (Virginia) region, replace the base_url with https://dashscope-us.aliyuncs.com/compatible-mode/v1/chat/completions
# The URL below is for the China (Beijing) region. URLs vary by region.
# === Delete this comment before execution ===
curl --location 'https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
"model": "qwen3.5-plus",
"messages": [
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"
}
},
{
"type": "text",
"text": "Solve this problem"
}
]
}
],
"stream":true,
"stream_options":{"include_usage":true},
"enable_thinking": true
}'DashScope
import os
import dashscope
from dashscope import MultiModalConversation
# The following is the base URL for the Beijing region. If you are using a model in the US (Virginia) region, replace the base_url with https://dashscope-us.aliyuncs.com/api/v1
# The URL below is for the China (Beijing) region. URLs vary by region.
dashscope.base_http_api_url = "https://dashscope.aliyuncs.com/api/v1"
enable_thinking=True
messages = [
{
"role": "user",
"content": [
{"image": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"},
{"text": "How do I solve this problem?"}
]
}
]
response = MultiModalConversation.call(
# If not configured, replace with: api_key="sk-xxx",
api_key=os.getenv('DASHSCOPE_API_KEY'),
model="qwen3.5-plus",
messages=messages,
stream=True,
# The enable_thinking parameter enables the thinking process.
# For qwen3.5-plus, qwen3-vl-plus, and qwen3-vl-flash, you can use enable_thinking to enable or disable thinking (qwen3.5-plus is enabled by default). For models with the 'thinking' suffix, such as qwen3-vl-235b-a22b-thinking, enable_thinking can only be set to true. This parameter does not apply to other visual reasoning models.
enable_thinking=enable_thinking
)
# Define the full thinking process
reasoning_content = ""
# Define the full response
answer_content = ""
# Check if the thinking process has ended and the response has started
is_answering = False
if enable_thinking:
print("=" * 20 + "Thinking process" + "=" * 20)
for chunk in response:
# If both the thinking process and the response are empty, ignore
message = chunk.output.choices[0].message
reasoning_content_chunk = message.get("reasoning_content", None)
if (chunk.output.choices[0].message.content == [] and
reasoning_content_chunk == ""):
pass
else:
# If it is currently the thinking process
if reasoning_content_chunk != None and chunk.output.choices[0].message.content == []:
print(chunk.output.choices[0].message.reasoning_content, end="")
reasoning_content += chunk.output.choices[0].message.reasoning_content
# If it is currently the response
elif chunk.output.choices[0].message.content != []:
if not is_answering:
print("\n" + "=" * 20 + "Full response" + "=" * 20)
is_answering = True
print(chunk.output.choices[0].message.content[0]["text"], end="")
answer_content += chunk.output.choices[0].message.content[0]["text"]
# To print the full thinking process and response, uncomment and run the following code
# print("=" * 20 + "Full thinking process" + "=" * 20 + "\n")
# print(f"{reasoning_content}")
# print("=" * 20 + "Full response" + "=" * 20 + "\n")
# print(f"{answer_content}")// DashScope SDK version >= 2.21.10
import java.util.*;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import io.reactivex.Flowable;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversation;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationParam;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationResult;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.exception.InputRequiredException;
import java.lang.System;
import com.alibaba.dashscope.utils.Constants;
public class Main {
// The following is the base URL for the Beijing region. If you are using a model in the US (Virginia) region, replace the base_url with https://dashscope-us.aliyuncs.com/api/v1
// The URL below is for the China (Beijing) region. URLs vary by region.
static {Constants.baseHttpApiUrl="https://dashscope.aliyuncs.com/api/v1";}
private static final Logger logger = LoggerFactory.getLogger(Main.class);
private static StringBuilder reasoningContent = new StringBuilder();
private static StringBuilder finalContent = new StringBuilder();
private static boolean isFirstPrint = true;
private static void handleGenerationResult(MultiModalConversationResult message) {
String re = message.getOutput().getChoices().get(0).getMessage().getReasoningContent();
String reasoning = Objects.isNull(re)?"":re; // Default value
List<Map<String, Object>> content = message.getOutput().getChoices().get(0).getMessage().getContent();
if (!reasoning.isEmpty()) {
reasoningContent.append(reasoning);
if (isFirstPrint) {
System.out.println("====================Thinking process====================");
isFirstPrint = false;
}
System.out.print(reasoning);
}
if (Objects.nonNull(content) && !content.isEmpty()) {
Object text = content.get(0).get("text");
finalContent.append(content.get(0).get("text"));
if (!isFirstPrint) {
System.out.println("\n====================Full response====================");
isFirstPrint = true;
}
System.out.print(text);
}
}
public static MultiModalConversationParam buildMultiModalConversationParam(MultiModalMessage Msg) {
return MultiModalConversationParam.builder()
// If not configured, replace with: .apiKey("sk-xxx")
// API keys differ by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
.apiKey(System.getenv("DASHSCOPE_API_KEY"))
.model("qwen3.5-plus")
.messages(Arrays.asList(Msg))
.enableThinking(true)
.incrementalOutput(true)
.build();
}
public static void streamCallWithMessage(MultiModalConversation conv, MultiModalMessage Msg)
throws NoApiKeyException, ApiException, InputRequiredException, UploadFileException {
MultiModalConversationParam param = buildMultiModalConversationParam(Msg);
Flowable<MultiModalConversationResult> result = conv.streamCall(param);
result.blockingForEach(message -> {
handleGenerationResult(message);
});
}
public static void main(String[] args) {
try {
MultiModalConversation conv = new MultiModalConversation();
MultiModalMessage userMsg = MultiModalMessage.builder()
.role(Role.USER.getValue())
.content(Arrays.asList(Collections.singletonMap("image", "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"),
Collections.singletonMap("text", "Solve this problem")))
.build();
streamCallWithMessage(conv, userMsg);
// Print the final result
// if (reasoningContent.length() > 0) {
// System.out.println("\n====================Full response====================");
// System.out.println(finalContent.toString());
// }
} catch (ApiException | NoApiKeyException | UploadFileException | InputRequiredException e) {
logger.error("An exception occurred: {}", e.getMessage());
}
System.exit(0);
}
}# ======= IMPORTANT =======
# API keys differ by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
# The following is the base URL for the Beijing region. If you are using a model in the US (Virginia) region, replace the base_url with: https://dashscope-us.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation
# The URL below is for the China (Beijing) region. URLs vary by region.
# === Delete this comment before execution ===
curl -X POST https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H 'Content-Type: application/json' \
-H 'X-DashScope-SSE: enable' \
-d '{
"model": "qwen3.5-plus",
"input":{
"messages":[
{
"role": "user",
"content": [
{"image": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"},
{"text": "Solve this problem"}
]
}
]
},
"parameters":{
"enable_thinking": true,
"incremental_output": true
}
}'Limit thinking length
Use the thinking_budget parameter to limit thinking process token length. If exceeded, the content is truncated and the model immediately generates the final answer. The default value is the model's maximum chain-of-thought length. For more information, see Model list.
Important
The thinking_budget parameter is supported by Qwen3.6, Qwen3.5, Qwen3-VL (thinking mode), kimi-k2.5 (thinking mode) and kimi-k2.6 (thinking mode) .
OpenAI compatible
thinking_budget is a non-standard OpenAI parameter. When using the OpenAI Python SDK, pass it through extra_body.
from openai import OpenAI
import os
# Initialize the OpenAI client
client = OpenAI(
# If you have not configured an environment variable, replace the following line with your Alibaba Cloud Model Studio API key: api_key="sk-xxx",
# API keys differ by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
api_key=os.getenv("DASHSCOPE_API_KEY"),
# The following is the base URL for the Beijing region. If you are using a model in the US (Virginia) region, replace the base_url with https://dashscope-us.aliyuncs.com/compatible-mode/v1
# The URL below is for the China (Beijing) region. URLs vary by region.
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)
reasoning_content = "" # Define the full thinking process
answer_content = "" # Define the full response
is_answering = False # Check if the thinking process has ended and the response has started
enable_thinking = True
# Create a chat completion request
completion = client.chat.completions.create(
model="qwen3.5-plus",
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"
},
},
{"type": "text", "text": "How do I solve this problem?"},
],
},
],
stream=True,
# The enable_thinking parameter enables the thinking process. The thinking_budget parameter sets the maximum number of tokens for the reasoning process.
# For qwen3.5-plus, qwen3-vl-plus, and qwen3-vl-flash, you can use enable_thinking to enable or disable thinking (qwen3.5-plus is enabled by default). For models with the 'thinking' suffix, such as qwen3-vl-235b-a22b-thinking, enable_thinking can only be set to true. This parameter does not apply to other Qwen-VL models.
extra_body={
'enable_thinking': enable_thinking,
"thinking_budget": 81920},
# Uncomment the following to return token usage in the last chunk
# stream_options={
# "include_usage": True
# }
)
if enable_thinking:
print("\n" + "=" * 20 + "Thinking process" + "=" * 20 + "\n")
for chunk in completion:
# If chunk.choices is empty, print the usage
if not chunk.choices:
print("\nUsage:")
print(chunk.usage)
else:
delta = chunk.choices[0].delta
# Print the thinking process
if hasattr(delta, 'reasoning_content') and delta.reasoning_content != None:
print(delta.reasoning_content, end='', flush=True)
reasoning_content += delta.reasoning_content
else:
# Start responding
if delta.content != "" and is_answering is False:
print("\n" + "=" * 20 + "Full response" + "=" * 20 + "\n")
is_answering = True
# Print the response process
print(delta.content, end='', flush=True)
answer_content += delta.content
# print("=" * 20 + "Full thinking process" + "=" * 20 + "\n")
# print(reasoning_content)
# print("=" * 20 + "Full response" + "=" * 20 + "\n")
# print(answer_content)import OpenAI from "openai";
// Initialize the OpenAI client
const openai = new OpenAI({
// If no environment variable configured: api_key="sk-xxx",
// API keys differ by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
apiKey: process.env.DASHSCOPE_API_KEY,
// The following is the base URL for the Beijing region. If you are using a model in the US (Virginia) region, replace the base_url with https://dashscope-us.aliyuncs.com/compatible-mode/v1
// The URL below is for the China (Beijing) region. URLs vary by region.
baseURL: 'https://dashscope.aliyuncs.com/compatible-mode/v1'
});
let reasoningContent = '';
let answerContent = '';
let isAnswering = false;
let enableThinking = true;
let messages = [
{
role: "user",
content: [
{ type: "image_url", image_url: { "url": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg" } },
{ type: "text", text: "Solve this problem" },
]
}]
async function main() {
try {
const stream = await openai.chat.completions.create({
model: 'qwen3.5-plus',
messages: messages,
stream: true,
// Note: In Node.js SDK, non-standard parameters (like enableThinking) pass as top-level properties, not in extra_body.
enable_thinking: enableThinking,
thinking_budget: 81920
});
if (enableThinking){console.log('\n' + '='.repeat(20) + 'Thinking process' + '='.repeat(20) + '\n');}
for await (const chunk of stream) {
if (!chunk.choices?.length) {
console.log('\nUsage:');
console.log(chunk.usage);
continue;
}
const delta = chunk.choices[0].delta;
// Handle the thinking process
if (delta.reasoning_content) {
process.stdout.write(delta.reasoning_content);
reasoningContent += delta.reasoning_content;
}
// Handle the formal response
else if (delta.content) {
if (!isAnswering) {
console.log('\n' + '='.repeat(20) + 'Full response' + '='.repeat(20) + '\n');
isAnswering = true;
}
process.stdout.write(delta.content);
answerContent += delta.content;
}
}
} catch (error) {
console.error('Error:', error);
}
}
main();# ======= IMPORTANT =======
# API keys differ by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
# The following is the base URL for the Beijing region. If you are using a model in the US (Virginia) region, replace the base_url with https://dashscope-us.aliyuncs.com/compatible-mode/v1/chat/completions
# The URL below is for the China (Beijing) region. URLs vary by region.
# === Delete this comment before execution ===
curl --location 'https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
"model": "qwen3.5-plus",
"messages": [
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"
}
},
{
"type": "text",
"text": "Solve this problem"
}
]
}
],
"stream":true,
"stream_options":{"include_usage":true},
"enable_thinking": true,
"thinking_budget": 81920
}'DashScope
import os
import dashscope
from dashscope import MultiModalConversation
# The following is the base URL for the Beijing region. If you are using a model in the US (Virginia) region, replace the base_url with https://dashscope-us.aliyuncs.com/api/v1
# The URL below is for the China (Beijing) region. URLs vary by region.
dashscope.base_http_api_url = "https://dashscope.aliyuncs.com/api/v1"
enable_thinking=True
messages = [
{
"role": "user",
"content": [
{"image": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"},
{"text": "How do I solve this problem?"}
]
}
]
response = MultiModalConversation.call(
# If not configured, replace with: api_key="sk-xxx",
api_key=os.getenv('DASHSCOPE_API_KEY'),
model="qwen3.5-plus",
messages=messages,
stream=True,
# The enable_thinking parameter enables the thinking process.
# For qwen3.5-plus, qwen3-vl-plus, and qwen3-vl-flash, you can use enable_thinking to enable or disable thinking (qwen3.5-plus is enabled by default). For models with the 'thinking' suffix, such as qwen3-vl-235b-a22b-thinking, enable_thinking can only be set to true. This parameter does not apply to other Qwen-VL models.
enable_thinking=enable_thinking,
# The thinking_budget parameter sets the maximum number of tokens for the reasoning process.
thinking_budget=81920,
)
# Define the full thinking process
reasoning_content = ""
# Define the full response
answer_content = ""
# Check if the thinking process has ended and the response has started
is_answering = False
if enable_thinking:
print("=" * 20 + "Thinking process" + "=" * 20)
for chunk in response:
# If both the thinking process and the response are empty, ignore
message = chunk.output.choices[0].message
reasoning_content_chunk = message.get("reasoning_content", None)
if (chunk.output.choices[0].message.content == [] and
reasoning_content_chunk == ""):
pass
else:
# If it is currently the thinking process
if reasoning_content_chunk != None and chunk.output.choices[0].message.content == []:
print(chunk.output.choices[0].message.reasoning_content, end="")
reasoning_content += chunk.output.choices[0].message.reasoning_content
# If it is currently the response
elif chunk.output.choices[0].message.content != []:
if not is_answering:
print("\n" + "=" * 20 + "Full response" + "=" * 20)
is_answering = True
print(chunk.output.choices[0].message.content[0]["text"], end="")
answer_content += chunk.output.choices[0].message.content[0]["text"]
# To print the full thinking process and response, uncomment and run the following code
# print("=" * 20 + "Full thinking process" + "=" * 20 + "\n")
# print(f"{reasoning_content}")
# print("=" * 20 + "Full response" + "=" * 20 + "\n")
# print(f"{answer_content}")// DashScope SDK version >= 2.21.10
import java.util.*;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import io.reactivex.Flowable;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversation;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationParam;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationResult;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.exception.InputRequiredException;
import java.lang.System;
import com.alibaba.dashscope.utils.Constants;
public class Main {
// The following is the base URL for the Beijing region. If you are using a model in the US (Virginia) region, replace the base_url with https://dashscope-us.aliyuncs.com/api/v1
// The URL below is for the China (Beijing) region. URLs vary by region.
static {Constants.baseHttpApiUrl="https://dashscope.aliyuncs.com/api/v1";}
private static final Logger logger = LoggerFactory.getLogger(Main.class);
private static StringBuilder reasoningContent = new StringBuilder();
private static StringBuilder finalContent = new StringBuilder();
private static boolean isFirstPrint = true;
private static void handleGenerationResult(MultiModalConversationResult message) {
String re = message.getOutput().getChoices().get(0).getMessage().getReasoningContent();
String reasoning = Objects.isNull(re)?"":re; // Default value
List<Map<String, Object>> content = message.getOutput().getChoices().get(0).getMessage().getContent();
if (!reasoning.isEmpty()) {
reasoningContent.append(reasoning);
if (isFirstPrint) {
System.out.println("====================Thinking process====================");
isFirstPrint = false;
}
System.out.print(reasoning);
}
if (Objects.nonNull(content) && !content.isEmpty()) {
Object text = content.get(0).get("text");
finalContent.append(content.get(0).get("text"));
if (!isFirstPrint) {
System.out.println("\n====================Full response====================");
isFirstPrint = true;
}
System.out.print(text);
}
}
public static MultiModalConversationParam buildMultiModalConversationParam(MultiModalMessage Msg) {
return MultiModalConversationParam.builder()
// If not configured, replace with: .apiKey("sk-xxx")
// API keys differ by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
.apiKey(System.getenv("DASHSCOPE_API_KEY"))
.model("qwen3.5-plus")
.messages(Arrays.asList(Msg))
.enableThinking(true)
.thinkingBudget(81920)
.incrementalOutput(true)
.build();
}
public static void streamCallWithMessage(MultiModalConversation conv, MultiModalMessage Msg)
throws NoApiKeyException, ApiException, InputRequiredException, UploadFileException {
MultiModalConversationParam param = buildMultiModalConversationParam(Msg);
Flowable<MultiModalConversationResult> result = conv.streamCall(param);
result.blockingForEach(message -> {
handleGenerationResult(message);
});
}
public static void main(String[] args) {
try {
MultiModalConversation conv = new MultiModalConversation();
MultiModalMessage userMsg = MultiModalMessage.builder()
.role(Role.USER.getValue())
.content(Arrays.asList(Collections.singletonMap("image", "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"),
Collections.singletonMap("text", "Solve this problem")))
.build();
streamCallWithMessage(conv, userMsg);
// Print the final result
// if (reasoningContent.length() > 0) {
// System.out.println("\n====================Full response====================");
// System.out.println(finalContent.toString());
// }
} catch (ApiException | NoApiKeyException | UploadFileException | InputRequiredException e) {
logger.error("An exception occurred: {}", e.getMessage());
}
System.exit(0);
}
}# ======= IMPORTANT =======
# API keys differ by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
# The following is the base URL for the Beijing region. If you are using a model in the US (Virginia) region, replace the base_url with: https://dashscope-us.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation
# The URL below is for the China (Beijing) region. URLs vary by region.
# === Delete this comment before execution ===
curl -X POST https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H 'Content-Type: application/json' \
-H 'X-DashScope-SSE: enable' \
-d '{
"model": "qwen3.5-plus",
"input":{
"messages":[
{
"role": "user",
"content": [
{"image": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"},
{"text": "Solve this problem"}
]
}
]
},
"parameters":{
"enable_thinking": true,
"incremental_output": true,
"thinking_budget": 81920
}
}'More examples
Visual reasoning models support all visual understanding features for complex scenarios such as:
Billing
Total cost = (Input tokens × Input price per token) + (Output tokens × Output price per token).
Thinking process (
reasoning_content) is billed as output tokens. If there is no thinking output, the non-thinking mode price applies.For token calculation for images/videos, see Image and video understanding.
API reference
For the input and output parameters, see Text generation.
Error codes
If the model call fails and returns an error message, see Error codes for resolution.
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