Qwen-OCR is a visual understanding model designed for text extraction. It extracts text and parses structured data from various images, such as scanned documents, tables, and receipts. It supports multiple languages and can perform advanced functions, including information extraction, table parsing, and formula recognition, using specific task instructions.
You can try Qwen-OCR online in the Playground (Singapore or Beijing).
Examples
Input image | Recognition result |
Recognize multiple languages
|
|
Recognize skewed images
| Product Introduction Imported fiber filaments from South Korea. 6941990612023 Item No.: 2023 |
Locate text position
High-precision recognition task supports text localization. | Visualization of localization
For more information, see the FAQ on how to draw the bounding box of each text line onto the original image. |
Models and pricing
International (Singapore)
Model | Version | Context window | Max input | Max output | Input cost | Output cost | Free quota |
(Tokens) | (Million tokens) | ||||||
qwen-vl-ocr | Stable | 34,096 | 30,000 Up to 30,000 per image | 4096 | $0.72 | $0.72 | 1 million tokens each Valid for 90 days after activation |
qwen-vl-ocr-2025-11-20 Also qwen-vl-ocr-1120 Based on Qwen3-VL. Significantly improves document parsing and text localization. | Snapshot | 38,192 | 8,192 | $0.07 | $0.16 | ||
Mainland China (Beijing)
Model | Version | Context window | Max input | Max output | Input cost | Output cost | Free quota |
(Tokens) | (Million tokens) | ||||||
qwen-vl-ocr Currenlty the same capabilities as qwen-vl-ocr-2025-08-28 | Stable | 34,096 | 30,000 Up to 30,000 per image | 4,096 | $0.717 | $0.717 | No free quota |
qwen-vl-ocr-latest Always the same capabilities as the latest snapshot | Latest | 38,192 | 8,192 | $0.043 | $0.072 | ||
qwen-vl-ocr-2025-11-20 Also qwen-vl-ocr-1120 Based on Qwen3-VL. Significantly improves document parsing and text localization. | Snapshot | ||||||
qwen-vl-ocr-2025-08-28 Also qwen-vl-ocr-0828 | 34,096 | 4,096 | $0.717 | $0.717 | |||
qwen-vl-ocr-2025-04-13 Also qwen-vl-ocr-0413 | |||||||
qwen-vl-ocr-2024-10-28 Also qwen-vl-ocr-1028 | |||||||
qwen-vl-ocr, qwen-vl-ocr-2025-04-13, and qwen-vl-ocr-2025-08-28models, themax_tokensparameter (maximum output length) defaults to 4096. To increase this value to a range of 4097 to 8192, send an email to modelstudio@service.aliyun.com and include the following information: your Alibaba Cloud account ID, image type (such as document images, e-commerce images, or contracts), model name, estimated QPS and total daily requests, and the percentage of requests where the model output exceeds 4096 tokens.
Preparations
Create an API key and export the API key as an environment variable.
Before you call the model using the OpenAI SDK or DashScope SDK, install the latest version of the SDK. The minimum required version is 1.22.2 for the DashScope Python SDK and 2.21.8 for the Java SDK.
DashScope SDK
Advantages: Supports all advanced features, such as automatic image rotation and built-in OCR tasks. It offers comprehensive functionality and a simpler method to call the model.
Scenarios: Ideal for projects that require full functionality.
OpenAI compatible SDK
Advantages: Convenient for users who are already using the OpenAI SDK or its ecosystem tools, which allows for quick migration.
Limits: Advanced features (automatic image rotation and built-in OCR tasks) cannot be invoked directly using parameters. You must manually simulate these features by constructing complex prompts and parse the output results yourself.
Scenarios: Ideal for projects that already have an OpenAI integration and do not rely on advanced features that are exclusive to DashScope.
Getting started
The following example extracts key information from a train ticket image (URL) and returns it in JSON format. For more information, see how to pass a local file and image limitations.
OpenAI compatible
Python
from openai import OpenAI
import os
PROMPT_TICKET_EXTRACTION = """
Please extract the invoice number, train number, departure station, destination station, departure date and time, seat number, seat type, ticket price, ID card number, and passenger name from the train ticket image.
Extract the key information accurately. Do not omit information or fabricate false information. Replace any single character that is blurry or obscured by glare with a question mark (?).
Return the data in JSON format: {'Invoice Number': 'xxx', 'Train Number': 'xxx', 'Departure Station': 'xxx', 'Destination Station': 'xxx', 'Departure Date and Time': 'xxx', 'Seat Number': 'xxx', 'Seat Type': 'xxx', 'Ticket Price': 'xxx', 'ID Card Number': 'xxx', 'Passenger Name': 'xxx'}
"""
try:
client = OpenAI(
# The API keys for the Singapore and Beijing regions are different. For more information, see https://www.alibabacloud.com/help/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"),
# The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/compatible-mode/v1
base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)
completion = client.chat.completions.create(
model="qwen-vl-ocr-2025-11-20",
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {"url":"https://img.alicdn.com/imgextra/i2/O1CN01ktT8451iQutqReELT_!!6000000004408-0-tps-689-487.jpg"},
# The minimum pixel threshold for the input image. If the image is smaller than this value, it is scaled up until the total pixels exceed min_pixels.
"min_pixels": 32 * 32 * 3,
# The maximum pixel threshold for the input image. If the image is larger than this value, it is scaled down until the total pixels are below max_pixels.
"max_pixels": 32 * 32 * 8192
},
# The model supports passing a prompt in the text field. If no prompt is passed, the default prompt is used: Please output only the text content from the image without any additional descriptions or formatting.
{"type": "text",
"text": PROMPT_TICKET_EXTRACTION}
]
}
])
print(completion.choices[0].message.content)
except Exception as e:
print(f"Error message: {e}")Node.js
import OpenAI from 'openai';
// Define the prompt to extract train ticket information.
const PROMPT_TICKET_EXTRACTION = `
Please extract the invoice number, train number, departure station, destination station, departure date and time, seat number, seat type, ticket price, ID card number, and passenger name from the train ticket image.
Extract the key information accurately. Do not omit information or fabricate false information. Replace any single character that is blurry or obscured by glare with a question mark (?).
Return the data in JSON format: {'Invoice Number': 'xxx', 'Train Number': 'xxx', 'Departure Station': 'xxx', 'Destination Station': 'xxx', 'Departure Date and Time': 'xxx', 'Seat Number': 'xxx', 'Seat Type': 'xxx', 'Ticket Price': 'xxx', 'ID Card Number': 'xxx', 'Passenger Name': 'xxx'}
`;
const openai = new OpenAI({
// The API keys for the Singapore and Beijing regions are different. For more information, see https://www.alibabacloud.com/help/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,
// The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/compatible-mode/v1
baseURL: 'https://dashscope-intl.aliyuncs.com/compatible-mode/v1',
});
async function main() {
const response = await openai.chat.completions.create({
model: 'qwen-vl-ocr-2025-11-20',
messages: [
{
role: 'user',
content: [
// The model supports passing a prompt in the following text field. If no prompt is passed, the default prompt is used: Please output only the text content from the image without any additional descriptions or formatting.
{ type: 'text', text: PROMPT_TICKET_EXTRACTION},
{
type: 'image_url',
image_url: {
url: 'https://img.alicdn.com/imgextra/i2/O1CN01ktT8451iQutqReELT_!!6000000004408-0-tps-689-487.jpg',
},
// The minimum pixel threshold for the input image. If the image is smaller than this value, it is scaled up until the total pixels exceed min_pixels.
min_pixels: 32 * 32 * 3,
// The maximum pixel threshold for the input image. If the image is larger than this value, it is scaled down until the total pixels are below max_pixels.
max_pixels: 32 * 32 * 8192
}
]
}
],
});
console.log(response.choices[0].message.content)
}
main();curl
# ======= Important =======
# The API keys for the Singapore and Beijing regions are different. For more information, see https://www.alibabacloud.com/help/model-studio/get-api-key.
# The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions
# === Delete this comment before running ===
curl -X POST https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen-vl-ocr-2025-11-20",
"messages": [
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {"url":"https://img.alicdn.com/imgextra/i2/O1CN01ktT8451iQutqReELT_!!6000000004408-0-tps-689-487.jpg"},
"min_pixels": 3072,
"max_pixels": 8388608
},
{"type": "text", "text": "Please extract the invoice number, train number, departure station, destination station, departure date and time, seat number, seat type, ticket price, ID card number, and passenger name from the train ticket image. Extract the key information accurately. Do not omit information or fabricate false information. Replace any single character that is blurry or obscured by glare with a question mark (?). Return the data in JSON format: {'Invoice Number': 'xxx', 'Train Number': 'xxx', 'Departure Station': 'xxx', 'Destination Station': 'xxx', 'Departure Date and Time': 'xxx', 'Seat Number': 'xxx', 'Seat Type': 'xxx', 'Ticket Price': 'xxx', 'ID Card Number': 'xxx', 'Passenger Name': 'xxx'}"}
]
}
]
}'Example response
DashScope
Python
import os
import dashscope
PROMPT_TICKET_EXTRACTION = """
Please extract the invoice number, train number, departure station, destination station, departure date and time, seat number, seat type, ticket price, ID card number, and passenger name from the train ticket image.
Extract the key information accurately. Do not omit information or fabricate false information. Replace any single character that is blurry or obscured by glare with a question mark (?).
Return the data in JSON format: {'Invoice Number': 'xxx', 'Train Number': 'xxx', 'Departure Station': 'xxx', 'Destination Station': 'xxx', 'Departure Date and Time': 'xxx', 'Seat Number': 'xxx', 'Seat Type': 'xxx', 'Ticket Price': 'xxx', 'ID Card Number': 'xxx', 'Passenger Name': 'xxx'}
"""
# The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'
messages = [{
"role": "user",
"content": [{
"image": "https://img.alicdn.com/imgextra/i2/O1CN01ktT8451iQutqReELT_!!6000000004408-0-tps-689-487.jpg",
# The minimum pixel threshold for the input image. If the image is smaller than this value, it is scaled up until the total pixels exceed min_pixels.
"min_pixels": 32 * 32 * 3,
# The maximum pixel threshold for the input image. If the image is larger than this value, it is scaled down until the total pixels are below max_pixels.
"max_pixels": 32 * 32 * 8192,
# Enables automatic image rotation.
"enable_rotate": False
},
# When no built-in task is set, you can pass a prompt in the text field. If no prompt is passed, the default prompt is used: Please output only the text content from the image without any additional descriptions or formatting.
{"type": "text", "text": PROMPT_TICKET_EXTRACTION}]
}]
try:
response = dashscope.MultiModalConversation.call(
# The API keys for the Singapore and Beijing regions are different. For more information, see https://www.alibabacloud.com/help/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='qwen-vl-ocr-2025-11-20',
messages=messages
)
print(response["output"]["choices"][0]["message"].content[0]["text"])
except Exception as e:
print(f"An error occurred: {e}")Java
import java.util.Arrays;
import java.util.Collections;
import java.util.Map;
import java.util.HashMap;
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.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.utils.Constants;
public class Main {
static {
// The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1
Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
}
public static void simpleMultiModalConversationCall()
throws ApiException, NoApiKeyException, UploadFileException {
MultiModalConversation conv = new MultiModalConversation();
Map<String, Object> map = new HashMap<>();
map.put("image", "https://img.alicdn.com/imgextra/i2/O1CN01ktT8451iQutqReELT_!!6000000004408-0-tps-689-487.jpg");
// The maximum pixel threshold for the input image. If the image is larger than this value, it is scaled down until the total pixels are below max_pixels.
map.put("max_pixels", 8388608);
// The minimum pixel threshold for the input image. If the image is smaller than this value, it is scaled up until the total pixels exceed min_pixels.
map.put("min_pixels", 3072);
// Enables automatic image rotation.
map.put("enable_rotate", false);
MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
.content(Arrays.asList(
map,
// When no built-in task is set, you can pass a prompt in the text field. If no prompt is passed, the default prompt is used: Please output only the text content from the image without any additional descriptions or formatting.
Collections.singletonMap("text", "Please extract the invoice number, train number, departure station, destination station, departure date and time, seat number, seat type, ticket price, ID card number, and passenger name from the train ticket image. Extract the key information accurately. Do not omit information or fabricate false information. Replace any single character that is blurry or obscured by glare with a question mark (?). Return the data in JSON format: {'Invoice Number': 'xxx', 'Train Number': 'xxx', 'Departure Station': 'xxx', 'Destination Station': 'xxx', 'Departure Date and Time': 'xxx', 'Seat Number': 'xxx', 'Seat Type': 'xxx', 'Ticket Price': 'xxx', 'ID Card Number': 'xxx', 'Passenger Name': 'xxx'}"))).build();
MultiModalConversationParam param = MultiModalConversationParam.builder()
// The API keys for the Singapore and Beijing regions are different. For more information, see https://www.alibabacloud.com/help/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(System.getenv("DASHSCOPE_API_KEY"))
.model("qwen-vl-ocr-2025-11-20")
.message(userMessage)
.build();
MultiModalConversationResult result = conv.call(param);
System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text"));
}
public static void main(String[] args) {
try {
simpleMultiModalConversationCall();
} catch (ApiException | NoApiKeyException | UploadFileException e) {
System.out.println(e.getMessage());
}
System.exit(0);
}
}curl
# ======= Important =======
# The API keys for the Singapore and Beijing regions are different. For more information, see https://www.alibabacloud.com/help/model-studio/get-api-key.
# The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation
# === Delete this comment before running ===
curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation'\
--header "Authorization: Bearer $DASHSCOPE_API_KEY"\
--header 'Content-Type: application/json'\
--data '{
"model": "qwen-vl-ocr-2025-11-20",
"input": {
"messages": [
{
"role": "user",
"content": [{
"image": "https://img.alicdn.com/imgextra/i2/O1CN01ktT8451iQutqReELT_!!6000000004408-0-tps-689-487.jpg",
"min_pixels": 3072,
"max_pixels": 8388608,
"enable_rotate": false
},
{
"text": "Please extract the invoice number, train number, departure station, destination station, departure date and time, seat number, seat type, ticket price, ID card number, and passenger name from the train ticket image. Extract the key information accurately. Do not omit information or fabricate false information. Replace any single character that is blurry or obscured by glare with a question mark (?). Return the data in JSON format: {'Invoice Number': 'xxx', 'Train Number': 'xxx', 'Departure Station': 'xxx', 'Destination Station': 'xxx', 'Departure Date and Time': 'xxx', 'Seat Number': 'xxx', 'Seat Type': 'xxx', 'Ticket Price': 'xxx', 'ID Card Number': 'xxx', 'Passenger Name': 'xxx'}"
}
]
}
]
}
}'Use built-in tasks
To simplify calls in specific scenarios, the models (except for qwen-vl-ocr-2024-10-28) include several built-in tasks.
How to use:
DashScope SDK: You do not need to design and pass a
Prompt. The model uses a fixedPromptinternally. You can set theocr_optionsparameter to invoke the built-in task.OpenAI compatible SDK: You must manually enter the
Promptspecified for the task.
The following table lists the value of task, the specified Prompt, the output format, and an example for each built-in task:
High-precision recognition
We recommend that you use model versions later than qwen-vl-ocr-2025-08-28 or the latest version to invoke the high-precision recognition task. This task has the following attributes:
Recognizes text content (extracts text)
Detects text position (locates text lines and outputs coordinates)
For more information about how to draw the bounding box on the original image after you obtain the coordinates, see the FAQ.
Value of task | Specified prompt | Output format and example |
| Locate all text lines and return the coordinates of the rotated rectangle |
|
import os
import dashscope
# The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'
messages = [{
"role": "user",
"content": [{
"image": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/ctdzex/biaozhun.jpg",
# The minimum pixel threshold for the input image. If the image is smaller than this value, it is scaled up until the total pixels exceed min_pixels.
"min_pixels": 32 * 32 * 3,
# The maximum pixel threshold for the input image. If the image is larger than this value, it is scaled down until the total pixels are below max_pixels.
"max_pixels": 32 * 32 * 8192,
# Enables automatic image rotation.
"enable_rotate": False}]
}]
response = dashscope.MultiModalConversation.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='qwen-vl-ocr-2025-11-20',
messages=messages,
# Set the built-in task to high-precision recognition.
ocr_options={"task": "advanced_recognition"}
)
# The multi-language recognition task returns the result as plain text.
print(response["output"]["choices"][0]["message"].content[0]["text"])// dashscope SDK version >= 2.21.8
import java.util.Arrays;
import java.util.Collections;
import java.util.Map;
import java.util.HashMap;
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.aigc.multimodalconversation.OcrOptions;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.utils.Constants;
public class Main {
static {
// The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1
Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
}
public static void simpleMultiModalConversationCall()
throws ApiException, NoApiKeyException, UploadFileException {
MultiModalConversation conv = new MultiModalConversation();
Map<String, Object> map = new HashMap<>();
map.put("image", "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/ctdzex/biaozhun.jpg");
// The maximum pixel threshold for the input image. If the image is larger than this value, it is scaled down until the total pixels are below max_pixels.
map.put("max_pixels", 8388608);
// The minimum pixel threshold for the input image. If the image is smaller than this value, it is scaled up until the total pixels exceed min_pixels.
map.put("min_pixels", 3072);
// Enables automatic image rotation.
map.put("enable_rotate", false);
// Configure the built-in OCR task.
OcrOptions ocrOptions = OcrOptions.builder()
.task(OcrOptions.Task.ADVANCED_RECOGNITION)
.build();
MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
.content(Arrays.asList(
map
)).build();
MultiModalConversationParam param = MultiModalConversationParam.builder()
// If you have not configured the environment variable, replace the following line with your Model Studio API key: .apiKey("sk-xxx")
.apiKey(System.getenv("DASHSCOPE_API_KEY"))
.model("qwen-vl-ocr-2025-11-20")
.message(userMessage)
.ocrOptions(ocrOptions)
.build();
MultiModalConversationResult result = conv.call(param);
System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text"));
}
public static void main(String[] args) {
try {
simpleMultiModalConversationCall();
} catch (ApiException | NoApiKeyException | UploadFileException e) {
System.out.println(e.getMessage());
}
System.exit(0);
}
}# ======= Important =======
# The API keys for the Singapore and Beijing regions are different. For more information, see https://www.alibabacloud.com/help/model-studio/get-api-key.
# The following is the base URL for the Singapore region. If you use a model in the Singapore region, replace the base_url with: https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation
# === Delete this comment before running ===
curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '
{
"model": "qwen-vl-ocr-2025-11-20",
"input": {
"messages": [
{
"role": "user",
"content": [
{
"image": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/ctdzex/biaozhun.jpg",
"min_pixels": 3072,
"max_pixels": 8388608,
"enable_rotate": false
}
]
}
]
},
"parameters": {
"ocr_options": {
"task": "advanced_recognition"
}
}
}
'Information extraction
The model supports extracting structured information from documents such as receipts, certificates, and forms, and returns the results in JSON format. You can choose between two modes:
Custom field extraction: You can specify a custom JSON template (
result_schema) in theocr_options.task_configparameter. This template defines the specific field names (key) to be extracted. The model automatically populates the corresponding values (value). The template supports up to three levels of nesting.Full field extraction: If the
result_schemaparameter is not specified, the model extracts all fields from the image.
The prompts for the two modes are different:
Value of task | Specified prompt | Output format and example |
| Custom field extraction: |
|
Full field extraction: |
|
The following are code examples for calling the model using the DashScope SDK and HTTP:
# use [pip install -U dashscope] to update sdk
import os
import dashscope
# The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'
messages = [
{
"role":"user",
"content":[
{
"image":"http://duguang-labelling.oss-cn-shanghai.aliyuncs.com/demo_ocr/receipt_zh_demo.jpg",
"min_pixels": 3072,
"max_pixels": 8388608,
"enable_rotate": False
}
]
}
]
params = {
"ocr_options":{
"task": "key_information_extraction",
"task_config": {
"result_schema": {
"Ride Date": "Corresponds to the ride date and time in the image, in the format YYYY-MM-DD, for example, 2025-03-05",
"Invoice Code": "Extract the invoice code from the image, usually a combination of numbers or letters",
"Invoice Number": "Extract the number from the invoice, usually composed of only digits."
}
}
}
}
response = dashscope.MultiModalConversation.call(
# The API keys for the Singapore and Beijing regions are different. For more information, see https://www.alibabacloud.com/help/model-studio/get-api-key.
api_key=os.getenv('DASHSCOPE_API_KEY'),
model='qwen-vl-ocr-2025-11-20',
messages=messages,
**params)
print(response.output.choices[0].message.content[0]["ocr_result"])import java.util.Arrays;
import java.util.Collections;
import java.util.Map;
import java.util.HashMap;
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.aigc.multimodalconversation.OcrOptions;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
import com.google.gson.JsonObject;
import com.alibaba.dashscope.utils.Constants;
public class Main {
static {
// The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1
Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
}
public static void simpleMultiModalConversationCall()
throws ApiException, NoApiKeyException, UploadFileException {
MultiModalConversation conv = new MultiModalConversation();
Map<String, Object> map = new HashMap<>();
map.put("image", "http://duguang-labelling.oss-cn-shanghai.aliyuncs.com/demo_ocr/receipt_zh_demo.jpg");
// The maximum pixel threshold for the input image. If the image is larger than this value, it is scaled down until the total pixels are below max_pixels.
map.put("max_pixels", 8388608);
// The minimum pixel threshold for the input image. If the image is smaller than this value, it is scaled up until the total pixels exceed min_pixels.
map.put("min_pixels", 3072);
// Enables automatic image rotation.
map.put("enable_rotate", false);
MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
.content(Arrays.asList(
map
)).build();
// Create the main JSON object.
JsonObject resultSchema = new JsonObject();
resultSchema.addProperty("Ride Date", "Corresponds to the ride date and time in the image, in the format YYYY-MM-DD, for example, 2025-03-05");
resultSchema.addProperty("Invoice Code", "Extract the invoice code from the image, usually a combination of numbers or letters");
resultSchema.addProperty("Invoice Number", "Extract the number from the invoice, usually composed of only digits.");
// Configure the built-in OCR task.
OcrOptions ocrOptions = OcrOptions.builder()
.task(OcrOptions.Task.KEY_INFORMATION_EXTRACTION)
.taskConfig(OcrOptions.TaskConfig.builder()
.resultSchema(resultSchema)
.build())
.build();
MultiModalConversationParam param = MultiModalConversationParam.builder()
// The API keys for the Singapore and Beijing regions are different. For more information, see https://www.alibabacloud.com/help/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(System.getenv("DASHSCOPE_API_KEY"))
.model("qwen-vl-ocr-2025-11-20")
.message(userMessage)
.ocrOptions(ocrOptions)
.build();
MultiModalConversationResult result = conv.call(param);
System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("ocr_result"));
}
public static void main(String[] args) {
try {
simpleMultiModalConversationCall();
} catch (ApiException | NoApiKeyException | UploadFileException e) {
System.out.println(e.getMessage());
}
System.exit(0);
}
}# ======= Important =======
# The API keys for the Singapore and Beijing regions are different. For more information, see https://www.alibabacloud.com/help/model-studio/get-api-key.
# The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation
# === Delete this comment before running ===
curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '
{
"model": "qwen-vl-ocr-2025-11-20",
"input": {
"messages": [
{
"role": "user",
"content": [
{
"image": "http://duguang-labelling.oss-cn-shanghai.aliyuncs.com/demo_ocr/receipt_zh_demo.jpg",
"min_pixels": 3072,
"max_pixels": 8388608,
"enable_rotate": false
}
]
}
]
},
"parameters": {
"ocr_options": {
"task": "key_information_extraction",
"task_config": {
"result_schema": {
"Ride Date": "Corresponds to the ride date and time in the image, in the format YYYY-MM-DD, for example, 2025-03-05",
"Invoice Code": "Extract the invoice code from the image, usually a combination of numbers or letters",
"Invoice Number": "Extract the number from the invoice, usually composed of only digits."
}
}
}
}
}
'If you use the OpenAI SDK or HTTP methods, you must append the custom JSON schema to the end of the prompt string. For more information, see the following code example:
Table parsing
The model parses the table elements in the image and returns the recognition result as text in HTML format.
Value of task | Specified prompt | Output format and example |
|
|
|
The following are code examples for calling the model using the DashScope SDK and HTTP:
import os
import dashscope
# The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'
messages = [{
"role": "user",
"content": [{
"image": "http://duguang-llm.oss-cn-hangzhou.aliyuncs.com/llm_data_keeper/data/doc_parsing/tables/photo/eng/17.jpg",
# The minimum pixel threshold for the input image. If the image is smaller than this value, it is scaled up until the total pixels exceed min_pixels.
"min_pixels": 32 * 32 * 3,
# The maximum pixel threshold for the input image. If the image is larger than this value, it is scaled down until the total pixels are below max_pixels.
"max_pixels": 32 * 32 * 8192,
# Enables automatic image rotation.
"enable_rotate": False}]
}]
response = dashscope.MultiModalConversation.call(
# The API keys for the Singapore and Beijing regions are different. For more information, see https://www.alibabacloud.com/help/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='qwen-vl-ocr-2025-11-20',
messages=messages,
# Set the built-in task to table parsing.
ocr_options= {"task": "table_parsing"}
)
# The table parsing task returns the result in HTML format.
print(response["output"]["choices"][0]["message"].content[0]["text"])import java.util.Arrays;
import java.util.Collections;
import java.util.Map;
import java.util.HashMap;
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.aigc.multimodalconversation.OcrOptions;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.utils.Constants;
public class Main {
static {
// The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1
Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
}
public static void simpleMultiModalConversationCall()
throws ApiException, NoApiKeyException, UploadFileException {
MultiModalConversation conv = new MultiModalConversation();
Map<String, Object> map = new HashMap<>();
map.put("image", "https://duguang-llm.oss-cn-hangzhou.aliyuncs.com/llm_data_keeper/data/doc_parsing/tables/photo/eng/17.jpg");
// The maximum pixel threshold for the input image. If the image is larger than this value, it is scaled down until the total pixels are below max_pixels.
map.put("max_pixels", 8388608);
// The minimum pixel threshold for the input image. If the image is smaller than this value, it is scaled up until the total pixels exceed min_pixels.
map.put("min_pixels",3072);
// Enables automatic image rotation.
map.put("enable_rotate", false);
// Configure the built-in OCR task.
OcrOptions ocrOptions = OcrOptions.builder()
.task(OcrOptions.Task.TABLE_PARSING)
.build();
MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
.content(Arrays.asList(
map
)).build();
MultiModalConversationParam param = MultiModalConversationParam.builder()
// The API keys for the Singapore and Beijing regions are different. For more information, see https://www.alibabacloud.com/help/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(System.getenv("DASHSCOPE_API_KEY"))
.model("qwen-vl-ocr-2025-11-20")
.message(userMessage)
.ocrOptions(ocrOptions)
.build();
MultiModalConversationResult result = conv.call(param);
System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text"));
}
public static void main(String[] args) {
try {
simpleMultiModalConversationCall();
} catch (ApiException | NoApiKeyException | UploadFileException e) {
System.out.println(e.getMessage());
}
System.exit(0);
}
}# ======= Important =======
# The API keys for the Singapore and Beijing regions are different. For more information, see https://www.alibabacloud.com/help/model-studio/get-api-key.
# The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation
# === Delete this comment before running ===
curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '
{
"model": "qwen-vl-ocr-2025-11-20",
"input": {
"messages": [
{
"role": "user",
"content": [
{
"image": "http://duguang-llm.oss-cn-hangzhou.aliyuncs.com/llm_data_keeper/data/doc_parsing/tables/photo/eng/17.jpg",
"min_pixels": 3072,
"max_pixels": 8388608,
"enable_rotate": false
}
]
}
]
},
"parameters": {
"ocr_options": {
"task": "table_parsing"
}
}
}
'Document parsing
The model can parse scanned documents or PDF documents that are stored as images. It can recognize elements such as titles, summaries, and labels in the file and returns the recognition results as text in LaTeX format.
Task value | Specified prompt | Output format and example |
|
|
|
The following are code examples for calling the model using the DashScope SDK and HTTP:
import os
import dashscope
# The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'
messages = [{
"role": "user",
"content": [{
"image": "https://img.alicdn.com/imgextra/i1/O1CN01ukECva1cisjyK6ZDK_!!6000000003635-0-tps-1500-1734.jpg",
# The minimum pixel threshold for the input image. If the image is smaller than this value, it is scaled up until the total pixels exceed min_pixels.
"min_pixels": 32 * 32 * 3,
# The maximum pixel threshold for the input image. If the image is larger than this value, it is scaled down until the total pixels are below max_pixels.
"max_pixels": 32 * 32 * 8192,
# Enables automatic image rotation.
"enable_rotate": False}]
}]
response = dashscope.MultiModalConversation.call(
# The API keys for the Singapore and Beijing regions are different. For more information, see https://www.alibabacloud.com/help/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='qwen-vl-ocr-2025-11-20',
messages=messages,
# Set the built-in task to document parsing.
ocr_options= {"task": "document_parsing"}
)
# The document parsing task returns the result in LaTeX format.
print(response["output"]["choices"][0]["message"].content[0]["text"])import java.util.Arrays;
import java.util.Collections;
import java.util.Map;
import java.util.HashMap;
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.aigc.multimodalconversation.OcrOptions;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.utils.Constants;
public class Main {
static {
// The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1
Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
}
public static void simpleMultiModalConversationCall()
throws ApiException, NoApiKeyException, UploadFileException {
MultiModalConversation conv = new MultiModalConversation();
Map<String, Object> map = new HashMap<>();
map.put("image", "https://img.alicdn.com/imgextra/i1/O1CN01ukECva1cisjyK6ZDK_!!6000000003635-0-tps-1500-1734.jpg");
// The maximum pixel threshold for the input image. If the image is larger than this value, it is scaled down until the total pixels are below max_pixels.
map.put("max_pixels", 8388608);
// The minimum pixel threshold for the input image. If the image is smaller than this value, it is scaled up until the total pixels exceed min_pixels.
map.put("min_pixels", 3072);
// Enables automatic image rotation.
map.put("enable_rotate", false);
// Configure the built-in OCR task.
OcrOptions ocrOptions = OcrOptions.builder()
.task(OcrOptions.Task.DOCUMENT_PARSING)
.build();
MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
.content(Arrays.asList(
map
)).build();
MultiModalConversationParam param = MultiModalConversationParam.builder()
// The API keys for the Singapore and Beijing regions are different. For more information, see https://www.alibabacloud.com/help/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(System.getenv("DASHSCOPE_API_KEY"))
.model("qwen-vl-ocr-2025-11-20")
.message(userMessage)
.ocrOptions(ocrOptions)
.build();
MultiModalConversationResult result = conv.call(param);
System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text"));
}
public static void main(String[] args) {
try {
simpleMultiModalConversationCall();
} catch (ApiException | NoApiKeyException | UploadFileException e) {
System.out.println(e.getMessage());
}
System.exit(0);
}
}# ======= Important =======
# The API keys for the Singapore and Beijing regions are different. For more information, see https://www.alibabacloud.com/help/model-studio/get-api-key.
# The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation
# === Delete this comment before running ===
curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation'\
--header "Authorization: Bearer $DASHSCOPE_API_KEY"\
--header 'Content-Type: application/json'\
--data '{
"model": "qwen-vl-ocr-2025-11-20",
"input": {
"messages": [
{
"role": "user",
"content": [{
"image": "https://img.alicdn.com/imgextra/i1/O1CN01ukECva1cisjyK6ZDK_!!6000000003635-0-tps-1500-1734.jpg",
"min_pixels": 3072,
"max_pixels": 8388608,
"enable_rotate": false
}
]
}
]
},
"parameters": {
"ocr_options": {
"task": "document_parsing"
}
}
}
'Formula recognition
The model can parse formulas in images and returns the recognition results as text in LaTeX format.
Value of task | Specified prompt | Output format and example |
|
|
|
The following are code examples for calling the model using the DashScope SDK and HTTP:
import os
import dashscope
# The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'
messages = [{
"role": "user",
"content": [{
"image": "http://duguang-llm.oss-cn-hangzhou.aliyuncs.com/llm_data_keeper/data/formula_handwriting/test/inline_5_4.jpg",
# The minimum pixel threshold for the input image. If the image is smaller than this value, it is scaled up until the total pixels exceed min_pixels.
"min_pixels": 32 * 32 * 3,
# The maximum pixel threshold for the input image. If the image is larger than this value, it is scaled down until the total pixels are below max_pixels.
"max_pixels": 32 * 32 * 8192,
# Enables automatic image rotation.
"enable_rotate": False}]
}]
response = dashscope.MultiModalConversation.call(
# The API keys for the Singapore and Beijing regions are different. For more information, see https://www.alibabacloud.com/help/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='qwen-vl-ocr-2025-11-20',
messages=messages,
# Set the built-in task to formula recognition.
ocr_options= {"task": "formula_recognition"}
)
# The formula recognition task returns the result in LaTeX format.
print(response["output"]["choices"][0]["message"].content[0]["text"])import java.util.Arrays;
import java.util.Collections;
import java.util.Map;
import java.util.HashMap;
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.aigc.multimodalconversation.OcrOptions;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.utils.Constants;
public class Main {
static {
// The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1
Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
}
public static void simpleMultiModalConversationCall()
throws ApiException, NoApiKeyException, UploadFileException {
MultiModalConversation conv = new MultiModalConversation();
Map<String, Object> map = new HashMap<>();
map.put("image", "http://duguang-llm.oss-cn-hangzhou.aliyuncs.com/llm_data_keeper/data/formula_handwriting/test/inline_5_4.jpg");
// The maximum pixel threshold for the input image. If the image is larger than this value, it is scaled down until the total pixels are below max_pixels.
map.put("max_pixels", 8388608);
// The minimum pixel threshold for the input image. If the image is smaller than this value, it is scaled up until the total pixels exceed min_pixels.
map.put("min_pixels", 3072);
// Enables automatic image rotation.
map.put("enable_rotate", false);
// Configure the built-in OCR task.
OcrOptions ocrOptions = OcrOptions.builder()
.task(OcrOptions.Task.FORMULA_RECOGNITION)
.build();
MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
.content(Arrays.asList(
map
)).build();
MultiModalConversationParam param = MultiModalConversationParam.builder()
// The API keys for the Singapore and Beijing regions are different. For more information, see https://www.alibabacloud.com/help/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(System.getenv("DASHSCOPE_API_KEY"))
.model("qwen-vl-ocr-2025-11-20")
.message(userMessage)
.ocrOptions(ocrOptions)
.build();
MultiModalConversationResult result = conv.call(param);
System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text"));
}
public static void main(String[] args) {
try {
simpleMultiModalConversationCall();
} catch (ApiException | NoApiKeyException | UploadFileException e) {
System.out.println(e.getMessage());
}
System.exit(0);
}
}# ======= Important =======
# The API keys for the Singapore and Beijing regions are different. For more information, see https://www.alibabacloud.com/help/model-studio/get-api-key.
# The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation
# === Delete this comment before running ===
curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '
{
"model": "qwen-vl-ocr",
"input": {
"messages": [
{
"role": "user",
"content": [
{
"image": "http://duguang-llm.oss-cn-hangzhou.aliyuncs.com/llm_data_keeper/data/formula_handwriting/test/inline_5_4.jpg",
"min_pixels": 3072,
"max_pixels": 8388608,
"enable_rotate": false
}
]
}
]
},
"parameters": {
"ocr_options": {
"task": "formula_recognition"
}
}
}
'General text recognition
General text recognition is primarily used for Chinese and English scenarios and returns recognition results in plain text format.
Value of task | Specified prompt | Output format and example |
|
|
|
The following are code examples for making calls using the DashScope SDK and HTTP:
import os
import dashscope
# The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'
messages = [{
"role": "user",
"content": [{
"image": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/ctdzex/biaozhun.jpg",
# The minimum pixel threshold for the input image. If the image is smaller than this value, it is scaled up until the total pixels exceed min_pixels.
"min_pixels": 32 * 32 * 3,
# The maximum pixel threshold for the input image. If the image is larger than this value, it is scaled down until the total pixels are below max_pixels.
"max_pixels": 32 * 32 * 8192,
# Enables automatic image rotation.
"enable_rotate": False}]
}]
response = dashscope.MultiModalConversation.call(
# The API keys for the Singapore and Beijing regions are different. For more information, see https://www.alibabacloud.com/help/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='qwen-vl-ocr-2025-11-20',
messages=messages,
# Set the built-in task to general text recognition.
ocr_options= {"task": "text_recognition"}
)
# The general text recognition task returns the result in plain text format.
print(response["output"]["choices"][0]["message"].content[0]["text"])import java.util.Arrays;
import java.util.Collections;
import java.util.Map;
import java.util.HashMap;
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.aigc.multimodalconversation.OcrOptions;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.utils.Constants;
public class Main {
static {
// The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1
Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
}
public static void simpleMultiModalConversationCall()
throws ApiException, NoApiKeyException, UploadFileException {
MultiModalConversation conv = new MultiModalConversation();
Map<String, Object> map = new HashMap<>();
map.put("image", "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/ctdzex/biaozhun.jpg");
// The maximum pixel threshold for the input image. If the image is larger than this value, it is scaled down until the total pixels are below max_pixels.
map.put("max_pixels", 8388608);
// The minimum pixel threshold for the input image. If the image is smaller than this value, it is scaled up until the total pixels exceed min_pixels.
map.put("min_pixels", 3072);
// Enables automatic image rotation.
map.put("enable_rotate", false);
// Configure the built-in task.
OcrOptions ocrOptions = OcrOptions.builder()
.task(OcrOptions.Task.TEXT_RECOGNITION)
.build();
MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
.content(Arrays.asList(
map
)).build();
MultiModalConversationParam param = MultiModalConversationParam.builder()
// The API keys for the Singapore and Beijing regions are different. For more information, see https://www.alibabacloud.com/help/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(System.getenv("DASHSCOPE_API_KEY"))
.model("qwen-vl-ocr-2025-11-20")
.message(userMessage)
.ocrOptions(ocrOptions)
.build();
MultiModalConversationResult result = conv.call(param);
System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text"));
}
public static void main(String[] args) {
try {
simpleMultiModalConversationCall();
} catch (ApiException | NoApiKeyException | UploadFileException e) {
System.out.println(e.getMessage());
}
System.exit(0);
}
}# ======= Important =======
# The API keys for the Singapore and Beijing regions are different. For more information, see https://www.alibabacloud.com/help/model-studio/get-api-key.
# The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation
# === Delete this comment before running ===
curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation'\
--header "Authorization: Bearer $DASHSCOPE_API_KEY"\
--header 'Content-Type: application/json'\
--data '{
"model": "qwen-vl-ocr-2025-11-20",
"input": {
"messages": [
{
"role": "user",
"content": [{
"image": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/ctdzex/biaozhun.jpg",
"min_pixels": 3072,
"max_pixels": 8388608,
"enable_rotate": false
}
]
}
]
},
"parameters": {
"ocr_options": {
"task": "text_recognition"
}
}
}'Multilingual recognition
Multilingual recognition is used for scenarios that involve languages other than Chinese and English. Supported languages are Arabic, French, German, Italian, Japanese, Korean, Portuguese, Russian, Spanish, and Vietnamese. The recognition results are returned in plain text format.
Value of task | Specified prompt | Output format and example |
|
|
|
The following are code examples for making calls using the DashScope SDK and HTTP:
import os
import dashscope
# The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'
messages = [{
"role": "user",
"content": [{
"image": "https://img.alicdn.com/imgextra/i2/O1CN01VvUMNP1yq8YvkSDFY_!!6000000006629-2-tps-6000-3000.png",
# The minimum pixel threshold for the input image. If an image is smaller than this threshold, it is scaled up until its total number of pixels exceeds `min_pixels`.
"min_pixels": 32 * 32 * 3,
# The maximum pixel threshold for the input image. If an image is larger than this threshold, it is scaled down until its total number of pixels is below `max_pixels`.
"max_pixels": 32 * 32 * 8192,
# Enable the automatic image rotation feature.
"enable_rotate": False}]
}]
response = dashscope.MultiModalConversation.call(
# API keys for the Singapore and Beijing regions are different. 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='qwen-vl-ocr-2025-11-20',
messages=messages,
# Set the built-in task to multilingual recognition.
ocr_options={"task": "multi_lan"}
)
# The multilingual recognition task returns results in plain text.
print(response["output"]["choices"][0]["message"].content[0]["text"])import java.util.Arrays;
import java.util.Collections;
import java.util.Map;
import java.util.HashMap;
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.aigc.multimodalconversation.OcrOptions;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.utils.Constants;
public class Main {
static {
// The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1
Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
}
public static void simpleMultiModalConversationCall()
throws ApiException, NoApiKeyException, UploadFileException {
MultiModalConversation conv = new MultiModalConversation();
Map<String, Object> map = new HashMap<>();
map.put("image", "https://img.alicdn.com/imgextra/i2/O1CN01VvUMNP1yq8YvkSDFY_!!6000000006629-2-tps-6000-3000.png");
// The maximum pixel threshold for the input image. If the image is larger, it is scaled down until its total pixels are below max_pixels.
map.put("max_pixels", 8388608);
// The minimum pixel threshold for the input image. If the image is smaller, it is scaled up until its total pixels exceed min_pixels.
map.put("min_pixels", 3072);
// Enable the automatic image rotation feature.
map.put("enable_rotate", false);
// Configure the built-in OCR task.
OcrOptions ocrOptions = OcrOptions.builder()
.task(OcrOptions.Task.MULTI_LAN)
.build();
MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
.content(Arrays.asList(
map
)).build();
MultiModalConversationParam param = MultiModalConversationParam.builder()
// API keys for the Singapore and Beijing regions are different. 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: .apiKey("sk-xxx")
.apiKey(System.getenv("DASHSCOPE_API_KEY"))
.model("qwen-vl-ocr-2025-11-20")
.message(userMessage)
.ocrOptions(ocrOptions)
.build();
MultiModalConversationResult result = conv.call(param);
System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text"));
}
public static void main(String[] args) {
try {
simpleMultiModalConversationCall();
} catch (ApiException | NoApiKeyException | UploadFileException e) {
System.out.println(e.getMessage());
}
System.exit(0);
}
}# ======= Important =======
# API keys for the Singapore and Beijing regions are different. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
# The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation
# === Delete this comment before execution ===
curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '
{
"model": "qwen-vl-ocr-2025-11-20",
"input": {
"messages": [
{
"role": "user",
"content": [
{
"image": "https://img.alicdn.com/imgextra/i2/O1CN01VvUMNP1yq8YvkSDFY_!!6000000006629-2-tps-6000-3000.png",
"min_pixels": 3072,
"max_pixels": 8388608,
"enable_rotate": false
}
]
}
]
},
"parameters": {
"ocr_options": {
"task": "multi_lan"
}
}
}
'Pass a local file (Base64 encoding or file path)
The model supports two methods for uploading local files:
Direct upload using a file path (more stable transfer, recommended)
Upload using Base64 encoding
Upload using a file path
You can pass the local file path directly to the model. This method is supported only by the DashScope Python and Java SDKs. It is not supported by DashScope HTTP or OpenAI-compatible methods.
For more information about how to specify the file path based on your programming language and operating system, see the following table.
Upload using Base64 encoding
You can convert the file to a Base64-encoded string and then pass it to the model. This method is applicable for OpenAI, DashScope SDK, and HTTP methods.
Limits
Uploading using a file path is recommended for higher stability. You can also use Base64 encoding for files that are smaller than 1 MB.
When passing a file path directly, the individual image must be smaller than 10 MB.
When passing a file using Base64 encoding, the encoded image must be smaller than 10 MB because Base64 encoding increases the data size.
For more information about how to compress a file, see How do I compress an image to the required size?
Pass a file path
This method is supported only when you call the model using the DashScope Python and Java SDKs. It is not supported by DashScope HTTP or OpenAI-compatible methods.
Python
import os
import dashscope
# The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'
# Replace xxx/test.jpg with the absolute path of your local image
local_path = "xxx/test.jpg"
image_path = f"file://{local_path}"
messages = [
{
"role": "user",
"content": [
{
"image": image_path,
# The minimum pixel threshold for the input image. If the image is smaller than this value, it is scaled up until its total pixels exceed min_pixels.
"min_pixels": 32 * 32 * 3,
# The maximum pixel threshold for the input image. If the image is larger than this value, it is scaled down until its total pixels are below max_pixels.
"max_pixels": 32 * 32 * 8192,
},
# If no built-in task is set for the model, you can pass a prompt in the text field. If no prompt is passed, the default prompt is used: Please output only the text content from the image without any additional descriptions or formatting.
{
"text": "Extract the invoice number, train number, departure station, arrival station, departure date and time, seat number, seat class, ticket price, ID number, and passenger name from the train ticket image. Extract the key information accurately. Do not omit or fabricate information. Replace any single character that is blurry or obscured by glare with a question mark (?). Return the data in JSON format as follows: {'Invoice Number': 'xxx', 'Train Number': 'xxx', 'Departure Station': 'xxx', 'Arrival Station': 'xxx', 'Departure Date and Time': 'xxx', 'Seat Number': 'xxx', 'Seat Class': 'xxx', 'Ticket Price': 'xxx', 'ID Number': 'xxx', 'Passenger Name': 'xxx'}"
},
],
}
]
response = dashscope.MultiModalConversation.call(
# API keys are different for the Singapore and Beijing regions. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-an-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="qwen-vl-ocr-2025-11-20",
messages=messages,
)
print(response["output"]["choices"][0]["message"].content[0]["text"])
Java
import java.util.Arrays;
import java.util.Collections;
import java.util.Map;
import java.util.HashMap;
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.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
import io.reactivex.Flowable;
import com.alibaba.dashscope.utils.Constants;
public class Main {
static {
// The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1
Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
}
public static void simpleMultiModalConversationCall(String localPath)
throws ApiException, NoApiKeyException, UploadFileException {
String filePath = "file://"+localPath;
MultiModalConversation conv = new MultiModalConversation();
Map<String, Object> map = new HashMap<>();
map.put("image", filePath);
// The maximum pixel threshold for the input image. If the image is larger than this value, it is scaled down until its total pixels are below max_pixels.
map.put("max_pixels", 8388608);
// The minimum pixel threshold for the input image. If the image is smaller than this value, it is scaled up until its total pixels exceed min_pixels.
map.put("min_pixels", 3072);
MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
.content(Arrays.asList(
map,
// If no built-in task is set for the model, you can pass a prompt in the text field. If no prompt is passed, the default prompt is used: Please output only the text content from the image without any additional descriptions or formatting.
Collections.singletonMap("text", "Extract the invoice number, train number, departure station, arrival station, departure date and time, seat number, seat class, ticket price, ID number, and passenger name from the train ticket image. Extract the key information accurately. Do not omit or fabricate information. Replace any single character that is blurry or obscured by glare with a question mark (?). Return the data in JSON format as follows: {'Invoice Number': 'xxx', 'Train Number': 'xxx', 'Departure Station': 'xxx', 'Arrival Station': 'xxx', 'Departure Date and Time': 'xxx', 'Seat Number': 'xxx', 'Seat Class': 'xxx', 'Ticket Price': 'xxx', 'ID Number': 'xxx', 'Passenger Name': 'xxx'}"))).build();
MultiModalConversationParam param = MultiModalConversationParam.builder()
// API keys are different for the Singapore and Beijing regions. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-an-api-key
// If you have not configured the environment variable, replace the following line with your Model Studio API key: .apiKey("sk-xxx")
.apiKey(System.getenv("DASHSCOPE_API_KEY"))
.model("qwen-vl-ocr-2025-11-20")
.message(userMessage)
.build();
MultiModalConversationResult result = conv.call(param);
System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text"));
}
public static void main(String[] args) {
try {
// Replace xxx/test.jpg with the absolute path of your local image
simpleMultiModalConversationCall("xxx/test.jpg");
} catch (ApiException | NoApiKeyException | UploadFileException e) {
System.out.println(e.getMessage());
}
System.exit(0);
}
}Pass a Base64 encoding
OpenAI compatible
Python
from openai import OpenAI
import os
import base64
# Read a local file and encode it in Base64 format
def encode_image(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode("utf-8")
# Replace xxx/test.png with the absolute path of your local image
base64_image = encode_image("xxx/test.png")
client = OpenAI(
# API keys are different for the Singapore and Beijing regions. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-an-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'),
# The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/compatible-mode/v1
base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)
completion = client.chat.completions.create(
model="qwen-vl-ocr-2025-11-20",
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
# Note that when passing a Base64 string, the image format (image/{format}) must match the Content-Type in the list of supported images. "f" is a string formatting method.
# PNG image: f"data:image/png;base64,{base64_image}"
# JPEG image: f"data:image/jpeg;base64,{base64_image}"
# WEBP image: f"data:image/webp;base64,{base64_image}"
"image_url": {"url": f"data:image/png;base64,{base64_image}"},
# The minimum pixel threshold for the input image. If the image is smaller than this value, it is scaled up until its total pixels exceed min_pixels.
"min_pixels": 32 * 32 * 3,
# The maximum pixel threshold for the input image. If the image is larger than this value, it is scaled down until its total pixels are below max_pixels.
"max_pixels": 32 * 32 * 8192
},
# The model supports passing a prompt in the following text field. If no prompt is passed, the default prompt is used: Please output only the text content from the image without any additional descriptions or formatting.
{"type": "text", "text": "Extract the invoice number, train number, departure station, arrival station, departure date and time, seat number, seat class, ticket price, ID number, and passenger name from the train ticket image. Extract the key information accurately. Do not omit or fabricate information. Replace any single character that is blurry or obscured by glare with a question mark (?). Return the data in JSON format as follows: {'Invoice Number': 'xxx', 'Train Number': 'xxx', 'Departure Station': 'xxx', 'Arrival Station': 'xxx', 'Departure Date and Time': 'xxx', 'Seat Number': 'xxx', 'Seat Class': 'xxx', 'Ticket Price': 'xxx', 'ID Number': 'xxx', 'Passenger Name': 'xxx'}"},
],
}
],
)
print(completion.choices[0].message.content)Node.js
import OpenAI from "openai";
import {
readFileSync
} from 'fs';
const client = new OpenAI({
// API keys are different for the Singapore and Beijing regions. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-an-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,
// The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/compatible-mode/v1
baseURL: "https://dashscope-intl.aliyuncs.com/compatible-mode/v1"
});
// Read a local file and encode it in Base64 format
const encodeImage = (imagePath) => {
const imageFile = readFileSync(imagePath);
return imageFile.toString('base64');
};
// Replace xxx/test.jpg with the absolute path of your local image
const base64Image = encodeImage("xxx/test.jpg")
async function main() {
const completion = await client.chat.completions.create({
model: "qwen-vl-ocr",
messages: [{
"role": "user",
"content": [{
"type": "image_url",
"image_url": {
// Note that when passing a Base64 string, the image format (image/{format}) must match the Content-Type in the list of supported images.
// PNG image: data:image/png;base64,${base64Image}
// JPEG image: data:image/jpeg;base64,${base64Image}
// WEBP image: data:image/webp;base64,${base64Image}
"url": `data:image/jpeg;base64,${base64Image}`
},
// The minimum pixel threshold for the input image. If the image is smaller than this value, it is scaled up until its total pixels exceed min_pixels.
"min_pixels": 32 * 32 * 3,
// The maximum pixel threshold for the input image. If the image is larger than this value, it is scaled down until its total pixels are below max_pixels.
"max_pixels": 32 * 32 * 8192
},
// The model supports passing a prompt in the following text field. If no prompt is passed, the default prompt is used: Please output only the text content from the image without any additional descriptions or formatting.
{
"type": "text",
"text": "Extract the invoice number, train number, departure station, arrival station, departure date and time, seat number, seat class, ticket price, ID number, and passenger name from the train ticket image. Extract the key information accurately. Do not omit or fabricate information. Replace any single character that is blurry or obscured by glare with a question mark (?). Return the data in JSON format as follows: {'Invoice Number': 'xxx', 'Train Number': 'xxx', 'Departure Station': 'xxx', 'Arrival Station': 'xxx', 'Departure Date and Time': 'xxx', 'Seat Number': 'xxx', 'Seat Class': 'xxx', 'Ticket Price': 'xxx', 'ID Number': 'xxx', 'Passenger Name': 'xxx'}"
}
]
}]
});
console.log(completion.choices[0].message.content);
}
main();curl
For information about the method to convert a file to a Base64-encoded string, see the example code.
For demonstration purposes, the Base64-encoded string
"data:image/png;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA..."in the code is truncated. In actual use, you must pass the complete encoded string.
# ======= Important =======
# API keys are different for the Singapore and Beijing regions. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-an-api-key
# The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions
# === Delete this comment before execution ===
curl --location 'https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
"model": "qwen-vl-ocr-latest",
"messages": [
{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": "data:image/png;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA..."}},
{"type": "text", "text": "Extract the invoice number, train number, departure station, arrival station, departure date and time, seat number, seat class, ticket price, ID number, and passenger name from the train ticket image. Extract the key information accurately. Do not omit or fabricate information. Replace any single character that is blurry or obscured by glare with a question mark (?). Return the data in JSON format as follows: {'Invoice Number': 'xxx', 'Train Number': 'xxx', 'Departure Station': 'xxx', 'Arrival Station': 'xxx', 'Departure Date and Time': 'xxx', 'Seat Number': 'xxx', 'Seat Class': 'xxx', 'Ticket Price': 'xxx', 'ID Number': 'xxx', 'Passenger Name': 'xxx'}"}
]
}]
}'DashScope
Python
import os
import base64
import dashscope
# The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'
# Base64 encoding format
def encode_image(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode("utf-8")
# Replace xxx/test.jpg with the absolute path of your local image
base64_image = encode_image("xxx/test.jpg")
messages = [
{
"role": "user",
"content": [
{
# Note that when passing a Base64 string, the image format (image/{format}) must match the Content-Type in the list of supported images. "f" is a string formatting method.
# PNG image: f"data:image/png;base64,{base64_image}"
# JPEG image: f"data:image/jpeg;base64,{base64_image}"
# WEBP image: f"data:image/webp;base64,{base64_image}"
"image": f"data:image/jpeg;base64,{base64_image}",
# The minimum pixel threshold for the input image. If the image is smaller than this value, it is scaled up until its total pixels exceed min_pixels.
"min_pixels": 32 * 32 * 3,
# The maximum pixel threshold for the input image. If the image is larger than this value, it is scaled down until its total pixels are below max_pixels.
"max_pixels": 32 * 32 * 8192,
},
# If no built-in task is set for the model, you can pass a prompt in the text field. If no prompt is passed, the default prompt is used: Please output only the text content from the image without any additional descriptions or formatting.
{
"text": "Extract the invoice number, train number, departure station, arrival station, departure date and time, seat number, seat class, ticket price, ID number, and passenger name from the train ticket image. Extract the key information accurately. Do not omit or fabricate information. Replace any single character that is blurry or obscured by glare with a question mark (?). Return the data in JSON format as follows: {'Invoice Number': 'xxx', 'Train Number': 'xxx', 'Departure Station': 'xxx', 'Arrival Station': 'xxx', 'Departure Date and Time': 'xxx', 'Seat Number': 'xxx', 'Seat Class': 'xxx', 'Ticket Price': 'xxx', 'ID Number': 'xxx', 'Passenger Name': 'xxx'}"
},
],
}
]
response = dashscope.MultiModalConversation.call(
# API keys are different for the Singapore and Beijing regions. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-an-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="qwen-vl-ocr-2025-11-20",
messages=messages,
)
print(response["output"]["choices"][0]["message"].content[0]["text"])Java
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.*;
import java.util.Arrays;
import java.util.Collections;
import java.util.Map;
import java.util.HashMap;
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.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
import io.reactivex.Flowable;
import com.alibaba.dashscope.utils.Constants;
public class Main {
static {
// The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1
Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
}
// Base64 encoding format
private static String encodeImageToBase64(String imagePath) throws IOException {
Path path = Paths.get(imagePath);
byte[] imageBytes = Files.readAllBytes(path);
return Base64.getEncoder().encodeToString(imageBytes);
}
public static void simpleMultiModalConversationCall(String localPath)
throws ApiException, NoApiKeyException, UploadFileException, IOException {
String base64Image = encodeImageToBase64(localPath); // Base64 encoding
MultiModalConversation conv = new MultiModalConversation();
Map<String, Object> map = new HashMap<>();
map.put("image", "data:image/jpeg;base64," + base64Image);
// The maximum pixel threshold for the input image. If the image is larger than this value, it is scaled down until its total pixels are below max_pixels.
map.put("max_pixels", 8388608);
// The minimum pixel threshold for the input image. If the image is smaller than this value, it is scaled up until its total pixels exceed min_pixels.
map.put("min_pixels", 3072);
MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
.content(Arrays.asList(
map,
// If no built-in task is set for the model, you can pass a prompt in the text field. If no prompt is passed, the default prompt is used: Please output only the text content from the image without any additional descriptions or formatting.
Collections.singletonMap("text", "Extract the invoice number, train number, departure station, arrival station, departure date and time, seat number, seat class, ticket price, ID number, and passenger name from the train ticket image. Extract the key information accurately. Do not omit or fabricate information. Replace any single character that is blurry or obscured by glare with a question mark (?). Return the data in JSON format as follows: {'Invoice Number': 'xxx', 'Train Number': 'xxx', 'Departure Station': 'xxx', 'Arrival Station': 'xxx', 'Departure Date and Time': 'xxx', 'Seat Number': 'xxx', 'Seat Class': 'xxx', 'Ticket Price': 'xxx', 'ID Number': 'xxx', 'Passenger Name': 'xxx'}"))).build();
MultiModalConversationParam param = MultiModalConversationParam.builder()
// API keys are different for the Singapore and Beijing regions. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-an-api-key
// If you have not configured the environment variable, replace the following line with your Model Studio API key: .apiKey("sk-xxx")
.apiKey(System.getenv("DASHSCOPE_API_KEY"))
.model("qwen-vl-ocr-2025-11-20")
.message(userMessage)
.build();
MultiModalConversationResult result = conv.call(param);
System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text"));
}
public static void main(String[] args) {
try {
// Replace xxx/test.jpg with the absolute path of your local image
simpleMultiModalConversationCall("xxx/test.jpg");
} catch (ApiException | NoApiKeyException | UploadFileException | IOException e) {
System.out.println(e.getMessage());
}
System.exit(0);
}
}curl
For information about the method to convert a file to a Base64-encoded string, see the example code.
For demonstration purposes, the Base64-encoded string
"data:image/png;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA..."in the code is truncated. In actual use, you must pass the complete encoded string.
# ======= Important =======
# API keys are different for the Singapore and Beijing regions. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-an-api-key
# The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation
# === Delete this comment before execution ===
curl -X POST https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "qwen-vl-ocr-latest",
"input":{
"messages":[
{
"role": "user",
"content": [
{"image": "data:image/png;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA..."},
{"text": "Extract the invoice number, train number, departure station, arrival station, departure date and time, seat number, seat class, ticket price, ID number, and passenger name from the train ticket image. Extract the key information accurately. Do not omit or fabricate information. Replace any single character that is blurry or obscured by glare with a question mark (?). Return the data in JSON format as follows: {'Invoice Number': 'xxx', 'Train Number': 'xxx', 'Departure Station': 'xxx', 'Arrival Station': 'xxx', 'Departure Date and Time': 'xxx', 'Seat Number': 'xxx', 'Seat Class': 'xxx', 'Ticket Price': 'xxx', 'ID Number': 'xxx', 'Passenger Name': 'xxx'}"}
]
}
]
}
}'More usages
Limitations
Image limits
File size: The size of a single image file cannot exceed 10 MB. For Base64-encoded files, the size of the encoded string cannot exceed 10 MB. For more information, see Pass a local file.
Dimensions and aspect ratio: The image width and height must both be greater than 10 pixels. The aspect ratio must not exceed 200:1 or 1:200.
Total pixels: The model automatically scales images, so there is no strict limit on the total number of pixels. However, images should not exceed 15.68 million pixels.
Image format
Common extensions
MIME type
BMP
.bmp
image/bmp
JPEG
.jpe, .jpeg, .jpg
image/jpeg
PNG
.png
image/png
TIFF
.tif, .tiff
image/tiff
WEBP
.webp
image/webp
HEIC
.heic
image/heic
Model limits
System message: This model does not support a custom
System Messagebecause it uses a fixed internalSystem Message. You must pass all instructions throughUser Message.No multi-turn conversations: The model does not support multi-turn conversations and only answers the most recent question.
Hallucination risk: The model may hallucinate if text in an image is too small or has a low resolution. Additionally, the accuracy of answers to questions not related to text extraction is not guaranteed.
Cannot process text files:
Files that contain image data must be transformed into an image sequence before they are processed. For more information, see the recommendations in Going live.
For files with plain text or structured data, use Qwen-Long, which can parse long text.
Billing and rate limiting
Billing: Qwen-OCR is a multimodal model. The total cost is calculated as follows: (Number of input tokens × Unit price for input) + (Number of output tokens × Unit price for output). For information about how image tokens are calculated, see Image token conversion method. You can view your bills or top up your account on the Expenses and Costs page in the Alibaba Cloud Management Console.
Rate limiting: For information about the rate limits of Qwen-OCR, see Rate limits.
Free quota (Singapore region only): Qwen-OCR provides a free quota of 1 million tokens. This quota is valid for 90 days from the date you activate Alibaba Cloud Model Studio or the date your request to use the model is approved.
Going live
Processing multi-page documents, such as PDFs:
Split: Use an image editing library, such as
Python'spdf2image, to convert each page of a PDF file into high-quality images.Submit requests: Use the multi-image input method for recognition.
Image pre-processing:
Ensure that input images are clear, evenly lit, and not overly compressed:
To prevent information loss, se lossless formats, such as PNG, for image storage and transmission.
To improve image definition, se noise reduction algorithms, such as mean or median filtering, for smoothing images that contain noise.
To correct uneven lighting, se algorithms such as adaptive histogram equalization to adjust brightness and contrast.
For skewed images: Use the DashScope SDK's
enable_rotate: trueparameter to significantly improve recognition performance.For very small or very large images: Use the
min_pixelsandmax_pixelsparameters to control scaling behavior before image editing.min_pixels: Enlarges small images to help detect details. We recommend that you keep the default value.max_pixels: Prevents very large images from consuming excessive resources. The default value is suitable for most scenarios. If small text is not detected clearly, you can increase themax_pixelsvalue. Note that this increases token consumption.
Result validation: Model recognition results may contain errors. For critical business operations, you can implement a manual review process or add validation rules to verify the accuracy of the model's output. For example, se format validation for ID card and bank card numbers.
Batch calls: For large-scale, non-real-time scenarios, se the Batch API to process batch jobs asynchronously at lower costs.
FAQ
How do I draw detection boxes on the original image after the model outputs text localization results?
API reference
For the input and output parameters for Qwen-OCR, see Qwen-OCR API reference.
Error codes
If a call fails, see Error messages for troubleshooting.