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Qwen-OCR API reference ​

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Extract text, structured data, and key information from images using the Qwen-OCR model. Qwen-OCR supports two API protocols: the OpenAI-compatible API and the DashScope API.

For use cases and getting-started guidance, see Text extraction (Qwen-OCR).

OpenAI-compatible API ​

Endpoints ​

RegionSDKbase_urlHTTP endpoint
RegionSDKbase_urlHTTP endpoint
Singaporehttps://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions
US (Virginia)https://dashscope-us.aliyuncs.com/compatible-mode/v1POST https://dashscope-us.aliyuncs.com/compatible-mode/v1/chat/completions
China (Beijing)https://dashscope.aliyuncs.com/compatible-mode/v1POST https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions

Prerequisites ​

Get an API key and set it as an environment variable. If you use the OpenAI SDK, install the SDK.

Quick start ​

Use the OpenAI-compatible chat completions endpoint. Send a user message with an image URL and text prompt. The model extracts text and returns it in choices[0].message.content.

Non-streaming ​

Python

python
from openai import OpenAI
import os

PROMPT_TICKET_EXTRACTION = """
Please extract the invoice number, train number, departure station, arrival station, departure date and time, seat number, seat class, ticket price, ID card number, and passenger name from the train ticket image.
You must accurately extract the key information. Do not omit or fabricate information. Replace any single character that is blurry or obscured by strong light with an English question mark (?).
Return the data in JSON format as follows: {'invoice_number': 'xxx', 'departure_station': 'xxx', 'arrival_station': 'xxx', 'departure_date_and_time':'xxx', 'seat_number': 'xxx','ticket_price':'xxx', 'id_card_number': 'xxx', 'passenger_name': 'xxx'},
"""

try:
    client = OpenAI(
        # If the environment variable is not configured, replace with: api_key="sk-xxx"
        api_key=os.getenv("DASHSCOPE_API_KEY"),
        # Singapore region. For US (Virginia), use https://dashscope-us.aliyuncs.com/compatible-mode/v1
        # For China (Beijing), use https://dashscope.aliyuncs.com/compatible-mode/v1
        base_url="https://{WorkspaceId}.ap-southeast-1.maas.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"},\
                        # Minimum pixel count. Images below this are upscaled.\
                        "min_pixels": 32 * 32 * 3,\
                        # Maximum pixel count. Images above this are downscaled.\
                        "max_pixels": 32 * 32 * 8192\
                    },\
                    # Custom prompt. Without this, the model uses: "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

javascript
import OpenAI from 'openai';

const PROMPT_TICKET_EXTRACTION = `
Please extract the invoice number, train number, departure station, arrival station, departure date and time, seat number, seat class, ticket price, ID card number, and passenger name from the train ticket image.
You must accurately extract the key information. Do not omit or fabricate information. Replace any single character that is blurry or obscured by strong light with an English question mark (?).
Return the data in JSON format as follows: {'invoice_number': 'xxx', 'departure_station': 'xxx', 'arrival_station': 'xxx', 'departure_date_and_time':'xxx', 'seat_number': 'xxx','ticket_price':'xxx', 'id_card_number': 'xxx', 'passenger_name': 'xxx'}
`;

const client = new OpenAI({
  // If the environment variable is not configured, replace with: apiKey: "sk-xxx"
  apiKey: process.env.DASHSCOPE_API_KEY,
  // For China (Beijing), use https://dashscope.aliyuncs.com/compatible-mode/v1
  baseURL: 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1',
});

async function main() {
  const response = await client.chat.completions.create({
    model: 'qwen-vl-ocr-2025-11-20',
    messages: [\
      {\
        role: 'user',\
        content: [\
          { 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',\
            },\
              // Minimum pixel count. Images below this are upscaled.\
              "min_pixels": 32 * 32 * 3,\
              // Maximum pixel count. Images above this are downscaled.\
              "max_pixels": 32 * 32 * 8192\
          }\
        ]\
      }\
    ],
  });
  console.log(response.choices[0].message.content)
}

main();

curl

bash
curl -X POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
  "model": "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, arrival station, departure date and time, seat number, seat class, ticket price, ID card number, and passenger name from the train ticket image. You must accurately extract the key information. Do not omit or fabricate information. Replace any single character that is blurry or obscured by strong light with an English question mark (?). Return the data in JSON format as follows: {\'invoice_number\': \'xxx\', \'departure_station\': \'xxx\', \'arrival_station\': \'xxx\', \'departure_date_and_time\':\'xxx\', \'seat_number\': \'xxx\',\'ticket_price\':\'xxx\', \'id_card_number\': \'xxx\', \'passenger_name\': \'xxx\'}"}\
            ]\
        }\
    ]
}'

Streaming ​

Set stream to true to receive results incrementally as the model generates them.

Python

python
import os
from openai import OpenAI

PROMPT_TICKET_EXTRACTION = """
Please extract the invoice number, train number, departure station, arrival station, departure date and time, seat number, seat class, ticket price, ID card number, and passenger name from the train ticket image.
You must accurately extract the key information. Do not omit or fabricate information. Replace any single character that is blurry or obscured by strong light with an English question mark (?).
Return the data in JSON format as follows: {'invoice_number': 'xxx','departure_station': 'xxx', 'arrival_station': 'xxx', 'departure_date_and_time':'xxx', 'seat_number': 'xxx','ticket_price':'xxx', 'id_card_number': 'xxx', 'passenger_name': 'xxx'},
"""

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
)
completion = client.chat.completions.create(
    model="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": 32 * 32 * 3,\
                    "max_pixels": 32 * 32 * 8192\
                },\
                {"type": "text","text": PROMPT_TICKET_EXTRACTION}\
            ]\
        }\
    ],
    stream=True,
    stream_options={"include_usage": True}
)

for chunk in completion:
    print(chunk.model_dump_json())

Node.js

javascript
import OpenAI from 'openai';

const PROMPT_TICKET_EXTRACTION = `
Please extract the invoice number, train number, departure station, arrival station, departure date and time, seat number, seat class, ticket price, ID card number, and passenger name from the train ticket image.
You must accurately extract the key information. Do not omit or fabricate information. Replace any single character that is blurry or obscured by strong light with an English question mark (?).
Return the data in JSON format as follows: {'invoice_number': 'xxx', 'departure_station': 'xxx', 'arrival_station': 'xxx', 'departure_date_and_time':'xxx', 'seat_number': 'xxx','ticket_price':'xxx', 'id_card_number': 'xxx', 'passenger_name': 'xxx'}
`;

const openai = new OpenAI({
  apiKey: process.env.DASHSCOPE_API_KEY,
  baseURL: 'https://{WorkspaceId}.ap-southeast-1.maas.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: [\
          { 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',\
            },\
              "min_pixels": 32 * 32 * 3,\
              "max_pixels": 32 * 32 * 8192\
          }\
        ]\
      }\
    ],
    stream: true,
    stream_options:{"include_usage": true}
  });
  let fullContent = ""
  console.log("Streaming output content:")
  for await (const chunk of response) {
    if (chunk.choices[0] && chunk.choices[0].delta.content != null) {
      fullContent += chunk.choices[0].delta.content;
      console.log(chunk.choices[0].delta.content);
    }
  }
  console.log(`Full output content: ${fullContent}`)
}

main();

curl

bash
curl -X POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
  "model": "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, arrival station, departure date and time, seat number, seat class, ticket price, ID card number, and passenger name from the train ticket image. You must accurately extract the key information. Do not omit or fabricate information. Replace any single character that is blurry or obscured by strong light with an English question mark (?). Return the data in JSON format as follows: {\'invoice_number\': \'xxx\', \'departure_station\': \'xxx\', \'arrival_station\': \'xxx\', \'departure_date_and_time\':\'xxx\', \'seat_number\': \'xxx\',\'ticket_price\':\'xxx\', \'id_card_number\': \'xxx\', \'passenger_name\': \'xxx\'}"}\
            ]\
        }\
    ],
    "stream": true,
    "stream_options": {"include_usage": true}
}'

Request parameters ​

ParameterTypeRequiredDescription
ParameterTypeRequiredDescription
modelstringYesModel name. See Recommended models for supported models.
messagesarrayYesAn array of message objects that provides context to the model.

Message object

Each message requires a role (must be user) and a content array with these element types:

ParameterTypeRequiredDescription
ParameterTypeRequiredDescription
typestringYestext for text input, image_url for image input.
textstringNoThe text prompt. Default: "Please output only the text content from the image without any additional descriptions or formatting".
image_url.urlstringYes (when type is image_url)URL or Base64-encoded Data URL of the image. For local files, see Text extraction.
min_pixelsintegerNoMinimum pixel threshold. Images below this value are upscaled. See Image resolution control.
max_pixelsintegerNoMaximum pixel threshold. Images above this value are downscaled. See Image resolution control.

Generation parameters

ParameterTypeDefaultDescription
ParameterTypeDefaultDescription
streambooleanfalseSet to true to receive incremental responses as the model generates output.
stream_options.include_usagebooleanfalseWhen stream is true, set this to true to include token usage in the last chunk.
max_tokensintegerVariesMaximum tokens in the output. Exceeding this truncates the response. See Output token limits.
temperaturefloat0.01Controls output diversity. Higher values produce more varied text. Range: [0, 2).
top_pfloat0.001Nucleus sampling threshold. Higher values increase diversity. Range: (0, 1.0]. Set either temperature or top_p, not both.
top_kinteger1Limits the candidate token set during sampling. If the value is None or greater than 100, the top_k policy is not enabled, and only the top_p policy takes effect. Must be >= 0. Not a standard OpenAI parameter -- pass via extra_body in the Python SDK: extra_body={"top_k": xxx}. In the Node.js SDK or HTTP, pass at the top level.
repetition_penaltyfloat1.0Penalty for repeated sequences. Values above 1.0 reduce repetition. Not a standard OpenAI parameter -- pass via extra_body in the Python SDK.
presence_penaltyfloat0.0Controls content repetition. Range: [-2.0, 2.0]. Positive values reduce repetition.
seedinteger--Ensures reproducible results when the same value is used with identical parameters. Range: [0, 2^31 - 1].
logprobsbooleanfalseSet to true to return log probabilities of output tokens.
top_logprobsinteger0Number of most likely tokens to return per step. Range: [0, 5]. Only effective when logprobs is true.
stopstring or array--Stop words or token IDs. Generation stops when a specified string or token_id appears. Do not mix strings and token_ids in the same array.

Response ​

Non-streaming response (chat.completion) ​

json
{
  "id": "chatcmpl-ba21fa91-dcd6-4dad-90cc-6d49c3c39094",
  "choices": [\
    {\
      "finish_reason": "stop",\
      "index": 0,\
      "logprobs": null,\
      "message": {\
        "content": "```json\n{\n    \"seller_name\": \"null\",\n    \"buyer_name\": \"Cai Yingshi\",\n    \"price_excluding_tax\": \"230769.23\",\n    \"organization_code\": \"null\",\n    \"invoice_code\": \"142011726001\"\n}\n```",\
        "refusal": null,\
        "role": "assistant",\
        "annotations": null,\
        "audio": null,\
        "function_call": null,\
        "tool_calls": null\
      }\
    }\
  ],
  "created": 1763283287,
  "model": "qwen-vl-ocr-latest",
  "object": "chat.completion",
  "service_tier": null,
  "system_fingerprint": null,
  "usage": {
    "completion_tokens": 72,
    "prompt_tokens": 1185,
    "total_tokens": 1257,
    "completion_tokens_details": {
      "accepted_prediction_tokens": null,
      "audio_tokens": null,
      "reasoning_tokens": null,
      "rejected_prediction_tokens": null,
      "text_tokens": 72
    },
    "prompt_tokens_details": {
      "audio_tokens": null,
      "cached_tokens": null,
      "image_tokens": 1001,
      "text_tokens": 184
    }
  }
}
FieldTypeDescription
FieldTypeDescription
idstringUnique request identifier.
choicesarrayModel-generated content.
choices[].finish_reasonstringstop when generation completed normally, length when truncated due to token limit.
choices[].indexintegerPosition in the choices array.
choices[].message.contentstringExtracted text or structured output from the model.
choices[].message.rolestringAlways assistant.
choices[].message.refusalstringAlways null.
choices[].message.audioobjectAlways null.
choices[].message.function_callobjectAlways null.
choices[].message.tool_callsarrayAlways null.
createdintegerUNIX timestamp of the request.
modelstringModel used.
objectstringAlways chat.completion.
service_tierstringAlways null.
system_fingerprintstringAlways null.
usage.completion_tokensintegerOutput token count.
usage.prompt_tokensintegerInput token count.
usage.total_tokensintegerSum of prompt_tokens and completion_tokens.
usage.completion_tokens_details.text_tokensintegerText output tokens. Other fields in completion_tokens_details are always null.
usage.prompt_tokens_details.image_tokensintegerImage input tokens.
usage.prompt_tokens_details.text_tokensintegerText input tokens. Other fields in prompt_tokens_details are always null.

Streaming response (chat.completion.chunk) ​

When stream is true, the response is delivered as a series of Server-Sent Event (SSE) chunks. Each chunk follows the same structure as the non-streaming response, with these differences:

  • object is always chat.completion.chunk.

  • choices[].delta replaces choices[].message. The delta object has the same fields as message.

  • choices[].delta.role is returned only in the first chunk.

  • finish_reason is null during generation, stop on completion, or length if truncated.

  • When include_usage is true, the last chunk has an empty choices array and includes the usage object.

json
{"id":"chatcmpl-f6fbdc0d-78d6-418f-856f-f099c2e4859b","choices":[{"delta":{"content":"","function_call":null,"refusal":null,"role":"assistant","tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1764139204,"model":"qwen-vl-ocr-latest","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null}
{"id":"chatcmpl-f6fbdc0d-78d6-418f-856f-f099c2e4859b","choices":[{"delta":{"content":"```","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1764139204,"model":"qwen-vl-ocr-latest","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null}
{"id":"chatcmpl-f6fbdc0d-78d6-418f-856f-f099c2e4859b","choices":[{"delta":{"content":"json","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1764139204,"model":"qwen-vl-ocr-latest","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null}
......
{"id":"chatcmpl-f6fbdc0d-78d6-418f-856f-f099c2e4859b","choices":[{"delta":{"content":"","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":"stop","index":0,"logprobs":null}],"created":1764139204,"model":"qwen-vl-ocr-latest","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null}
{"id":"chatcmpl-f6fbdc0d-78d6-418f-856f-f099c2e4859b","choices":[],"created":1764139204,"model":"qwen-vl-ocr-latest","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":{"completion_tokens":141,"prompt_tokens":513,"total_tokens":654,"completion_tokens_details":{"accepted_prediction_tokens":null,"audio_tokens":null,"reasoning_tokens":null,"rejected_prediction_tokens":null,"text_tokens":141},"prompt_tokens_details":{"audio_tokens":null,"cached_tokens":null,"image_tokens":332,"text_tokens":181}}}

Image resolution control ​

min_pixels and max_pixels control image resizing before processing. Token-to-pixel ratio depends on model version:

ModelPixels per tokenmin_pixelsdefault (minimum)max_pixelsdefaultmax_pixelsmaximum
ModelPixels per tokenmin_pixelsdefault (minimum)max_pixelsdefaultmax_pixelsmaximum
qwen-vl-ocr-latest, qwen-vl-ocr-2025-11-2032 x 32 = 1,0243,072 (3 tokens)8,388,608 (8,192 tokens)30,720,000 (30,000 tokens)
qwen-vl-ocr, qwen-vl-ocr-2025-08-28, and earlier28 x 28 = 7843,136 (4 tokens)6,422,528 (8,192 tokens)23,520,000 (30,000 tokens)

Resizing behavior:

  • If the image pixel count is below min_pixels, the image is upscaled until it exceeds min_pixels.

  • If the image pixel count is within [min_pixels, max_pixels], the original image is used without resizing.

  • If the image pixel count exceeds max_pixels, the image is downscaled below max_pixels.

Output token limits ​

ModelDefault and maximummax_tokens
ModelDefault and maximummax_tokens
qwen-vl-ocr-latest, qwen-vl-ocr-2025-11-20, qwen-vl-ocr-2024-10-28Same as the model's maximum output length. See Supported models.
qwen-vl-ocr, qwen-vl-ocr-2025-04-13, qwen-vl-ocr-2025-08-284,096

For qwen-vl-ocr, qwen-vl-ocr-2025-04-13, and qwen-vl-ocr-2025-08-28, max_tokens defaults to 4096. To increase it (4097–8192), contact your commercial manager with: your Alibaba Cloud account ID, image type (e.g., documents, e-commerce, contracts), model name, estimated QPS and daily request volume, and the percentage of requests exceeding 4096 output tokens.

DashScope API ​

Endpoints ​

RegionHTTP endpoint
RegionHTTP endpoint
SingaporePOST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation
US (Virginia)POST https://dashscope-us.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation
China (Beijing)POST https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation

SDK base URL configuration:

Python:

python
dashscope.base_http_api_url = 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1'

Java (Method 1 -- constructor):

java
import com.alibaba.dashscope.protocol.Protocol;
MultiModalConversation conv = new MultiModalConversation(Protocol.HTTP.getValue(), "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1");

Java (Method 2 -- static block):

java
import com.alibaba.dashscope.utils.Constants;
Constants.baseHttpApiUrl="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1";

Replace the domain with dashscope-us.aliyuncs.com for the US (Virginia) region or dashscope.aliyuncs.com for the China (Beijing) region. For the China (Beijing) region, you do not need to set base_url for SDK calls.

Get an API key and set it as an environment variable. If you use the DashScope SDK, you must also install the DashScope SDK.

Built-in tasks ​

The DashScope API provides built-in OCR tasks via the ocr_options parameter. Each task uses an optimized default prompt, eliminating the need for a text message.

Taskocr_options.taskvalueOutput format
Taskocr_options.taskvalueOutput format
General text recognitiontext_recognitionPlain text
High-precision recognitionadvanced_recognitionPlain text with bounding boxes
Information extractionkey_information_extractionStructured key-value pairs
Table parsingtable_parsingTable structure
Document parsingdocument_parsingDocument structure
Formula recognitionformula_recognitionLaTeX formulas
Multilingual recognitionmulti_lanMultilingual text

High-precision recognition ​

Returns text with positional data for each recognized line.

Python

python
import os
import dashscope

dashscope.base_http_api_url = 'https://{WorkspaceId}.ap-southeast-1.maas.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",\
                "min_pixels": 32 * 32 * 3,\
                "max_pixels": 32 * 32 * 8192,\
                "enable_rotate": False}]\
            }]

response = dashscope.MultiModalConversation.call(
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    model='qwen-vl-ocr-2025-11-20',
    messages=messages,
    ocr_options={"task": "advanced_recognition"}
)
print(response["output"]["choices"][0]["message"].content[0]["text"])

Java

java
// 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 {
        Constants.baseHttpApiUrl="https://{WorkspaceId}.ap-southeast-1.maas.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");
        map.put("max_pixels", 8388608);
        map.put("min_pixels", 3072);
        map.put("enable_rotate", false);

        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()
                .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);
    }
}

curl

bash
curl --location 'https://{WorkspaceId}.ap-southeast-1.maas.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 ​

Extracts structured key-value data from images. Specify fields to extract in task_config.result_schema.

Python

python
import os
import dashscope

dashscope.base_http_api_url = 'https://{WorkspaceId}.ap-southeast-1.maas.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(
    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"])

Java

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.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;
import com.google.gson.JsonObject;

public class Main {

    static {
        Constants.baseHttpApiUrl="https://{WorkspaceId}.ap-southeast-1.maas.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");
        map.put("max_pixels", 8388608);
        map.put("min_pixels", 3072);
        map.put("enable_rotate", false);

        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.");

        OcrOptions ocrOptions = OcrOptions.builder()
                .task(OcrOptions.Task.KEY_INFORMATION_EXTRACTION)
                .taskConfig(OcrOptions.TaskConfig.builder().resultSchema(resultSchema).build())
                .build();
        MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
                .content(Arrays.asList(
                        map
                        )).build();
        MultiModalConversationParam param = MultiModalConversationParam.builder()
                .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);
    }
}

curl

bash
curl --location 'https://{WorkspaceId}.ap-southeast-1.maas.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."
        }
      }
    }
  }
}
'

Table parsing ​

Extracts table structure from images.

Python

python
import os
import dashscope

dashscope.base_http_api_url = 'https://{WorkspaceId}.ap-southeast-1.maas.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",\
                "min_pixels": 32 * 32 * 3,\
                "max_pixels": 32 * 32 * 8192,\
                "enable_rotate": False}]\
            }]

response = dashscope.MultiModalConversation.call(
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    model='qwen-vl-ocr-2025-11-20',
    messages=messages,
    ocr_options={"task": "table_parsing"}
)
print(response["output"]["choices"][0]["message"].content[0]["text"])

Java

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.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 {
        Constants.baseHttpApiUrl="https://{WorkspaceId}.ap-southeast-1.maas.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");
        map.put("max_pixels", 8388608);
        map.put("min_pixels", 3072);
        map.put("enable_rotate", false);

        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()
                .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);
    }
}

curl

bash
curl --location 'https://{WorkspaceId}.ap-southeast-1.maas.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 ​

Extracts the structural layout and text from documents.

Python

python
import os
import dashscope

dashscope.base_http_api_url = 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1'

messages = [{\
            "role": "user",\
            "content": [{\
                "image": "https://img.alicdn.com/imgextra/i1/O1CN01ukECva1cisjyK6ZDK_!!6000000003635-0-tps-1500-1734.jpg",\
                "min_pixels": 32 * 32 * 3,\
                "max_pixels": 32 * 32 * 8192,\
                "enable_rotate": False}]\
            }]

response = dashscope.MultiModalConversation.call(
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    model='qwen-vl-ocr-2025-11-20',
    messages=messages,
    ocr_options={"task": "document_parsing"}
)
print(response["output"]["choices"][0]["message"].content[0]["text"])

Java

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.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 {
        Constants.baseHttpApiUrl="https://{WorkspaceId}.ap-southeast-1.maas.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");
        map.put("max_pixels", 8388608);
        map.put("min_pixels", 3072);
        map.put("enable_rotate", false);

        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()
                .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);
    }
}

curl

bash
curl --location 'https://{WorkspaceId}.ap-southeast-1.maas.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 ​

Extracts mathematical formulas from images and returns them in LaTeX format.

Python

python
import os
import dashscope

dashscope.base_http_api_url = 'https://{WorkspaceId}.ap-southeast-1.maas.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",\
                "min_pixels": 32 * 32 * 3,\
                "max_pixels": 32 * 32 * 8192,\
                "enable_rotate": False}]\
            }]

response = dashscope.MultiModalConversation.call(
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    model='qwen-vl-ocr-2025-11-20',
    messages=messages,
    ocr_options={"task": "formula_recognition"}
)
print(response["output"]["choices"][0]["message"].content[0]["text"])

Java

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.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 {
        Constants.baseHttpApiUrl="https://{WorkspaceId}.ap-southeast-1.maas.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");
        map.put("max_pixels", 8388608);
        map.put("min_pixels", 3072);
        map.put("enable_rotate", false);

        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()
                .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);
    }
}

curl

bash
curl --location 'https://{WorkspaceId}.ap-southeast-1.maas.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 ​

Extracts plain text from images without structural formatting.

Python

python
import os
import dashscope

dashscope.base_http_api_url = 'https://{WorkspaceId}.ap-southeast-1.maas.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",\
                "min_pixels": 32 * 32 * 3,\
                "max_pixels": 32 * 32 * 8192,\
                "enable_rotate": False}]\
            }]

response = dashscope.MultiModalConversation.call(
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    model='qwen-vl-ocr-2025-11-20',
    messages=messages,
    ocr_options={"task": "text_recognition"}
)
print(response["output"]["choices"][0]["message"].content[0]["text"])

Java

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.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 {
        Constants.baseHttpApiUrl="https://{WorkspaceId}.ap-southeast-1.maas.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");
        map.put("max_pixels", 8388608);
        map.put("min_pixels", 3072);
        map.put("enable_rotate", false);

        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()
                .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);
    }
}

curl

bash
curl --location 'https://{WorkspaceId}.ap-southeast-1.maas.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 ​

Recognizes text in multiple languages from images.

Python

python
import os
import dashscope

dashscope.base_http_api_url = 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1'

messages = [{\
            "role": "user",\
            "content": [{\
                "image": "https://img.alicdn.com/imgextra/i2/O1CN01VvUMNP1yq8YvkSDFY_!!6000000006629-2-tps-6000-3000.png",\
                "min_pixels": 32 * 32 * 3,\
                "max_pixels": 32 * 32 * 8192,\
                "enable_rotate": False}]\
            }]

response = dashscope.MultiModalConversation.call(
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    model='qwen-vl-ocr-2025-11-20',
    messages=messages,
    ocr_options={"task": "multi_lan"}
)
print(response["output"]["choices"][0]["message"].content[0]["text"])

Java

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.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 {
        Constants.baseHttpApiUrl="https://{WorkspaceId}.ap-southeast-1.maas.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");
        map.put("max_pixels", 8388608);
        map.put("min_pixels", 3072);
        map.put("enable_rotate", false);

        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()
                .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);
    }
}

curl

bash
curl --location 'https://{WorkspaceId}.ap-southeast-1.maas.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"
    }
  }
}
'

Streaming (DashScope) ​

Enable streaming output to receive results incrementally. The method varies by SDK:

  • Python SDK: Set stream=True and incremental_output=True.

  • Java SDK: Use the streamCall interface.

  • HTTP: Set the X-DashScope-SSE: enable header.

Python

python
import os
import dashscope

dashscope.base_http_api_url = 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1'

PROMPT_TICKET_EXTRACTION = """
Please extract the invoice number, train number, departure station, arrival station, departure date and time, seat number, seat class, ticket price, ID card number, and passenger name from the train ticket image.
You must accurately extract the key information. Do not omit or fabricate information. Replace any single character that is blurry or obscured by strong light with an English question mark (?).
Return the data in JSON format as follows: {'invoice_number': 'xxx','departure_station': 'xxx', 'arrival_station': 'xxx', 'departure_date_and_time':'xxx', 'seat_number': 'xxx','ticket_price':'xxx', 'id_card_number': 'xxx', 'passenger_name': 'xxx'},
"""

messages = [\
    {\
        "role": "user",\
        "content": [\
            {\
                "image": "https://img.alicdn.com/imgextra/i2/O1CN01ktT8451iQutqReELT_!!6000000004408-0-tps-689-487.jpg",\
                "min_pixels": 32 * 32 * 3,\
                "max_pixels": 32 * 32 * 8192},\
            {\
                "type": "text",\
                "text": PROMPT_TICKET_EXTRACTION\
            }\
        ]\
    }\
]

response = dashscope.MultiModalConversation.call(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="qwen-vl-ocr-2025-11-20",
    messages=messages,
    stream=True,
    incremental_output=True,
)
full_content = ""
print("Streaming output content:")
for response in response:
    try:
        print(response["output"]["choices"][0]["message"].content[0]["text"])
        full_content += response["output"]["choices"][0]["message"].content[0]["text"]
    except:
        pass
print(f"Full content: {full_content}")

Java

java
import java.util.*;

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 {
        Constants.baseHttpApiUrl="https://{WorkspaceId}.ap-southeast-1.maas.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");
        map.put("max_pixels", 8388608);
        map.put("min_pixels", 3072);
        MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
                .content(Arrays.asList(
                        map,
                        Collections.singletonMap("text", "Please extract the invoice number, train number, departure station, arrival station, departure date and time, seat number, seat class, ticket price, ID card number, and passenger name from the train ticket image. You must accurately extract the key information. Do not omit or fabricate information. Replace any single character that is blurry or obscured by strong light with an English question mark (?). Return the data in JSON format as follows: {\'invoice_number\': \'xxx\', \'departure_station\': \'xxx\', \'arrival_station\': \'xxx\', \'departure_date_and_time\':\'xxx\', \'seat_number\': \'xxx\',\'ticket_price\':\'xxx\', \'id_card_number\': \'xxx\', \'passenger_name\': \'xxx\'"))).build();
        MultiModalConversationParam param = MultiModalConversationParam.builder()
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .model("qwen-vl-ocr-2025-11-20")
                .message(userMessage)
                .incrementalOutput(true)
                .build();
        Flowable<MultiModalConversationResult> result = conv.streamCall(param);
        result.blockingForEach(item -> {
            try {
                List<Map<String, Object>> contentList = item.getOutput().getChoices().get(0).getMessage().getContent();
                if (!contentList.isEmpty()){
                    System.out.println(contentList.get(0).get("text"));
                }//
            } catch (Exception e){
                System.exit(0);
            }
        });
    }

    public static void main(String[] args) {
        try {
            simpleMultiModalConversationCall();
        } catch (ApiException | NoApiKeyException | UploadFileException e) {
            System.out.println(e.getMessage());
        }
        System.exit(0);
    }
}

curl

bash
curl --location 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--header 'X-DashScope-SSE: enable' \
--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\
                  },\
                  {"type": "text", "text": "Please extract the invoice number, train number, departure station, arrival station, departure date and time, seat number, seat class, ticket price, ID card number, and passenger name from the train ticket image. You must accurately extract the key information. Do not omit or fabricate information. Replace any single character that is blurry or obscured by strong light with an English question mark (?). Return the data in JSON format as follows: {\'invoice_number\': \'xxx\', \'departure_station\': \'xxx\', \'arrival_station\': \'xxx\', \'departure_date_and_time\':\'xxx\', \'seat_number\': \'xxx\',\'ticket_price\':\'xxx\', \'id_card_number\': \'xxx\', \'passenger_name\': \'xxx\'}"}\
              ]\
            }\
        ]
    },
    "parameters": {
        "incremental_output": true
    }
}'

Request parameters ​

ParameterTypeRequiredDescription
ParameterTypeRequiredDescription
modelstringYesModel name. See Recommended models for supported models.
input.messagesarrayYesAn array of message objects.

Message object

Each message requires a role (must be user) and a content field (string or array). Use a string for text-only input. Use an array if the input includes image data, with these fields:

ParameterTypeRequiredDescription
ParameterTypeRequiredDescription
imagestringNoURL, Base64 Data URL, or local path of the image. See Passing local files.
textstringNoThe text prompt. Default: "Please output only the text content from the image without any additional descriptions or formatting". Not required when using a built-in task.
enable_rotatebooleanNoSet to true to correct skewed images. Default: false.
min_pixelsintegerNoMinimum pixel threshold. See Image resolution control.
max_pixelsintegerNoMaximum pixel threshold. See Image resolution control.

Generation parameters

Set these in the parameters object for HTTP calls.

ParameterTypeDefaultDescription
ParameterTypeDefaultDescription
max_tokensintegerVariesMaximum tokens in the output. See Output token limits. In the Java SDK, use maxTokens.
streambooleanfalseEnable streaming output. Python SDK only. For Java, use streamCall. For HTTP, set X-DashScope-SSE: enable.
incremental_outputbooleanfalseWhen true (recommended), each chunk contains only new content. When false, each chunk contains the full sequence so far. In the Java SDK, use incrementalOutput.
temperaturefloat0.01Controls output diversity. Range: [0, 2).
top_pfloat0.001Nucleus sampling threshold. Range: (0, 1.0]. Set either temperature or top_p, not both.
top_kinteger1Limits the candidate token set during sampling. If the value is None or greater than 100, the top_k policy is not enabled, and only the top_p policy takes effect. Must be >= 0.
repetition_penaltyfloat1.0Penalty for repeated sequences. Values above 1.0 reduce repetition.
presence_penaltyfloat0.0Controls content repetition. Range: [-2.0, 2.0].
seedinteger--Ensures reproducible results. Range: [0, 2^31 - 1].
logprobsbooleanfalseSet to true to return log probabilities. Supported models: qwen-vl-ocr-2025-04-13 and later. In the Java SDK, use the same name. For HTTP, place in parameters.
top_logprobsinteger0Number of most likely tokens per step. Range: [0, 5]. Only effective when logprobs is true. In the Java SDK, use topLogprobs. For HTTP, place in parameters.
stopstring or array--Stop words or token IDs. Generation stops when a specified string or token_id appears. Do not mix strings and token_ids in the same array.

Built-in task parameters (ocr_options )

When using a built-in task, pass ocr_options in parameters (HTTP), as a keyword argument (Python SDK), or via the OcrOptions builder (Java SDK).

ParameterTypeRequiredDescription
ParameterTypeRequiredDescription
ocr_options.taskstringYesBuilt-in task name. Valid values: text_recognition, key_information_extraction, document_parsing, table_parsing, formula_recognition, multi_lan, advanced_recognition.
ocr_options.task_configobjectNoConfiguration for key_information_extraction.
ocr_options.task_config.result_schemaobjectNoJSON object specifying fields to extract. Keys are field names, values are optional descriptions for improved accuracy. Supports up to three nesting levels.

result_schema example:

json
"result_schema": {
     "invoice_number": "The unique identification number of the invoice, usually a combination of numbers and letters.",
     "issue_date": "The date the invoice was issued. Extract it in YYYY-MM-DD format, for example, 2023-10-26.",
     "seller_name": "The full company name of the seller shown on the invoice.",
     "total_amount": "The total amount on the invoice, including tax. Extract the numerical value and keep two decimal places, for example, 123.45."
}

In the Java SDK, this parameter is OcrOptions. The minimum DashScope Python SDK version is 1.22.2. The minimum Java SDK version is 2.18.4. For advanced_recognition, Java SDK >= 2.21.8 is required.

Response ​

The DashScope API uses identical response format for streaming and non-streaming output.

json
{
  "status_code": 200,
  "request_id": "8f8c0f6e-6805-4056-bb65-d26d66080a41",
  "code": "",
  "message": "",
  "output": {
    "text": null,
    "finish_reason": null,
    "choices": [\
      {\
        "finish_reason": "stop",\
        "message": {\
          "role": "assistant",\
          "content": [\
            {\
              "ocr_result": {\
                "kv_result": {\
                  "price_excluding_tax": "230769.23",\
                  "invoice_code": "142011726001",\
                  "organization_code": "null",\
                  "buyer_name": "Cai Yingshi",\
                  "seller_name": "null"\
                }\
              },\
              "text": "```json\n{\n    \"price_excluding_tax\": \"230769.23\",\n    \"invoice_code\": \"142011726001\",\n    \"organization_code\": \"null\",\n    \"buyer_name\": \"Cai Yingshi\",\n    \"seller_name\": \"null\"\n}\n```"\
            }\
          ]\
        }\
      }\
    ],
    "audio": null
  },
  "usage": {
    "input_tokens": 926,
    "output_tokens": 72,
    "characters": 0,
    "image_tokens": 754,
    "input_tokens_details": {
      "image_tokens": 754,
      "text_tokens": 172
    },
    "output_tokens_details": {
      "text_tokens": 72
    },
    "total_tokens": 998
  }
}
FieldTypeDescription
FieldTypeDescription
status_codestring200 indicates success. The Java SDK throws an exception instead of returning this field.
request_idstringUnique request identifier. In the Java SDK, this is requestId.
codestringError code. Empty on success. Only the Python SDK returns this field.
output.textstringAlways null.
output.finish_reasonstringnull during generation, stop when complete, length when truncated.
output.choices[].finish_reasonstringSame values as output.finish_reason.
output.choices[].message.rolestringAlways assistant.
output.choices[].message.content[].textstringExtracted text or formatted output from the model.
output.choices[].message.content[].ocr_resultobjectReturned for built-in tasks (key_information_extraction, advanced_recognition).
output.choices[].message.content[].ocr_result.kv_resultobjectKey-value extraction results (for key_information_extraction).
output.choices[].message.content[].ocr_result.words_infoarrayText line results with positional data (for advanced_recognition).
output.choices[].message.content[].ocr_result.words_info[].rotate_rectarray[center_x, center_y, width, height, angle] -- rotated bounding rectangle.
output.choices[].message.content[].ocr_result.words_info[].locationarray[x1, y1, x2, y2, x3, y3, x4, y4] -- four vertices clockwise from the top-left.
output.choices[].message.content[].ocr_result.words_info[].textstringContent of the text line.
output.choices[].message.logprobsobjectLog probability information, returned when logprobs is true.
usage.input_tokensintegerInput token count.
usage.output_tokensintegerOutput token count.
usage.charactersintegerFixed to 0.
usage.total_tokensintegerSum of input_tokens and output_tokens.
usage.image_tokensintegerTokens corresponding to the image input.
usage.input_tokens_details.image_tokensintegerImage input tokens.
usage.input_tokens_details.text_tokensintegerText input tokens.
usage.output_tokens_details.text_tokensintegerText output tokens.

Supported models ​

ModelDescription
ModelDescription
qwen-vl-ocr-latestAlways points to the latest version.
qwen-vl-ocr-2025-11-20Latest dated snapshot.
qwen-vl-ocr-2025-08-28Previous version.
qwen-vl-ocr-2025-04-13Previous version.
qwen-vl-ocr-2024-10-28Previous version.
qwen-vl-ocrBase model.

Error codes ​

If a model call returns an error, see Error messages to resolve the issue.

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