Qwen Doc: Find the Documentation and Choose a Document API

“Qwen doc” may mean finding Qwen documentation or using a Qwen model to work with documents. For documentation discovery, start at the Qwen documentation landing page. It describes the Qwen model family, but the captured page does not itself give document-processing instructions or API setup.

For document processing, Alibaba Cloud Model Studio has separate guides for Qwen-Doc-Turbo, Qwen-Long, and PDF understanding. Check each guide’s region and input requirements before choosing a route; these are Model Studio options, not interchangeable generic Qwen endpoints. (help.aliyun.com)

Which Qwen document option fits your task?

If you need to…	Investigate…	Verified conditions
Extract fields, classify or moderate content, or summarize documents	Qwen-Doc-Turbo	Model Studio documents these data-mining tasks and lists file URL, file-ID, and plain-text inputs. The guide applies to China (Beijing), and the file-URL method uses the DashScope protocol. (alibabacloud.com)
Work with very long files through upload and file-ID references	Qwen-Long	Its guide describes uploading files and then referencing their IDs. The guide applies to China (Beijing). Direct HTTP requests support up to 1 million tokens; the provider recommends file submission for longer content. The file-based maximum is not established here. (alibabacloud.com)
Analyze a PDF’s text and images	Model Studio PDF understanding	The guide lists qwen3.8-max, qwen3.8-max-0902, qwen3.8-flash, and qwen3.8-27b. PDF understanding is available in China (Beijing) and Singapore, but qwen3.8-27b is Beijing-only. The documented interface is OpenAI-compatible Chat Completions, with PDF supplied by URL or Base64. (help.aliyun.com)
Use a general Qwen model	Check that model’s specific API and input documentation	Qwen-family descriptions do not establish that every hosted model accepts document files or is suitable for a particular document task.

These are starting points, not guarantees of accuracy or availability for your account. Before selecting, identify whether you need text, images or scans, extraction, question answering, or long-context analysis—and verify support for those inputs on the exact model and API route.

Qwen-Doc-Turbo: a documented file-URL API example

The Model Studio guide describes Qwen-Doc-Turbo for information extraction, moderation, classification, and summaries. It lists three input methods: public file URL, uploaded file referenced by file ID, and plain text. Its comparison lists up to 10 files for the URL method, up to 253,000 tokens for file-ID input, and a 9,000-token limit for plain text. The guide applies to China (Beijing) and requires a Beijing-region API key. The file-URL method supports DashScope, not the OpenAI-compatible protocol. (help.aliyun.com)

Here is a minimal synchronous Python example following the provider’s DashScope SDK pattern. Replace the workspace ID and public document URL, set DASHSCOPE_API_KEY to a China (Beijing) Model Studio key, and install the DashScope SDK before running it. The URL method accepts up to 10 public URLs per request; check the provider’s limitations guide for file-type and size eligibility. (help.aliyun.com)

import os
import dashscope

dashscope.base_http_api_url = (
    "https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1"
)

response = dashscope.Generation.call(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="qwen-doc-turbo",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Summarize the key facts in this document.",
                },
                {
                    "type": "doc_url",
                    "doc_url": ["https://example.com/public-document.docx"],
                    "file_parsing_strategy": "auto",
                },
            ],
        },
    ],
)

if response.status_code == 200:
    print(response.output.choices[0].message.content)
else:
    print("Request failed:", response.status_code)
    print("Error code:", response.code)
    print("Error message:", response.message)

The generated answer is in response.output.choices[0].message.content. In this synchronous example, a returned status_code of 200 is the documented success check; a non-200 response provides code and message for error details. The guide also wraps the call in exception handling, so handle SDK/runtime exceptions separately. Do not infer document processing succeeded from an HTTP status alone if the response body does not contain the expected answer. (help.aliyun.com)

When Qwen-Long may fit

Qwen-Long’s documented workflow is to upload a file, receive a file ID, and reference that ID in a model request. The guide says upload, storage, and parsing are not charged; tokens in referenced file content count as input tokens on each model call. Its documented scope is China (Beijing). (alibabacloud.com)

The model information page lists text formats including TXT, DOCX, PDF, XLSX, EPUB, MOBI, MD, and CSV, and image formats including BMP, PNG, JPG/JPEG, GIF, and PDF scans. It states that direct HTTP requests support up to 1 million tokens and recommends file submission for longer content. The cited information does not establish a maximum token limit for file submission, so do not treat this workflow as a verified 10-million-token capacity. (alibabacloud.com)

PDF understanding is a separate API choice

The PDF-understanding guide says PDFs can be supplied by URL or Base64 and names the supported models and regional conditions in the table above. It explicitly says not to send PDF input through the Responses API: in that case, the call can return HTTP 200 without delivering the PDF to the model. Use the documented OpenAI-compatible Chat Completions route for this PDF workflow, and validate that the model’s response actually reflects the supplied document. (help.aliyun.com)

Validate on representative documents before production

No model choice establishes your extraction accuracy in advance. Before production, evaluate a representative set of documents using the exact model, region, and input route you plan to deploy:

1. Include the real document variations and formats you expect, but only where the chosen route documents support them.
2. Define correctness for each field, including date, currency, and missing-value handling.
3. Compare model output with annotated expected answers; inspect omissions, unsupported values, and formatting errors.
4. Check response parsing and error handling using the provider’s route-specific request and response documentation.
5. Repeat the evaluation if you change the model ID, input route, prompt, or preprocessing.

This is a suggested validation process, not a report of tests performed. If your deployment must run outside China (Beijing), do not assume Qwen-Doc-Turbo or Qwen-Long is available there based on the cited guides. For PDF understanding, use only a model and region listed in its guide, and verify account availability before building around it. (help.aliyun.com)

Sources

Qwen documentation landing page

Qwen-Doc-Turbo — Alibaba Cloud Model Studio

Qwen-Long — Alibaba Cloud Model Studio

Qwen-Long model information — Alibaba Cloud Model Studio

PDF understanding via VL model — Alibaba Cloud Model Studio

Streaming output for Qwen models — Alibaba Cloud Model Studio