Ali

Qwen-VL-OCR

ali/qwen-vl-ocr
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

通义千问VL-OCR(qwen-vl-ocr),即基于Qwen-VL训练的OCR识别大模型。通过统一模型的方式聚合多种图文识别、解析、处理类任务,提供强大的图文识别能力。

Input / output modalities
文本 · 图像 to 文本
Reference input / output price
Input¥0.3Output¥0.5per 1M tokens
Context window
32K
Added to catalog
Nov 28, 2025

Providers and pricing

ProviderInput /MOutput /MCached /MContextMax outputDetails
Ali¥0.3¥0.5¥032K8.2K

Ali

Latency
0.56s
Throughput
117 tokens/s
Context
32K

Pricing

Input
¥0.3/M tokens
Output
¥0.5/M tokens
Cached
¥0/M tokens

Specifications

Context
32K
Max output
8.2K
Supported APIs
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

Qwen-VL-OCR code examples and API guide

Modelmesh normalizes requests and responses across service providers behind one consistent API.

Modelmesh provides an OpenAI-compatible Completion API for more than 300 models and service providers. Call it directly, through the OpenAI SDK, or with supported third-party SDKs.

Modelmesh-specific request headers in these examples are optional. When supplied, your application can appear on the Modelmesh rankings.

Supported endpointsSelect an endpoint to switch the example below.
/v1/chat/completions
from openai import OpenAI API_KEY = "$SSY_API_KEY" client = OpenAI( base_url="https://router.shengsuanyun.com/api/v1", api_key=API_KEY, ) try: completion = client.chat.completions.create( model="ali/qwen-vl-ocr", messages=[{"role": "user", "content": "Which number is larger, 9.11 or 9.8?"}], temperature=0.6, top_p=0.7, stream=True, ) response_text = "" for chunk in completion: if chunk.choices and chunk.choices[0].delta.content is not None: content = chunk.choices[0].delta.content print(content, end="", flush=True) response_text += content except Exception as error: print(f"Request failed: {error}")
                
              

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