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Ali

QVQ-Plus

ali/qvq-plus
/v1/responses/v1beta/models/*/v1/models/*/v1/messages/v1/chat/completions

千问QVQ视觉推理模型增强版,支持视觉输入及思维链输出,在数学、编程、视觉分析、创作以及通用任务上都表现了更强的能力

Input / output modalities
文本 · 图像 · 视频 to 文本
Reference input / output price
Input¥2Output¥5per 1M tokens
Context window
131.1K
Added to catalog
Feb 20, 2025

Providers and pricing

ProviderInput /MOutput /MCached /MContextMax outputDetails
Ali¥2¥5¥0131.1K8.2K

Ali

Latency
0.48s
Throughput
38 tokens/s
Context
131.1K

Pricing

Input
¥2/M tokens
Output
¥5/M tokens
Cached
¥0/M tokens

Specifications

Context
131.1K
Max output
8.2K
Supported APIs
/v1/responses/v1beta/models/*/v1/models/*/v1/messages/v1/chat/completions

QVQ-Plus 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/responses
from openai import OpenAI API_KEY = "$SSY_API_KEY" client = OpenAI( base_url="https://router.shengsuanyun.com/api/v1", api_key=API_KEY, ) messages = [ {"role": "user", "content": "Describe Romance of the Three Kingdoms in 100 words."}, ] response = client.responses.create( model="ali/qvq-plus", input=messages, ) print(response.output_text)
                
              

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