Ali

QvQ-72b

ali/qvq-72b
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

QVQ-72B是阿里云通义千问团队开发的多模态推理模型,拥有720亿参数,具备强大的视觉理解和推理能力。该模型在解决数学、物理、科学等领域的复杂推理问题上表现突出,能够处理需要同时理解文本和图像的任务。

Input / output modalities
文本 to 文本
Reference input / output price
Input¥12Output¥36per 1M tokens
Context window
131.1K
Added to catalog
Feb 20, 2025

Providers and pricing

ProviderInput /MOutput /MCached /MContextMax outputDetails
Ali¥12¥36¥0131.1K8.2K

Ali

Latency
0.48s
Throughput
38 tokens/s
Context
131.1K

Pricing

Input
¥12/M tokens
Output
¥36/M tokens
Cached
¥0/M tokens

Specifications

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

QvQ-72b 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/qvq-72b", 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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