Ali

Qwen3-Max-Preview

ali/qwen3-max-preview
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

Qwen3-Max-Preview是阿里巴巴旗下通义千问团队发布的最新旗舰大语言模型,是 Qwen3 系列中参数量最大的模型,参数规模超过1万亿。模型在推理、指令跟随、多语言支持和长尾知识覆盖等方面有重大改进,支持超过100种语言,中英文理解能力出色。在数学推理、编程和科学推理等任务中表现出色,能更可靠地遵循复杂指令,减少幻觉,生成更高质量的响应。

Input / output modalities
文本 to 文本
Reference input / output price
Input¥6Output¥24per 1M tokens
Context window
262.1K
Added to catalog
Sep 8, 2025

Providers and pricing

ProviderInput lengthInput /MOutput /MCached /MContextMax outputDetails
Ali≤ 32K¥6¥24¥1.2262.1K32.8K
32K – 128K¥10¥40¥2
≥ 128K¥15¥60¥3

Ali

Latency
0.51s
Throughput
69 tokens/s
Context
262.1K

Pricing

Input
¥6/M tokens
Output
¥24/M tokens
Cached
¥1.2/M tokens

Tiered pricing

Pricing varies by input token range.

0–32K Token

Input tier
¥6/M tokens
Output tier
¥24/M tokens
Cached tier
¥1.2/M tokens

32K–128K Token

Input tier
¥10/M tokens
Output tier
¥40/M tokens
Cached tier
¥2/M tokens

128K–∞ Token

Input tier
¥15/M tokens
Output tier
¥60/M tokens
Cached tier
¥3/M tokens

Specifications

Context
262.1K
Max output
32.8K
Supported APIs
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

Qwen3-Max-Preview 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/qwen3-max-preview", 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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