Ali

Qwen3-Max

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

Qwen3-Max 是在 Qwen3 系列的基础上构建的更新版本,与 2025 年 1 月版本相比,在推理、指令遵循、多语言支持和长尾知识覆盖方面进行了重大改进。它在数学、编码、逻辑和科学任务中提供更高的准确性,更可靠地遵循复杂的中文和英文指令,减少幻觉,并为开放式问答、写作和对话提供更高质量的回答。该模型支持 100 多种语言,具有更强的翻译和常识推理能力,并针对检索增强生成 (RAG) 和工具调用进行了优化,尽管它不包括专用的“思考”模式。

Input / output modalities
文本 to 文本
Reference input / output price
Input¥2.5Output¥10per 1M tokens
Context window
262.1K
Added to catalog
Sep 24, 2025

Providers and pricing

ProviderInput lengthInput /MOutput /MCached /MContextMax outputDetails
Ali≤ 32K¥2.5¥10¥0.5262.1K32.8K
32K – 128K¥4¥16¥0.8
128K – 256K¥7¥28¥1.4
32K – 256K——¥4
256K – 1M——¥0.7

Ali

Latency
2.26s
Throughput
116 tokens/s
Context
262.1K

Pricing

Input
¥2.5/M tokens
Output
¥10/M tokens
Cached
¥0.5/M tokens

Tiered pricing

Pricing varies by input token range.

0–32K Token

Input tier
¥2.5/M tokens
Output tier
¥10/M tokens
Cached tier
¥0.5/M tokens
Cache read tier
¥0.25/M tokens
5-minute cache write tier
¥3.13/M tokens

32K–128K Token

Input tier
¥4/M tokens
Output tier
¥16/M tokens
Cached tier
¥0.8/M tokens

128K–256K Token

Input tier
¥7/M tokens
Output tier
¥28/M tokens
Cached tier
¥1.4/M tokens

32K–256K Token

Cache read tier
¥4/M tokens
5-minute cache write tier
¥5/M tokens

256K–1M Token

Cache read tier
¥0.7/M tokens
5-minute cache write tier
¥8.75/M tokens

Additional pricing

Built in tools · Web search
¥0.004
Built in tools · I2i search
¥0.004
Built in tools · T2i search
¥0.004

Specifications

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

Qwen3-Max 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", 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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