Ali

Qwen3.5-Flash

ali/qwen3.5-flash
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

Qwen3.5原生视觉语言系列Flash模型,基于混合架构设计,融合了线性注意力机制与稀疏混合专家模型,实现了更高的推理效率。模型效果在纯文本与多模态方面相较3系列均实现飞跃式进步;响应速度快,兼具推理速度和性能。

Input / output modalities
文本 · 图像 · 视频 to 文本
Reference input / output price
Input¥0.2Output¥2per 1M tokens
Context window
1M
Added to catalog
Mar 15, 2026

Providers and pricing

ProviderInput lengthInput /MOutput /MCached /MContextMax outputDetails
Ali≤ 128K¥0.2¥2¥0.021M64K
128K – 256K¥0.8¥8¥0.08
256K – 1M¥1.2¥12¥0.12

Ali

Latency
2.74s
Throughput
95 tokens/s
Context
1M

Pricing

Input
¥0.2/M tokens
Output
¥2/M tokens
Cached
¥0.02/M tokens

Tiered pricing

Pricing varies by input token range.

0–128K Token

Input tier
¥0.2/M tokens
Output tier
¥2/M tokens
Cached tier
¥0.02/M tokens

128K–256K Token

Input tier
¥0.8/M tokens
Output tier
¥8/M tokens
Cached tier
¥0.08/M tokens

256K–1M Token

Input tier
¥1.2/M tokens
Output tier
¥12/M tokens
Cached tier
¥0.12/M tokens

Additional pricing

Tools · Web search preview
¥0.006

Specifications

Context
1M
Max output
64K
Supported APIs
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

Qwen3.5-Flash 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/qwen3.5-flash", 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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