Qwen3.5-Flash

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ReasoningText GenerationVisual Understanding

Overview

ReasoningText GenerationVisual Understanding

The Qwen3.5 native vision-language Flash models are built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. Compared to the 3 series, these models deliver a leap forward in performance for both pure text and multimodal tasks, offering fast response times while balancing inference speed and overall performance.

Input

TextImageVideo

Output

Text

Features

Prefix Completion

Function Calling

Cache

Structured Outputs

Batches

Web Search

Pricing

Input
$0.1Per 1M tokens
Output
$0.4Per 1M tokens
Explicit Cache Creation
$0.125Per 1M tokens
Explicit Cache Read
$0.01Per 1M tokens

Context

Standard

Context

Max Input

991.80K

Max Output

65.53K

Thinking

Context

Max Input

983.61K

Max Output

65.53K

Max Reasoning

81.92K

Rate Limits

RPMRequests Per Minute
15K
TPMTokens Per Minute
5M

Built-in Tools

web_searchResponses API

web_extractorResponses API

code_interpreterResponses API

t2i_searchResponses API

i2i_searchResponses API

API Reference

Get API Key

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import os
import dashscope
dashscope.base_http_api_url = "https://dashscope-intl.aliyuncs.com/api/v1"

messages = [
    {
        "role": "user",
        "content": [
            {"image": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"},
            {"text": "What is depicted in the image?"}]
    }]
response = dashscope.MultiModalConversation.call(
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    model='qwen3.5-flash',
    messages=messages
)
print(response.output.choices[0].message.content[0]["text"])

import os
import dashscope
dashscope.base_http_api_url = "https://dashscope-intl.aliyuncs.com/api/v1"

messages = [
    {
        "role": "user",
        "content": [
            {"image": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"},
            {"text": "What is depicted in the image?"}]
    }]
response = dashscope.MultiModalConversation.call(
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    model='qwen3.5-flash',
    messages=messages
)
print(response.output.choices[0].message.content[0]["text"])