Qwen3.5-Flash-2026-02-23

Qwen3.5-Flash

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Visual UnderstandingText GenerationReasoning

Overview

Visual UnderstandingText GenerationReasoning

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.This version is a snapshot as of February 23, 2026.

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

Context

Max Input

991.80K

Max Output

65.53K

Rate Limits

RPMRequests Per Minute
60
TPMTokens Per Minute
1M

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-2026-02-23',
    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-2026-02-23',
    messages=messages
)
print(response.output.choices[0].message.content[0]["text"])