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

Enable Partial Mode when calling the Qwen API to make the model continue strictly from your provided prefix text.View docs

Function Calling

Use function calling to connect large language models with external tools and systems.View docs

Cache

Context Cache stores shared prefixes for long-context requests to reduce repeated computation, improve latency, and lower cost.View docs

Structured Outputs

Structured Outputs help ensure the model returns a JSON string in the expected format.View docs

Batches

Web Search

Enable web search so the model can answer with real-time retrieved data.View docs

feature.funeTuning

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

toc.rateLimitsAndContext

Max Input
991.80K
Max Output
65.53K
RPMRequests Per Minute
15K
TPMTokens Per Minute
5M
contextField.maxInputThinking
983.61K
contextField.maxOutputThinking
65.53K
Context
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',
    messages=messages
)
print(response.output.choices[0].message.content[0]["text"])