Qwen3.5-27B - Qwen Cloud

Qwen3.5-Open-Source

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ReasoningVisual UnderstandingText Generation

Overview

ReasoningVisual UnderstandingText Generation

The Qwen3.5 27B native vision-language Dense model incorporates a linear attention mechanism, delivering fast response times while balancing inference speed and performance. Its overall capabilities are comparable to those of the Qwen3.5-122B-A10B.

Input

TextImageVideo

Output

Text

Features

Prefix Completion

Function Calling

Cache

Structured Outputs

Batches

Web Search

Pricing

Input
$0.3Per 1M tokens
Output
$2.4Per 1M tokens

Context

Standard

Context

262.14K

Max Input

260.09K

Max Output

65.53K

Thinking

Context

262.14K

Max Input

258.04K

Max Output

65.53K

Max Reasoning

81.92K

Rate Limits

RPMRequests Per Minute
600
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='qwen3.5-27b',
    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='qwen3.5-27b',
    messages=messages
)
print(response.output.choices[0].message.content[0]["text"])