Qwen3.5-27B

Alibaba Qwen chatcodereasoningtool_usevideovision Long

API ID: qwen/qwen3.5-27b-20260224

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Input Price
$0.26
/1M tokens
Output Price
$2.60
/1M tokens

About Qwen3.5-27B

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.

💰
Price Ranking
#1005 lowest price among 1240 Chat models

Model Specifications

Context Length
262k
Max Output
82k
Release Date
2026-02-25
Capabilities
chat code reasoning tool_use video vision
Input Modalities
textimagevideo
Output Modalities
text

Best For

  • Complex reasoning, math problems, multi-step logic
  • Code generation, debugging, code review, refactoring
  • Image analysis, document understanding, visual Q&A
  • Conversations, content writing, general assistance

Consider Alternatives For

  • Simple Q&A (cheaper models available)

💰 Real-World Cost Examples

Estimated monthly costs for common use cases

Personal AI Assistant
$0.90
/month
50 conversations/day, ~500 tokens each
Code Copilot
$7.49
/month
200 completions/day, ~1k tokens each
Customer Service Bot
$27.30
/month
1000 tickets/day, ~800 tokens each
Data Analysis Pipeline
$37.05
/month
500 analyses/day, ~2k tokens each
Calculate Your Custom Usage

Alibaba Qwen Model Lineup

Compare all models from Alibaba Qwen to find the best fit

Model Input Output Context Capabilities
Qwen3.5-27B Current Free Free 262k chat code reasoning tool_use video vision
Qwen2.5-VL 7B Instruct Free Free 33k chat vision tool_use
Qwen3 Embedding 0.6B Free Free 8k chat code
Qwen3 Embedding 0.6B Free Free 8k chat code
Qwen2.5 VL 3B Instruct Free Free 64k chat vision code
Qwen2.5 VL 3B Instruct Free Free 64k chat vision code
View All Alibaba Qwen Models →

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🚀 Quick Start

Get started with Qwen3.5-27B API

OpenAI-compatible SDK 
from openai import OpenAI

client = OpenAI(
    base_url="https://api.provider.com/v1",
    api_key="YOUR_API_KEY"
)

response = client.chat.completions.create(
    model="qwen/qwen3.5-27b-20260224",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)
print(response.choices[0].message.content)

Resources

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