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Qwen3.5 Flash
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Qwen3.5 Flash

qwen3.5-flash
Qwen
The Qwen3.5 native vision-language Flash series models are designed with a hybrid architecture that integrates linear attention mechanisms and sparse mixture-of-experts models, achieving higher inference efficiency. Compared with the 3 series, the models deliver leapfrog improvements in both pure-text and multimodal performance; they respond quickly and combine inference speed with high performance.

Pricing

TierPricingWeb SearchCache WriteCache Read
Input<=128K
$0.028$0.282
$0.000548/request$0.03525/M tokens$0.00282/M tokens
128K<Input<=256K
$0.113$1.126
$0.000548/request$0.14075/M tokens$0.01126/M tokens
256K<Input<=1000K
$0.169$1.690
$0.000548/request$0.21125/M tokens$0.0169/M tokens

Input Modalities

  • Text
  • Vision
  • Video

Output Modalities

  • Text

Capabilities

  • Thinking
  • Web
  • Tools
  • Tool calling
  • Structured outputs
  • Long context

Providers

Alibaba Cloud alicloud-qwen3.5-flash
Pricing$0.028$0.282
Web Search$0.000548/request
Cache Write$0.03525/M tokens
Cache Read$0.00282/M tokens
Pricing$0.113$1.126
Web Search$0.000548/request
Cache Write$0.14075/M tokens
Cache Read$0.01126/M tokens
Pricing$0.169$1.690
Web Search$0.000548/request
Cache Write$0.21125/M tokens
Cache Read$0.0169/M tokens
Context991K
Max output64K
Latency0.9S
Throughput125.1TPS
Uptime
100.00% uptime 2 days ago
100.00% uptime yesterday
100.00% uptime today

Performance for qwen3.5-flash

Uptime is the percentage of requests that succeeded over the past 72 hours. AIHubMix continuously monitors every provider and automatically retries with the next-best provider when one returns an error or responds too slowly; Latency is total round-trip time (lower is better); Throughput is how fast the model writes (tokens per second, higher is better).

Uptime
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Latency
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Throughput
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Try this model

Python
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AIHUBMIX_API_KEY"],
    base_url="https://aihubmix.com/v1",
)

response = client.chat.completions.create(
    model="qwen3.5-flash",
    messages=[
      {
        "role": "user",
        "content": "Hello, how are you?"
      }
    ],
    max_tokens=1024,
    stream=False,
)

print(response.choices[0].message.content)

Frequently asked questions

What is Qwen3.5 Flash?

The Qwen3.5 native vision-language Flash series models are designed with a hybrid architecture that integrates linear attention mechanisms and sparse mixture-of-experts models, achieving higher inference efficiency. Compared with the 3 series, the models deliver leapfrog improvements in both pure-text and multimodal performance; they respond quickly and combine inference speed with high performance.
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