Qwen3.5 Flash

Name: Qwen3.5 Flash API
Brand: 阿里云
SKU: qwen3.5-flash

silvamux/qwen3.5-flash

Supported modalities

text

Input price$0.029-0.17per 1M tokens

Output price$0.287-1.7per 1M tokens

Pricing

Tiers are determined by the input context length of each request; Tune and Pro share the same tiered prices.

Unit: per 1M tokens

Input context	Input	Output	Cache read	Cache write
≤ 128K tokens	$0.029	$0.287	$0.003	$0.036
≤ 256K tokens	$0.12	$1.15	$0.012	$0.14
≤ 1000K tokens	$0.17	$1.7	$0.017	$0.215

Capabilities

Evaluation data not available yet

Performance

Qwen3.5 Flash

qwen3.5-flash

Last 24 hours · 127,141 requests

First token · TTFT · P50: 2437ms
Output speed · TPS · P50: 84tok/s
Full response · E2E · P50: 9.95s
Request success rate: 98.50%

24-hour availability

24 hours ago12 hours agoNow

Qwen3.5 Flash example code and API

Proqwen3.5-flash

/api/v0/chat/completions/api/v1/chat/completions

UnifiedOpenAI

curl https://silvamux.io/api/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3.5-flash",
    "messages": [{"role": "user", "content": "Hello, please introduce yourself"}]
  }'

# pip install openai
from openai import OpenAI

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

response = client.chat.completions.create(
    model="qwen3.5-flash",
    messages=[
        {"role": "user", "content": "Hello, please introduce yourself"}
    ],
)

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

// Gradle: implementation(__HL_SLOT_0__)
import okhttp3.*;

OkHttpClient client = new OkHttpClient();
MediaType json = MediaType.get("application/json");
String payload = "{\"model\":\"qwen3.5-flash\",\"messages\":[{\"role\":\"user\",\"content\":\"Hello, please introduce yourself\"}]}";

Request request = new Request.Builder()
    .url("https://silvamux.io/api/v1/chat/completions")
    .header("Authorization", "Bearer YOUR_API_KEY")
    .post(RequestBody.create(payload, json))
    .build();

try (Response response = client.newCall(request).execute()) {
    System.out.println(response.body().string());
}

// npm install openai
import OpenAI from 'openai'

const client = new OpenAI({
  baseURL: 'https://silvamux.io/api/v1',
  apiKey: 'YOUR_API_KEY',
})

const response = await client.chat.completions.create({
  model: 'qwen3.5-flash',
  messages: [{ role: 'user', content: 'Hello, please introduce yourself' }],
})

console.log(response.choices[0].message.content)