text·image·video
Qwen3.5 Flash
silvamux/
qwen3.5-flashQwen3.5 Flash是一款面向高频处理场景的语言模型,数学推理、幻觉控制和文本理解较为突出,适合批量问答、信息抽取与需要稳定响应的分析任务。
Pricing
Tiers are determined by the input context length of each request; Tune and Pro share the same tiered prices.
| Input context | Input | Output | Cache read | Cache write |
|---|---|---|---|---|
| ≤ 131.072K tokens | ¥0.2 | ¥2 | ¥0.02 | ¥0.25 |
| ≤ 262.144K tokens | ¥0.8 | ¥8 | ¥0.08 | ¥1 |
| > 262.144K tokens | ¥1.2 | ¥12 | ¥0.12 | ¥1.5 |
Capabilities
Qwen3.5 Flash 综合得分 76,整体表现良好,是一款轻量高性价比模型。数学与科学推理(93%)、幻觉控制(87%)与文本理解(85%)表现突出,数值分析准确、输出可信,在数据分析与智能客服场景中有不错的性价比。相对而言,创意写作(61%)一般,创意类内容任务能力有限。对成本敏感的分析类任务最能体现它的价值。
76/ 100
专项进阶能力
8 items智能体协同79%
7 questions · 主/客观混合
代码生成80%
10 questions · 客观
代码理解与调试72%
12 questions · 主观
创意写作61%
17 questions · 主观
信息检索 / RAG71%
13 questions · 主/客观混合
长文本处理75%
3 questions · 主观
数学与科学推理93%
14 questions · 客观
多语言能力75%
10 questions · 主/客观混合
通用基础能力
3 items通识知识72%
15 questions · 主/客观混合
文本理解85%
5 questions · 主观
逻辑推理67%
20 questions · 主/客观混合
安全与合规能力
2 items幻觉控制87%
9 questions · 主观
指令遵循77%
14 questions · 主观
Performance
Qwen3.5 Flash
qwen3.5-flashLast 24 hours · 98,207 requests
- First token · TTFT · P50
- 2145ms P95 2146 ms
- Output speed · TPS · P50
- 78tok/s P95 88 tok/s
- Full response · E2E · P50
- 5.85s P95 9.25 s
- Request success rate
- 99.74% 97,950 completed requests
24-hour availability
24 hours ago12 hours agoNow
Qwen3.5 Flash example code and API
Pro
qwen3.5-flash/api/v0/chat/completions/api/v1/chat/completions/api/anthropic/v1/messagescurl https://www.silvamux.com/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://www.silvamux.com/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://www.silvamux.com/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://www.silvamux.com/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)