Gemini 3.5 Flash

Name: Gemini 3.5 Flash API
Brand: Google
SKU: gemini-3.5-flash

silvamux/gemini-3.5-flash

Gemini 3.5 Flash is a multimodal model built for scale, strengthening coding and agentic capability on top of fast responses — suited to real-time assistants, code workflows and high-concurrency content processing.

Supported modalities

text

Input price$0.3 / 1M tokensper 1M tokens

Output price$2.5 / 1M tokensper 1M tokens

Context1M

Max output66K

Pricing

Tune and Pro share the same prices.

Input context	Input	Output	Cache read	Cache write
-	$0.3 / 1M tokens	$2.5 / 1M tokens	$0.075 / 1M tokens	$0.3 / 1M tokens

Capabilities

Gemini 3.5 Flash scored 70 overall, performing well — an efficient all-rounder for scale. It stands out in general knowledge (92%), multilingual capability (82%) and instruction following (82%), answering reliably across languages while obeying format constraints, which fits customer support and knowledge-base Q&A at high request volume. Comparatively, code generation (42%) is weak; coding tasks should go elsewhere. For fast, well-behaved knowledge responses it is a sensible default.

Overall

70/ 100良好

HighlightsGeneral Knowledge 92%Multilingual Capability 82% · Instruction Following 82%

Best forCustomer Support / Enterprise Knowledge Q&A / Translation & LocalizationScenario fit 74%

Relative weaknessCode Generation42%

Advanced Capabilities

8 items

Agent Collaboration67%

18 questions · Mixed

Code Generation42%

12 questions · Objective

Code Comprehension & Debugging64%

6 questions · Mixed

Creative Writing60%

22 questions · Mixed

Information Retrieval / RAG67%

9 questions · Mixed

Long-Context Handling78%

16 questions · Mixed

Math & Science Reasoning77%

14 questions · Objective

Multilingual Capability82%

11 questions · Mixed

General Foundations

3 items

General Knowledge92%

10 questions · Mixed

Text Comprehension61%

23 questions · Mixed

Logical Reasoning67%

16 questions · Mixed

Safety & Compliance

2 items

Hallucination Control70%

15 questions · Mixed

Instruction Following82%

18 questions · Subjective

Performance

Gemini 3.5 Flash

gemini-3.5-flash

Last 24 hours · 101,698 requests

First token · TTFT · P50: 1014ms
Output speed · TPS · P50: 79tok/s
Full response · E2E · P50: 4.21s
Request success rate: 99.99%

24-hour availability

24 hours ago12 hours agoNow

Gemini 3.5 Flash example code and API

Progemini-3.5-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": "gemini-3.5-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="gemini-3.5-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\":\"gemini-3.5-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: 'gemini-3.5-flash',
  messages: [{ role: 'user', content: 'Hello, please introduce yourself' }],
})

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