Gemini generateContent API

curl "https://open.tutujin.com/v1beta/models/gemini-3.8-flash:generateContent" \
  -H "x-goog-api-key: $TUTUJIN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "systemInstruction": {"parts": [{"text": "You are an e-commerce copy assistant. Keep it short."}]},
    "contents": [
      {"role": "user", "parts": [{"text": "Write 3 selling points for a 500 ml insulated bottle"}]}
    ],
    "generationConfig": {"temperature": 0.7, "maxOutputTokens": 512}
  }'

import os
from google import genai
from google.genai import types

client = genai.Client(
    api_key=os.environ["TUTUJIN_API_KEY"],
    http_options=types.HttpOptions(base_url="https://open.tutujin.com"),
)

resp = client.models.generate_content(
    model="gemini-3.8-flash",
    contents="Write 3 selling points for a 500 ml insulated bottle",
    config=types.GenerateContentConfig(
        system_instruction="You are an e-commerce copy assistant. Keep it short.",
        temperature=0.7,
        max_output_tokens=512,
    ),
)
print(resp.text)
print(resp.usage_metadata)

import { GoogleGenAI } from '@google/genai'

const ai = new GoogleGenAI({
  apiKey: process.env.TUTUJIN_API_KEY,
  httpOptions: { baseUrl: 'https://open.tutujin.com' },
})

const resp = await ai.models.generateContent({
  model: 'gemini-3.8-flash',
  contents: 'Write 3 selling points for a 500 ml insulated bottle',
  config: {
    systemInstruction: 'You are an e-commerce copy assistant. Keep it short.',
    temperature: 0.7,
    maxOutputTokens: 512,
  },
})
console.log(resp.text)
console.log(resp.usageMetadata)

{
  "candidates": [
    {
      "content": {
        "role": "model",
        "parts": [{ "text": "1. Double-wall vacuum keeps drinks hot for hours..." }]
      },
      "finishReason": "STOP",
      "index": 0
    }
  ],
  "usageMetadata": {
    "promptTokenCount": 28,
    "candidatesTokenCount": 96,
    "totalTokenCount": 124
  },
  "modelVersion": "gemini-3.8-flash"
}