google/gemini-3-flash-preview
API Usage
You can interact with the Gemini 3 Flash Preview model through our OpenAI-compatible APIs and Gemini native APIs. Below are examples showing how to use the model’s API.API Examples
Generate a model response using the chat endpoint of Gemini 3 Flash Preview.Shell
curl --request POST \
--url https://api.gmi-serving.com/v1/chat/completions \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer *************' \
--data '{
"model": "google/gemini-3-flash-preview",
"messages": [
{"role": "system", "content": "You are a helpful AI assistant"},
{"role": "user", "content": "List 3 countries and their capitals."}
],
"temperature": 0,
"max_tokens": 500
}'
example for function call
# example for function call
curl --request POST \
--url https://api.gmi-serving.com/v1/chat/completions \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer *************' \
--data '{
"temperature": 0,
"max_tokens": 200,
"model": "google/gemini-3-flash-preview",
"tools": [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get weather in a location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city, e.g. San Francisco"
}
},
"required": [
"location"
]
}
}
}
],
"messages": [
{
"role": "user",
"content": "What is the weather in San Francisco?"
}
]
}'
example for vision (image input)
### example for vision (image input)
curl --request POST \
--url https://api.gmi-serving.com/v1/chat/completions \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer *************' \
--data '{
"model": "google/gemini-3-flash-preview",
"messages": [{
"role": "user",
"content": [
{"type": "text", "text": "What animal is in this image?"},
{"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/4/4d/Cat_November_2010-1a.jpg/220px-Cat_November_2010-1a.jpg"}}
]
}],
"max_tokens": 100
}'
example for video input
curl --request POST \
--url https://api.gmi-serving.com/v1/chat/completions \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer *************' \
--data '{
"model": "google/gemini-3-flash-preview",
"messages": [{
"role": "user",
"content": [
{"type": "text", "text": "Describe this video."},
{"type": "video_url", "video_url": {"url": "gs://cloud-samples-data/generative-ai/video/pixel8.mp4"}}
]
}],
"max_tokens": 1000
}'
Gemini Native API
basic generateContent
curl --request POST \
--url https://api.gmi-serving.com/v1/models/gemini-3-flash-preview:generateContent \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer *************' \
--data '{
"contents": [
{
"role": "user",
"parts": [{"text": "List 3 countries and their capitals."}]
}
]
}'
example for video input (Gemini Native)
curl --request POST \
--url https://api.gmi-serving.com/v1/models/gemini-3-flash-preview:generateContent \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer *************' \
--data '{
"contents": [{
"role": "user",
"parts": [
{"text": "Describe this video."},
{"fileData": {"mimeType": "video/mp4", "fileUri": "gs://cloud-samples-data/generative-ai/video/pixel8.mp4"}}
]
}]
}'
Python
import requests
import json
url = "https://api.gmi-serving.com/v1/chat/completions"
headers = {
"Content-Type": "application/json",
"Authorization": "Bearer *************"
}
payload = {
"model": "google/gemini-3-flash-preview",
"messages": [
{"role": "system", "content": "You are a helpful AI assistant"},
{"role": "user", "content": "List 3 countries and their capitals."}
],
"temperature": 0,
"max_tokens": 500
}
response = requests.post(url, headers=headers, json=payload)
print(json.dumps(response.json(), indent=2))
Google Vertex SDK
from google import genai
from google.genai import types
client = genai.Client(
vertexai=True,
http_options={
"base_url": "https://api.gmi-serving.com/v1/models/gemini-3-flash-preview:generateContent",
"headers": {
"Authorization": "Bearer <GMI_API_KEY>"
}
}
)
video_config = types.GenerateContentConfig(
system_instruction="You are a professional action analyst. Please answer in Spanish.",
)
video_part = types.Part.from_uri(
file_uri="gs://cloud-samples-data/generative-ai/video/pixel8.mp4",
mime_type="video/mp4"
)
video_metadata_part = types.Part(video_metadata=types.VideoMetadata(fps=10.0))
try:
response = client.models.generate_content(
model="google/gemini-3-flash-preview",
contents=["Describe this video.", video_part, video_metadata_part],
config=video_config
)
print("--- Gemini 3 Flash reply ---")
print(response.text)
except Exception as e:
print(f"Failed: {e}")