Using Gemini 1.5 Flash on GCP Vertex AI

Implementation guide · Gemini 1.5 · Google DeepMind

Serverless

GCP Vertex AI exposes Gemini 1.5 Flash through model ID gemini-1.5-flash. Use the setup steps, sourced pricing, capabilities, and official provider links below to validate this route before deployment.

Last refreshed 2026-06-15. Next refresh: weekly.

Quick Start

  1. 1
    Create an account at GCP Vertex AI and generate an API key.
  2. 2
    Use the GCP Vertex AI SDK or REST API to call gemini-1.5-flash — see the documentation for request format.
  3. 3
    You'll be billed $0.07/1M input, $0.30/1M output tokens. See full pricing.

Code Examples

Install
pip install google-cloud-aiplatform
API key
GOOGLE_CLOUD_PROJECT
Model ID
gemini-1.5-flash

For Google-published models use the model name directly, e.g. "gemini-2.0-flash-001". For third-party publishers (Anthropic, Meta, etc.) use the full publisher path, e.g. "publishers/anthropic/models/claude-3-5-sonnet-v2@20241022".

import os
import vertexai
from vertexai.generative_models import GenerativeModel

# Reads GOOGLE_CLOUD_PROJECT from env; authenticates via Application Default Credentials
vertexai.init(project=os.environ["GOOGLE_CLOUD_PROJECT"], location="us-central1")
model = GenerativeModel("gemini-1.5-flash")
response = model.generate_content("Hello")
print(response.text)

Pricing on GCP Vertex AI

TypePrice (per 1M)
Input tokens$0.07
Output tokens$0.30
Image input$0.13
Video input$0.47
Audio input$0.04

Capabilities

Structured Outputs

About Gemini 1.5 Flash

Gemini 1.5 Flash is a large language AI model by Google, crafted for speed and efficiency in high-volume scenarios 145. As a lightweight model, it's optimized for fast processing and cost-effectiveness, making it ideal for real-time applications and high-frequency tasks 567. With its multimodal capabilities, Gemini 1.5 Flash effectively processes and reasons across multiple data types, including text, images, audio, video, and PDFs 145. Despite its smaller size compared to Gemini 1.5 Pro, it excels in tasks like summarization, chat applications, and data extraction from lengthy documents, employing "knowledge distillation" to transfer essential knowledge from larger models 5.

Model Specs

Released2024-05-14
Context1m
ArchitectureDecoder Only
Knowledge cutoff2024-05

Provider

GCP Vertex AI

Google Cloud Platform (GCP)

Mountain View, California, United States