Using Gemini 1.5 Pro on Google Vertex AI (Extended Context) on GCP Vertex AI
Implementation guide · Gemini 1.5 · Google DeepMind
Serverless
GCP Vertex AI exposes Gemini 1.5 Pro on Google Vertex AI (Extended Context) through model ID vertex-gemini-1.5-pro-extended. 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
- 2Use the GCP Vertex AI SDK or REST API to call
vertex-gemini-1.5-pro-extended— see the documentation for request format. - 3
Code Examples
Install
pip install google-cloud-aiplatformAPI key
GOOGLE_CLOUD_PROJECTModel ID
vertex-gemini-1.5-pro-extendedFor 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("vertex-gemini-1.5-pro-extended")
response = model.generate_content("Hello")
print(response.text)Pricing on GCP Vertex AI
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.25 |
| Output tokens | $0.75 |
Capabilities
VisionMultimodalStructured Outputs
About Gemini 1.5 Pro on Google Vertex AI (Extended Context)
Gemini 1.5 Pro on Google Vertex AI (Extended Context) is Google DeepMind's Gemini 1.5 model with multimodal text and image input. It offers a 1M-token context window.
Model Specs
Released2024-02-15
Context1m
ArchitectureDecoder Only
Knowledge cutoff2023-11