Using Gemini 1.0 Pro Vision on GCP Vertex AI
Implementation guide · Gemini 1.0 · Google DeepMind
GCP Vertex AI exposes Gemini 1.0 Pro Vision through model ID gemini-1.0-pro-vision. 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
gemini-1.0-pro-vision— see the documentation for request format. - 3
Code Examples
pip install google-cloud-aiplatformGOOGLE_CLOUD_PROJECTgemini-1.0-pro-visionFor 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-1.0-pro-vision")
response = model.generate_content("Hello")
print(response.text)Pricing on GCP Vertex AI
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.50 |
| Output tokens | $1.50 |
| Image input | $2.50 |
| Video input | $7.20 |
Capabilities
About Gemini 1.0 Pro Vision
Gemini 1.0 Pro Vision is a multimodal large language model crafted by Google, excelling in tasks involving both visual and textual data. It boasts advanced capabilities in visual understanding, classification, and summarization, enabling the creation of content from images and videos. The model adeptly processes a range of visual and textual inputs, such as photographs, documents, and infographics, and is capable of generating image descriptions and object identification. Moreover, it supports zero-shot, one-shot, and few-shot learning, enhancing its adaptability to diverse applications.