Using Gemini 1.0 Ultra on GCP Vertex AI
Implementation guide · Gemini 1.0 · Google DeepMind
GCP Vertex AI exposes Gemini 1.0 Ultra through model ID gemini-1.0-ultra. 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-ultra— see the documentation for request format. - 3
Code Examples
pip install google-cloud-aiplatformGOOGLE_CLOUD_PROJECTgemini-1.0-ultraFor 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-ultra")
response = model.generate_content("Hello")
print(response.text)Pricing on GCP Vertex AI
| Type | Price (per 1M) |
|---|---|
| Input tokens | $1.00 |
| Output tokens | $3.00 |
Capabilities
No model capability flags are currently sourced.
About Gemini 1.0 Ultra
Google's Gemini 1.0 Ultra is a leading large language model designed for tackling highly complex tasks with advanced analytical capabilities. As the largest model in the Gemini 1.0 family, it excels in coding, mathematical reasoning, and multimodal reasoning. Its strength lies in its ability to seamlessly understand and process diverse data types, including text, code, audio, images, and video. Gemini Ultra surpasses human experts on the MMLU benchmark with a 90% score, although it has limitations in image generation and some multimodal tasks.