Using T5Gemma on GCP Vertex AI

Implementation guide · T5Gemma · Google DeepMind

ServerlessOpen Weights

GCP Vertex AI exposes T5Gemma through model ID t5gemma. Use the setup steps, sourced pricing, capabilities, and official provider links below to validate this route before deployment.

Last refreshed 2026-06-30. 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 t5gemma — see the documentation for request format.
  3. 3
    You'll be billed Free/1M input, Free/1M output tokens. See full pricing.
API PortalDocumentationPricing

Code Examples

Install
pip install google-cloud-aiplatform
API key
GOOGLE_CLOUD_PROJECT
Model ID
t5gemma

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("t5gemma")
response = model.generate_content("Hello")
print(response.text)

Pricing on GCP Vertex AI

TypePrice (per 1M)
Input tokensFree
Output tokensFree

Capabilities

JSON / Tool useStructured Outputs

About T5Gemma

T5Gemma is Google's open encoder-decoder model family based on Gemma 2, not a Gemma 3 proprietary decoder-only model.

Full model details →

Model Specs

Released2025-07-09
Parameters2B
ArchitectureEncoder-Decoder

Provider

GCP Vertex AI

Google Cloud Platform (GCP)

Mountain View, California, United States