Last refreshed 2026-06-15. Next refresh: weekly.
Why use Gemini 1.0 Pro Vision on GCP Vertex AI?
GCP Vertex AI offers Gemini 1.0 Pro Vision with pay-as-you-go pricing at $0.50/1M input tokens. Vertex AI is Google Cloud's managed AI platform, offering access to Gemini models and hundreds of partner models alongside tools for fine-tuning, grounding, vector search, and end-to-end MLOps pipelines.
Setup recipe
Python + curlpip install google-cloud-aiplatformexport GOOGLE_CLOUD_PROJECT=...import os
import vertexai
from vertexai.generative_models import GenerativeModel
vertexai.init(project=os.environ["GOOGLE_CLOUD_PROJECT"], location="us-central1")gemini-1.0-pro-visionRequest example
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)Gotchas
- 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".
- The examples expect GOOGLE_CLOUD_PROJECT; rename it only if your application config maps the new variable.
Pricing
| 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.