Last refreshed 2026-06-15. Next refresh: weekly.
Why use Chat Bison on GCP Vertex AI?
GCP Vertex AI offers Chat Bison 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")chat-bison-001Request 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("chat-bison-001")
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 | $0.50 |
Capabilities
About Chat Bison
Chat Bison, a large language model from Google, is adept at multi-turn conversations, excelling in language understanding and generation for interactive applications. Although details about its architecture are limited, it is based on Google's PaLM 2 framework and trained on extensive text and code datasets. This model is proficient in various language tasks, including code and text generation, editing, and problem-solving, and is particularly well-suited for multilingual content and handling synonymous inquiries in FAQ systems.