Using DeepSeek R1 on GCP Vertex AI
Implementation guide · DeepSeek R1 · DeepSeek
ServerlessOpen Source
GCP Vertex AI exposes DeepSeek R1 through model ID deepseek-r1. 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
deepseek-r1— see the documentation for request format. - 3
Code Examples
Install
pip install google-cloud-aiplatformAPI key
GOOGLE_CLOUD_PROJECTModel ID
deepseek-r1For 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("deepseek-r1")
response = model.generate_content("Hello")
print(response.text)Pricing on GCP Vertex AI
| Type | Price (per 1M) |
|---|---|
| Input tokens | $1.35 |
| Output tokens | $5.40 |
Capabilities
ReasoningStructured OutputsCode Execution
About DeepSeek R1
DeepSeek R1: Reasoning-optimized model with extended thinking capabilities. 128K context.
Model Specs
Released2025-01-20
Parameters671B, 37B Active
Context128k
ArchitectureDecoder Only
Knowledge cutoff2023-12