Last refreshed 2026-06-15. Next refresh: weekly.
Why use DeepSeek R1 0528 on GCP Vertex AI?
GCP Vertex AI offers DeepSeek R1 0528 with pay-as-you-go pricing at $1.35/1M 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.
Compare DeepSeek R1 0528 across 7 providers to find the best fit for your use caseInput / 1M
$1.35
Output / 1M
$5.40
Cache
Not sourced
Batch
Not sourced
Setup recipe
Python + curlInstall
pip install google-cloud-aiplatformAuth
export GOOGLE_CLOUD_PROJECT=...Call
import os
import vertexai
from vertexai.generative_models import GenerativeModel
vertexai.init(project=os.environ["GOOGLE_CLOUD_PROJECT"], location="us-central1")Model ID
deepseek-r1-0528Request 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("deepseek-r1-0528")
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.
Compare DeepSeek R1 0528 Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Together AI | $3.00 | $7.00 |
| Fireworks AI | $0.56 | $1.68 |
| GCP Vertex AI | $1.35 | $5.40 |
| Novita AI | $0.70 | $2.50 |
| OpenRouter | $0.50 | $2.15 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $1.35 |
| Output tokens | $5.40 |
Capabilities
ReasoningStructured OutputsCode Execution
About DeepSeek R1 0528
DeepSeek R1 0528 is DeepSeek's DeepSeek R1 model with an optional reasoning mode. It offers a 130K-token context window with weights openly available for self-hosting and scores 81 on GPQA.
Model Specs
Released2025-05-28
Parameters685B total, 37B active (MoE)
Context130k
ArchitectureDecoder Only