Using Kimi K2 Thinking on GCP Vertex AI
Implementation guide · Kimi K2 · Moonshot AI
ServerlessOpen Source
GCP Vertex AI exposes Kimi K2 Thinking through model ID kimi-k2-thinking. 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
kimi-k2-thinking— see the documentation for request format. - 3
Code Examples
Install
pip install google-cloud-aiplatformAPI key
GOOGLE_CLOUD_PROJECTModel ID
kimi-k2-thinkingFor 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("kimi-k2-thinking")
response = model.generate_content("Hello")
print(response.text)Pricing on GCP Vertex AI
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.60 |
| Output tokens | $2.50 |
Capabilities
ReasoningStructured Outputs
About Kimi K2 Thinking
Extended thinking variant of Kimi K2 with native reasoning capabilities. 256K context.
Model Specs
Released2025-01-01
Parameters1T (32B active)
Context256k
ArchitectureDecoder Only