Using Mistral Large on GCP Vertex AI
Implementation guide · Mistral Large · MistralAI
ServerlessOpen Weights
GCP Vertex AI exposes Mistral Large through model ID mistral-large-1. Use the setup steps, sourced pricing, capabilities, and official provider links below to validate this route before deployment.
Last refreshed 2026-06-16. Next refresh: weekly.
Quick Start
- 1
- 2Use the GCP Vertex AI SDK or REST API to call
mistral-large-1— see the documentation for request format. - 3
Code Examples
Install
pip install google-cloud-aiplatformAPI key
GOOGLE_CLOUD_PROJECTModel ID
mistral-large-1For 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("mistral-large-1")
response = model.generate_content("Hello")
print(response.text)Pricing on GCP Vertex AI
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.32 |
| Output tokens | $0.96 |
Capabilities
VisionJSON / Tool useStructured Outputs
About Mistral Large
Mistral Large is a language model from MistralAI. It is deprecated (originally released 2024-02-08); use it only for reproducing earlier results or evaluating drift over time.
Model Specs
Released2024-02-08
Parameters123B
Context32k
Knowledge cutoff2024-03