Resolving Connection and Behavioral Issues When Migrating to Gemini 3.1 Pro Preview on Vertex AI

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I recently completed a migration from the Gemini 2 series (and Google AI Studio) to the Gemini 3.1 Pro Preview on Vertex AI. During the process, I encountered several connection errors and unexpected model behaviors that seem common in this community.

For those currently facing similar issues, here is the technical approach that resolved them in my environment.

1. Prerequisite: Authentication Differences (AI Studio vs. Vertex AI)

The most common migration error stems from authentication mismatches.

  • Google AI Studio: Uses simple API keys (apiKey: "AIzaSy...").

  • Vertex AI (GCP): Strictly requires IAM via Service Accounts and Application Default Credentials (ADC).

You cannot pass an AI Studio API key to Vertex AI endpoints. You must configure a GCP Service Account with the proper roles (e.g., Vertex AI User) and authenticate your environment accordingly.

2. Resolving Connection Errors: The “Double Passport” Initialization

Even with correct ADC, you may hit 404 Not Found or LOCATION_MISMATCH errors. For the Gemini 3.1 Pro Preview, failing to explicitly specify the global region will cause routing failures.

Using the @google/genai SDK, you must define the location at both the top level and inside the vertexai configuration block:

JavaScript

const { GoogleGenAI } = require("@google/genai");
const project = process.env.PROJECT_ID || "your-project-id";
const location = "global"; // Mandatory for 3.1 Pro Preview

const ai = new GoogleGenAI({
  project: project,
  location: location, 
  vertexai: { project: project, location: location },
});

3. Adapting to Model Behavior (Prompting & Tuning)

Gemini 3.1 Pro requires a different prompting strategy compared to older models.

Avoid Micro-Management Overly rigid, step-by-step constraints often cause the model to truncate outputs or refuse tasks. Instead of defining every step, define a clear Role and strict Boundaries (what not to do), and let the model handle the internal reasoning.

Temperature and System Instructions The default temperature is 1.0. Dropping it to 0.0 (a common habit from older models) can paralyze its contextual understanding. Keep the temperature near default and enforce strictness via System Instructions instead. For example, if you need strict JSON, dictate the exact schema boundary in the system prompt rather than modifying the request body parameters.

I hope these notes provide a useful reference for your own implementations.

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