Gemini API 404 Model Not Found Error: How to Fix

A 404 Not Found or 404 Requested entity was not found error in the Gemini API indicates that the Google server cannot locate the resource specified in your request. In almost all cases, this occurs because your code targets a deprecated model identifier, uses an outdated SDK, or confuses the endpoint configurations of Google AI Studio and Google Cloud Vertex AI.

Use this step-by-step troubleshooting guide to resolve the Gemini API 404 error and update your integration.

1. Replace Deprecated Model Names Google frequently deprecates older versions of Gemini models. If your codebase still references legacy models like `gemini-1.0-pro` or generic preview strings, the server will return a 404 error.

Verify that you are using current, active model identifiers:

• Recommended Default: Use gemini-1.5-flash for fast, cost-effective multimodal tasks.
• Recommended Complex: Use gemini-1.5-pro for complex reasoning and large context windows.
• Legacy Models to Avoid: Do not use gemini-1.0-pro-vision, gemini-pro, or gemini-pro-vision as they are deprecated or shut down.

Update your initialization code to target the current production models:

`python # WRONG (Will cause a 404 error) model = genai.GenerativeModel('gemini-pro')

CORRECT model = genai.GenerativeModel('gemini-1.5-flash') ```

2. Update Your Google GenAI SDK If you target a modern model but your SDK library is outdated, the SDK helper functions may construct the wrong request URL behind the scenes, triggering a 404. Update your package manager dependencies to the latest release.

For Python installations, run: `bash pip install --upgrade google-generativeai `

For Node.js integrations, run: `bash npm install @google/generative-ai@latest `

After upgrading, restart your local runtime environment or redeploy your cloud container to apply the changes.

3. Verify Your Endpoint Namespace Google offers the Gemini API through two entirely distinct platforms: **Google AI Studio** (for developers) and **Vertex AI** (for enterprise Google Cloud users). Mixing up their syntax and endpoint structures is a common cause of 404 errors.

* Google AI Studio Syntax: If you are using an API key generated from AI Studio, the model string is simple: models/gemini-1.5-flash or simply gemini-1.5-flash passed directly to the SDK helper. * Vertex AI Syntax: If you are using Vertex AI via Google Cloud Platform (GCP) IAM credentials, you must include project, location, and publisher paths in the resource name: projects/{project-id}/locations/{location}/publishers/google/models/gemini-1.5-flash

Double-check that you are using the correct library for your credentials. Do not attempt to pass an AI Studio API key to a Vertex AI SDK client.

4. Check for Regional Model Availability Not all Gemini models are available in every geographic location. If you are hosting your application in a region where a specific model or version has not rolled out, Google's gateway may return a 404 error.

• Confirm your hosting region (e.g., us-central1, europe-west9) supports the specific Gemini version you are calling.
• If using an experimental preview model (e.g., gemini-1.5-pro-exp), check Google's official release notes to ensure the experimental endpoint has not been taken offline.

When to Escalate If you have updated your SDK, corrected the model names to `gemini-1.5-flash`, and still receive a 404 error:

• Check System Status: Visit the Google Cloud Service Health Dashboard to see if there is an active API outage or regional degradation affecting model deployments.
• Verify Billing Status: Go to your Google AI Studio or Google Cloud console to ensure your billing account is active. Accounts with suspended billing or unpaid balances may find their custom model endpoints restricted.
• Consult Community Forums: Search the Google Cloud Community forums or the official GitHub repository for the google-generativeai SDK to check if a new SDK bug is causing malformed endpoint construction.