Build AI features with Google's Gemini 4 Argon for advanced reasoning

Lovable uses Gemini 4 Argon, a frontier model, for complex reasoning with text, images and audio.

When to use it

Building an expense analyzer that extracts details from receipt photos.

Why it is worth it

It handles complex reasoning and multi-modal input better than faster models, solving harder problems.

Paste this into Lovable's chat on your project.

Build an expense analysis page for my app. When someone uploads a receipt photo, the AI should:
1. Extract the merchant name, date, and total amount from the image
2. Categorize the expense as Meals, Travel, Office Supplies, or Entertainment
3. Flag expenses over $250 for manager approval
4. Display a confidence score and explanation of the categorization

Use Gemini 4 Argon since it handles images and complex reasoning. When you're done, show me a form with a file upload, and display the analysis results including a sample result for a Southwest Airlines ticket for $187.50 categorized as Travel with 97% confidence.

Swap the expense categories, approval threshold, and example receipt for your own app's needs.

What you get

You have an expense analyzer that reads receipts with Gemini 4 Argon and flags high amounts for review.