ChatGPT Adds Virtual Try-On and Favorites in Global Shopping Update

OpenAI is expanding ChatGPT’s shopping capabilities with two features designed to help users visualize products and save items for later.

Announced Thursday, the global update introduces virtual try-on for clothing and accessories, alongside a Favorites function for keeping track of products discovered through the assistant.

The launch gives ChatGPT a larger role in the browsing and consideration stages of online shopping. It follows an earlier effort to introduce instant checkout, which OpenAI subsequently changed course on after the feature underperformed.

Virtual Try-On Uses Photos to Preview Fashion Items

The new try-on feature lets users upload a selfie or full-body photograph to visualize how clothing or an accessory might look on them.

A Try On button will appear within ChatGPT’s shopping results, providing a way to move from discovering an item to generating a personalized preview.

Users can also upload an image of a product, including a screenshot from a website, and ask ChatGPT to create a try-on image.

That extends the feature beyond products surfaced directly through ChatGPT. Someone browsing elsewhere could bring an item into the conversation and use the assistant to explore its appearance.

The output is a generated visualization. It can help users consider a style, but should not be treated as a measurement of how a garment will physically fit.

Images 2.5 Powers the Shopping Update

OpenAI says the features use its newly introduced ChatGPT Images 2.5 model.

The company claims the model produces more natural lighting and richer textures, follows editing instructions more reliably and generates images with less delay.

Those improvements are relevant to fashion previews, where the appearance of fabrics, lighting and accessories can influence whether an image feels useful.

Reliable editing also matters when users request changes to a look or ask the assistant to apply a particular item to their photograph.

These are OpenAI’s performance claims. The practical quality of a try-on will depend on the uploaded photographs, product images and resulting generation.

Favorites Keeps Products and Previews Together

The second addition, Favorites, allows users to save products they discover through ChatGPT to the app’s Library.

Saved items will appear alongside try-on images, making it easier to return to a product and its visual preview.

The feature supports shopping that unfolds across multiple sessions. Users can collect options, revisit earlier discoveries and continue comparing items without having to reconstruct the original search.

It also gives ChatGPT a persistent role in product consideration, extending its usefulness beyond a single recommendation or conversation.

Style Prompts Expand Fashion Discovery

OpenAI highlighted other ways users can employ ChatGPT while shopping.

Someone could describe a style they want to explore and ask the assistant to find pieces that complete the look. Alternatively, users can upload photographs of celebrity outfits and ask it to identify items available for purchase.

These workflows connect visual inspiration with product discovery, an area where Pinterest and Google already have established services.

Virtual try-on adds another step to that process by allowing users to visualize a discovered item before deciding whether to pursue it.

Shopping Features Face Established Competition

The rollout comes as AI companies test how far assistants should participate in consumer shopping.

OpenAI’s earlier checkout experiment illustrates the difficulty of turning conversational discovery into a purchasing habit. Meanwhile, some users criticized proactive recommendations from AI startup Instinct as feeling closer to advertising than assistance.

ChatGPT’s latest tools focus on actions users initiate: trying an item on and saving a product they like.

Google introduced its own virtual try-on feature last year, giving OpenAI an established competitor. Whether shoppers adopt ChatGPT as a regular fashion destination will depend on the usefulness of its recommendations, previews and saved-product experience.