POST /v1/vinted/search_by_image runs Vinted’s native image search and returns
ordinary active listings with prices, photos and URLs. No Vinted login is needed.
A successful request costs 10 credits; failed requests are free.
Image input
Supply exactly one of:image_url: a public HTTP(S) URL that returns an image.image_base64: the image encoded as base64, either a bare payload or adata:image/jpeg;base64,...URL.
Filters
Send ID filters as comma-separated positive integers, such as
"53,14". Image
search uses Vinted’s visual ranking; it has no order or query parameter.
Resend the same image with the next page and the preceding response’s
pagination.time. Image search does not offer seller-country enrichment.
Clients
Use Python/Node SDK 0.47.0+ or CLI 0.15.0+.Results and limits
The response uses the sameitems, pagination and market envelope as text
search. Prices, photos, listing URLs and available size/condition labels are
included. Structured brand_title may be null; display_title is a display label,
not a guaranteed brand identifier.
Each item carries a similarity_score: Vinted’s own measure of visual similarity
to the query image. Vinted returns a ranking on only some calls; on the rest
similarity_score is null on every item. A null says nothing about the item —
only that Vinted sent no score — so do not treat a missing score as a weak match.
Filter on the score when it is present, and fall back to your own ranking when it
is not.
Whether a given photo gets scored is close to fixed for that photo within a
session — repeated calls on one image tend to be all scored or all unscored — so
retrying to obtain a score is not worth the credits. An unscored response is
billed the same as a scored one.
Searching with a listing’s own photo usually returns that listing at or near the
top, but nothing guarantees it is first. Drop it by id before computing a
median, or it will bias the result toward its own asking price. The result set
is largely but not perfectly stable across repeated calls on the same photo.
These are active asking prices, not completed sale prices. For valuation,
combine the matching listings with sold comparables and price suggestions.
