Best Unified Virtual Try-On APIs in 2026

TryOn API is the only product here built solely to route virtual try-on and related image models behind one endpoint. Its landing page describes 14 models from 7 providers behind a single OpenAI-compatible base URL (https://tryon-api.com/api/v1), with chat completions, streaming SSE and modalities:["image"] supported, plus a simpler multipart /tryon route for a person image and a garment image. On October 24, 2026 its models page listed eight entries with credits per request: Kling Kolors Virtual Try-On v1.5 (1 credit), Gemini image (1), OpenAI gpt-image-1 (2), FLUX.1 Kontext (1), and, in beta, FASHN v1.5, Leffa, FLUX Pro Virtual Try-On and a WearFits apparel model (1 each). That mix of dedicated try-on and general image models under one balance fits the category: you benchmark Kling against Gemini against FLUX by changing the model string. Billing is in credits topped up via Stripe, with no per-seat plan. The page says a fuller live catalog replaces the static table once it loads. Credit-pack prices were not visible on the public pages I could open, so check the TryOn API docs and model list for current rates.

fal.ai is a general generative-media platform, not a try-on product, but it has the deepest catalog of try-on endpoints behind one SDK. Its own guide, published July 2, 2026, lists 10 try-on models with prices: FASHN v1.6 at $0.075 per generation, Kling Kolors v1.5 at $0.07, image-apps-v2 at $0.04 per image, Leffa at $0.10, FLUX Virtual Try-On Pro at $0.0375 for the first input megapixel plus $0.005 per extra input megapixel and per output megapixel, and a research-only cat-vton at $0.00125 per compute second. The FASHN v1.5 model page confirmed $0.075 per generation on October 24, 2026. You get one account and one integration, with the model chosen by endpoint string. fal beats TryOn API on the number of try-on endpoints and on public prices. It loses on request format: each model has its own input schema, so swapping models means adapting code. The guide is also published by a vendor selling those endpoints. One caution: on October 24 the fal page for the Kling Kolors v1.5 endpoint said it is deprecated, while the July guide still listed it. Check model status before building on a specific endpoint.

Runware is an image and video inference API that hosts Black Forest Labs' FLUX Virtual Try-On (AIR ID bfl:flux@vto, released May 28, 2026) behind one endpoint, https://api.runware.ai/v1. Its schema page prices the model at $0.0375 for the first input megapixel, $0.005 per further input megapixel and $0.005 per output megapixel, with listed examples of $0.0475 to $0.0625 per task and latencies of about 9 to 10.5 seconds. The request takes exactly one person image and one garment image; the schema caps the reference array at two items, so the multi-garment composition mentioned in its description is not available through that field. Runware's general pricing article says natively hosted models are billed on generation time with no fixed per-model list, so treat the schema figures as the most specific number, not a guarantee. It is a strong pick if FLUX VTO is the model you want, but it is a catalog of image models, not a way to compare several try-on providers.

Segmind offers serverless APIs for many image models and its own SegFit try-on line. SegFit v1.2 is listed at $0.0015 per GPU second on serverless, with dedicated cloud at $0.0007 to $0.0031 per GPU second; v1.1 is $0.0072 per second and the original SegFit $0.0108 per second. There is no flat per-call price, so cost per image depends on runtime; the pricing page's estimator shows $0.045 for one request, which corresponds to roughly 30 GPU seconds. Credits work across all Segmind models, so it behaves as a gateway across its own catalog rather than across other vendors' try-on models. The model page states no license or commercial-use terms, so confirm them in the terms before shipping to a storefront.

OpenRouter is the reference gateway for language models, and its documentation now describes an image endpoint (POST /api/v1/images) that accepts reference images through input_references, with the number of references varying by provider. Example image models in its docs include Seedream 4.5, gpt-image-1, gpt-image-2, FLUX.2 Pro and Gemini 2.5 Flash Image. That makes it possible to prompt a general model to dress a person in a garment, but the docs name no dedicated virtual try-on model, so it does not meet the category on its own. It earns a place because teams already using it for text can add image-to-image without a second vendor, and it is the closest analogue to the routing idea. Quality, garment fidelity and content policy depend entirely on the underlying generative model.

Replicate runs community and official models behind a single API. Its pricing page states that most public models are billed by run time at a per-second rate that depends on hardware, while some are billed per output; the page does not mention try-on at all. Community-uploaded try-on models are available, but licence terms are set per model and many research models restrict commercial use, so read each model's licence before relying on it. That makes Replicate a good place to prototype, but check carefully before putting a model in a production storefront.