TryOn-API.com vs OpenRouter vs direct provider SDKs

Short answer: if you are building virtual try-on, TryOn-API.com gives you a single OpenAI-compatible endpoint in front of both dedicated try-on engines (Kling Kolors) and general image models repurposed for try-on (Google Gemini, OpenAI gpt-image-1, FAL FLUX Kontext), with one key, one unified credit balance, and automatic fallback. OpenRouter is a great general LLM/image router but does not offer dedicated try-on models or a try-on request shape. Calling providers directly gives you the most control but means maintaining several SDKs, billing accounts, and your own fallback logic.

At a glance

When to choose each

Choose TryOn-API.com when…

• You want virtual try-on specifically, not general image generation.
• You want to A/B different try-on models by changing a single model slug.
• You want one key, one bill, and automatic fallback when a provider is slow or down.
• You already speak the OpenAI / OpenRouter chat completions schema and want minimal migration.

Choose OpenRouter when…

• You mostly need general LLMs or general image generation across many providers.
• You do not need dedicated try-on engines or a person/garment request shape.

Choose direct provider SDKs when…

• You are committed to a single provider and want its newest features the day they ship.
• You need provider-specific parameters not exposed by any gateway.
• You are willing to maintain separate billing, auth, and fallback logic yourself.

Migration cost

If you already call an OpenAI- or OpenRouter-style image chat completion, moving to TryOn-API.com is a three-line change — base URL, key, and model slug:

# Any OpenAI-compatible SDK — change three things:
const client = new OpenAI({
  baseURL: "https://tryon-api.com/api/v1",   // 1. base URL
  apiKey:  process.env.TRYON_API_KEY,          // 2. your TryOn-API key
});
const res = await client.chat.completions.create({
  model: "kling/kolors-v1-5",                 // 3. a try-on model
  modalities: ["image"],
  messages: [{ role: "user", content: [
    { type: "text", text: "put the garment on the person" },
    { type: "image_url", image_url: { url: personDataUrl }, tryon_role: "person" },
    { type: "image_url", image_url: { url: garmentDataUrl }, tryon_role: "garment" },
  ]}],
});

Frequently asked questions

Does OpenRouter support virtual try-on?

OpenRouter routes to general LLM and image-generation models, but it does not expose dedicated virtual try-on engines such as Kling Kolors, nor a try-on-specific request shape (person + garment roles). TryOn-API.com uses the same OpenAI-compatible chat completions schema, but adds tryon_role tagging, a native multipart /tryon endpoint, dedicated try-on models, and unified credits across providers.

Why use TryOn-API.com instead of calling Kling, Gemini, OpenAI or FAL directly?

Calling providers directly means several SDKs, several billing relationships, several auth schemes, and several different request/response shapes — plus your own fallback logic when one provider is slow or down. TryOn-API.com gives you one OpenAI-compatible API, one key, one unified credit balance, and automatic fallback chains.

Is TryOn-API.com a drop-in replacement for the OpenAI SDK?

Yes. The /api/v1/chat/completions route follows the OpenAI and OpenRouter schema, including streaming via SSE and modalities:["image"]. Existing OpenAI SDKs work by changing only the base URL, the key, and the model slug.

What happens if a provider is down?

Pass a fallback chain with models[] and routing preferences (sort: price|latency, allow_fallbacks). If the primary model fails, TryOn-API.com automatically routes to the next capable model in the chain.

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See also the machine-readable llms.txt and full API reference.