Hosted predictions
Price by model
review before each request
- •Select an available model suited to the task.
- •Check its inputs, output and current price.
Runware reference names · not endorsements of this guide or Synexa
Runware describes its own inference infrastructure. The link here opens Synexa, where each supported model runs as an individual prediction with model-specific inputs.
Explore the platform
Explore listed text-to-image models and review each model's supported inputs.
Compare available video models by source inputs, duration and output requirements.
Browse supported speech, music and sound workflows in the current catalog.
Check which available models accept text or images and produce 3D results.
Check the displayed price for the specific Synexa model before running it.
Create and retrieve predictions with a key; a listed model's inputs can differ.
This guide covers Runware as a reference. The model link opens Synexa's catalog; confirm live availability and model-specific pricing before integrating.
curl -X POST https://api.synexa.ai/v1/predictions \ -H "x-api-key: YOUR_SYNEXA_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "black-forest-labs/flux-schnell", "input": { "prompt": "machinery, green-to-yellow gradient" } }' # GET https://api.synexa.ai/v1/predictions/<id> # with x-api-key to check status/output
● Hosted models · Synexa destination
Synexa offers hosted predictions for models in its current catalog. Choose a model and its required inputs; this CTA is not a route to Runware container deployment, Sonic Pods or a dedicated-GPU reservation.
Browse Synexa's available models, then compare each model's inputs, outputs and price before creating a prediction.
Synexa checks your balance against the selected model's price before creating a prediction. See current pricing in its catalog; no GPU-hour rate is promised by this link.
Explore hosted modelsChoose the model and output that fit your task. Synexa's linked predictions workflow is distinct from GPU reservation or bare-metal rental.
Hosted predictions
review before each request
Model playground
available model inputs and examples
The cost of generation depends on the model you select. Compare the current model price, inputs and expected volume instead of assuming a GPU-hour quote.
100K assets/month is a hypothetical workload, not a quote. Check current per-model prices and provider terms before estimating total spend.
Monthly estimate
Variesby model and usageExamples of model families to research · confirm current availability
Synexa documents cURL, Python and Node.js examples for listed models. Start with a Synexa API key and use each model's documented input schema.
✓ x-api-key authentication
✓ POST /v1/predictions · GET /v1/predictions/{id}
>
Find available Synexa models and inspect their inputs.
Send authenticated requests to Synexa's predictions API.
Follow the selected model's schema and response flow.
“Check the model, its required inputs and its output format before you integrate. A familiar API path does not mean every model accepts the same controls.”
Browse a listed model, check its price and inputs, and test it in Synexa's playground before integrating.
Questions developers ask before the first call. Explore the platform for details about your own workload.
For an available model, POST one {model, input} object to /v1/predictions with an x-api-key header; use the returned ID to check status and output. Inputs differ by model.
Sonic Pods are a Runware infrastructure concept described for inference. This guide does not offer or link to those facilities; its model link opens Synexa.
Synexa sets pricing per model. Check the current model price and your account balance before submitting a prediction; no site-wide GPU-hour or savings rate is promised here.
This link opens Synexa's model service, not a GPU reservation flow. If you need hardware capacity, check the destination's current offerings and terms directly.
This independent guide cannot verify either service's data practices. Consult the destination's current privacy and model terms before sending sensitive inputs.