How to Get Started with the Kinovi API

Kinovi gives you one API for video, image, and audio models, with pay-as-you-go credits, published model IDs and rates, and docs that include examples. You can start by picking a model from the catalog, reading its rate and input format, then sending a task through the API. The same balance also works in Kinovi Studio if you prefer to create interactively before wiring anything up.

What you need before you start

• A Kinovi account with credits loaded. Kinovi uses pay-as-you-go credits, so you top up a balance rather than committing to a plan.
• The model ID for the model you want to run. Kinovi publishes current model IDs and rates in its catalog, so you don't have to guess or reverse-engineer endpoints.
• Your input assets, if the model needs them. Video models can take a reference video or image depending on the route; image and audio models take their own model-specific inputs.
• A way to make HTTP requests, or the Kinovi Studio UI if you'd rather test a model visually first.

Step 1: Choose a model and check its rate

Kinovi's catalog lists models by provider and task. The featured models include:

Rates are published per model and per resolution. For example, Seedance 2.5 is listed at $0.2205/s on Kinovi versus $0.1197/s + 5 video on Fal.ai for the base route, with Kinovi showing savings of roughly 43–46% across 480p, 720p, and 1080p tiers. Higher resolutions cost more per second, and adding a reference video changes the rate again — so check the exact row that matches your output resolution and input type before you commit to a long render.

Step 2: Read the docs for that model's input format

Kinovi's docs include examples, and the key idea is that each task flow uses model-specific inputs. That means you don't send one universal payload to every model — the fields differ depending on whether you're doing text-to-video, image-to-video, reference-to-video, video edit, or video extend.

For a text-to-video model like Wan 3.0 Text-to-Video, expect a prompt and output settings. For an image-to-video or reference-to-video route, expect an image or reference video field in addition to the prompt. For Seedance 2.5 Video Edit or Video Extend, the input is built around an existing video plus a reference. Match the example in the docs for your specific model ID rather than adapting a payload from a different model.

Step 3: Run the task and verify the result

Send your request with the model ID and the inputs from the docs. The expected result is a completed generation task that returns your output asset — a video, image, or audio file depending on the model.

To verify things are working before you scale up:

1. Start with the lowest resolution tier available for your model. It's the cheapest way to confirm your payload is correct.
2. Check that the output matches the task type you selected. A video edit should preserve the source video's structure; a text-to-video should follow your prompt.
3. Compare the credits consumed against the published per-second or per-task rate to confirm you're being billed as expected.

Step 4: Use the same balance in Studio

If you'd rather not write code first, Kinovi Studio uses the same balance as the API. That means you can test a model interactively, confirm the output quality and prompt behavior, then move the same model ID and inputs into your API integration. This is useful for video models where prompt understanding and motion consistency are hard to judge from documentation alone.

Common sticking points

• Using the wrong input shape. Because inputs are model-specific, a payload that works for Wan 3.0 Text-to-Video won't work for Seedance 2.5 Video Edit. Always start from the docs example for that exact model.
• Ignoring resolution in cost estimates. The same model can differ by more than 2x per second between 480p and 1080p. Estimate cost at your target resolution, not the cheapest listed tier.
• Forgetting reference-video pricing. Routes that accept a reference video have their own rate rows, and in some cases a different rate than the base route.
• Assuming a single provider. Kinovi routes span multiple providers, including ByteDance, Alibaba, OpenAI, Google Gemini, Midjourney, MiniMax, Suno, and NanoBanana. The model ID tells you which one you're calling.

When this approach fits

Kinovi makes sense if you want one API and one balance across video, image, and audio models without building separate integrations for each provider, and if pay-as-you-go credits match how you actually use the models. If you only ever need one model from one provider, a direct integration may be simpler — but you'd lose the single-balance and single-catalog convenience that Kinovi is built around.