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| Rule & Modality | Channel | Credits | Price (USD) | Official / Reference Price | Daily Savings |
|---|---|---|---|---|---|
720p-video videoKlingmode: 720p | Regular | 24per second | $0.109 | Fal$0.126per second | Based on 1,000 10-second videos/dayFal costs $168.950/day moreApiPass is 13.41% lower |
1080p-video videoKlingmode: 1080p | Regular | 32per second | $0.145 | Fal$0.168per second | Based on 1,000 10-second videos/dayFal costs $225.450/day moreApiPass is 13.42% lower |
Competitor pricing costs $168.950/day more
ApiPass is 13.41% lower, estimated at Based on 1,000 10-second videos/day
720p-video
mode: 720p
ApiPass Price
$0.109
24 credits per second
Competitor Prices
Complete guide to using Kling-3-motion-control
Access Kling 3.0 Motion Control API on ApiPass to transfer motion from reference videos to character images while preserving facial identity, expressions, and realistic motion dynamics. Build consistent AI video animations with image and video references, optional prompt guidance, and 720p or 1080p output modes.
Kling Motion Control 3.0 is Kuaishou Kling’s AI video motion model for video-to-video motion transfer. It takes a reference image showing the target character and a reference video defining the motion pattern, then generates a new video where the character follows the motion while maintaining stable facial identity, natural expressions, and realistic movement. Compared with Kling 2.6 Motion Control, Kling VIDEO 3.0 Motion Control improves facial consistency and motion stability across complex scenes, multi-angle shots, dynamic framing, and longer motion sequences.
Provide a character image through input_urls and a motion reference video through video_urls. Kling 3.0 Motion Control transfers the movement, gestures, and performance from the video to the character image while keeping the generated video coherent.
The model is designed to preserve facial identity across turns, camera changes, and dynamic movement. This makes it suitable for animating portraits, illustrated characters, avatars, mascots, and virtual presenters with consistent appearance.
Use the optional prompt field to guide the desired output, animation content, style, or scene behavior. Prompts can be empty or up to 2500 characters, giving developers lightweight creative control over the motion-controlled generation.
Kling 3.0 Motion Control keeps facial identity stable across camera angles and long motion sequences. Characters maintain the same facial structure while turning, moving, or performing complex actions, making the API ideal for consistent AI video generation.
Kling Motion Control 3.0 accurately captures subtle emotional transitions such as smiling, surprise, or sadness. Developers can generate expressive AI videos where emotions remain natural while motion is transferred from the reference video.
Kling VIDEO 3.0 Motion Control preserves facial structure even when part of the face becomes briefly hidden during motion or camera shifts. Generated videos restore facial detail smoothly while maintaining identity consistency.
Kling 3.0 Motion Control maintains facial clarity when camera framing changes or dynamic movement occurs. Faces stay sharp and recognizable during zooms, pans, and cinematic shots created through the motion control workflow.
Animate illustrated characters, avatars, or mascots using real human motion. By combining a reference video with a character image, Kling 3.0 Motion Control transfers natural body movement and gestures to create lifelike animated performances.
Generate short-form videos by applying trending dances, gestures, or expressions to AI characters. Creators, influencers, and brands can quickly produce engaging content for TikTok, YouTube Shorts, and other social platforms.
Turn static visuals, mascots, or virtual characters into dynamic promotional videos. Brands can create presenters, product demonstrations, and branded performance clips using motion from reference footage.
Prototype scenes and character performances before production. Filmmakers and creative teams can test choreography, acting, camera movement, and character consistency using motion-controlled AI video generation.
Use clear, well-prepared image and video references to improve identity consistency, motion accuracy, and emotional quality in generated videos.
Use a sharp, well-lit character image with a clear view of the face. The API relies on facial data to maintain identity consistency, so high-quality facial references improve stability and motion accuracy.
Upload facial references that closely match the final look you want to generate. The motion control workflow focuses strongly on facial structure, so accurate references help produce more reliable identity preservation.
For more realistic head movement, use reference material that covers useful views such as front-facing and side profiles. More facial information can improve angle transitions and head-turn accuracy.
When generating complex performances or emotional transitions, choose a strong motion reference video. Videos provide richer movement and facial information, helping Kling VIDEO 3.0 Motion Control create smoother and more consistent results.
ApiPass provides a straightforward task-based workflow for Kling 3.0 Motion Control. Create a task with POST /api/v1/jobs/createTask, then retrieve the result with GET /api/v1/jobs/recordInfo?taskId=<taskId> or use an optional callBackUrl for completion notifications.
Build stable motion-controlled video features with required image and video inputs, optional prompt guidance, 720p or 1080p output modes, and support for reference orientation settings through character_orientation.
Use JPEG, PNG, or JPG images up to 10MB with a subject shown clearly, and MP4 or QuickTime motion videos between 3 and 30 seconds. The input image should show the subject clearly and meet the supported aspect ratio range of 2:5 to 5:2.
Start in the ApiPass Playground to quickly test Kling 3.0 Motion Control. Upload a reference character image and a motion reference video, add an optional prompt, choose the character orientation and output mode, then preview motion transfer results before integrating the API.
Create your ApiPass API key and prepare publicly accessible file URLs for input_urls and video_urls. The image should be JPEG, PNG, or JPG, and the video should be MP4 or QuickTime. Review request parameters such as prompt, mode, character_orientation, and optional callBackUrl.
Send a POST request to https://api.apipass.dev/api/v1/jobs/createTask using model kling/kling-v3-motion-control. The response returns a taskId, which you can query through https://api.apipass.dev/api/v1/jobs/recordInfo?taskId=YOUR_TASK_ID until the generated video result is available.
All APIs require authentication via Bearer Token.
Authorization: Bearer
Submit a new Nano Banana 2 image generation or editing task
The API accepts a JSON payload with the following structure:
1{
2 "model": "string",
3 "callBackUrl": "string (optional)",
4 "channel": "auto",
5 "input": {
6 // Input parameters
7 }
8}modelRequiredstringThe model name to use for generation
"google/nano-banana-2"
callBackUrlOptionalstringCallback URL for task completion notifications. If omitted, no callback will be sent.
"https://your-domain.com/api/callback"
channelOptionalstringYou may specify the corresponding provider within APIPASS via the channel parameter; these providers handle the actual image and video generation tasks. APIPASS currently offers three provider options:
The default value for the channel parameter is auto. When enabled, APIPASS automatically allocates tasks across available providers based on real-time pricing and stability metrics to balance minimal cost and reliable performance. Retain the default auto value unless you have custom routing requirements.
Available options:
auto
The input object contains the following parameters:
input.promptRequiredstringA text description of the image you want to generate
Describe the subject, style, lighting, and composition for best results
"A serene alpine lake reflecting snow-capped mountains at golden hour, photorealistic"
input.image_inputOptionalarray(URL)Input images to transform or use as reference. Supports up to 14 images.
Accepted types: image/jpeg, image/png; Max size: 30MB per image; Max files: 14
["https://example.com/reference.jpg"]
input.aspect_ratioOptionalstringAspect ratio of the generated image. Defaults to match_input_image when image_input is provided, otherwise 1:1.
Available options:
"16:9"
input.resolutionOptionalstringResolution of the generated image. Higher resolutions produce more detail but take longer to generate. Default: 1K.
Available options:
"1K"
input.output_formatOptionalstringFormat of the output image. Default: jpg.
Available options:
"jpg"
1curl -X POST "https://api.apipass.dev/api/v1/jobs/createTask" \
2 -H "Content-Type: application/json" \
3 -H "Authorization: Bearer YOUR_API_KEY" \
4 -d '{
5 "model": "google/nano-banana-2",
6 "callBackUrl": "https://your-domain.com/api/callback",
7 "input": {
8 "prompt": "A serene alpine lake reflecting snow-capped mountains at golden hour, photorealistic",
9 "aspect_ratio": "16:9",
10 "resolution": "1K",
11 "google_search": false,
12 "image_search": false,
13 "output_format": "jpg"
14 }
15 }'1{
2 "code": 200,
3 "message": "success",
4 "data": {
5 "taskId": "task_12345678"
6 }
7}codeStatus code, 200 for success, others for failure
messageResponse message, error description when failed
data.taskIdTask ID for querying task status and results
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