MiniMax 01
MiniMax 01 is a compact text to video model for short clips. It turns simple prompts into 720p videos with smooth motion and cinematic framing. It targets fast iteration and stable output so developers can prototype interactive video features and creative tools with low latency.
API Options
Platform-level options for task execution and delivery.
-
taskType
string required value: videoInference -
Identifier for the type of task being performed
-
taskUUID
string required UUID v4 -
UUID v4 identifier for tracking tasks and matching async responses. Must be unique per task.
-
outputType
string default: URL -
Video output type.
Allowed values 1 value
-
outputFormat
string default: MP4 -
Specifies the file format of the generated output. The available values depend on the task type and the specific model's capabilities.
- `MP4`: Widely supported video container (H.264), recommended for general use.
- `WEBM`: Optimized for web delivery.
- `MOV`: QuickTime format, common in professional workflows (Apple ecosystem).
Allowed values 3 values
-
outputQuality
integer min: 20 max: 99 default: 95 -
Compression quality of the output. Higher values preserve quality but increase file size.
-
webhookURL
string URI -
Specifies a webhook URL where JSON responses will be sent via HTTP POST when generation tasks complete. For batch requests with multiple results, each completed item triggers a separate webhook call as it becomes available.
Learn more 1 resource
- Webhooks PLATFORM
- Webhooks
-
deliveryMethod
string default: async -
Determines how the API delivers task results.
Allowed values 1 value
- Returns an immediate acknowledgment with the task UUID. Poll for results using getResponse. Required for long-running tasks like video generation.
Learn more 1 resource
- Task Polling PLATFORM
-
uploadEndpoint
string URI -
Specifies a URL where the generated content will be automatically uploaded using the HTTP PUT method. The raw binary data of the media file is sent directly as the request body. For secure uploads to cloud storage, use presigned URLs that include temporary authentication credentials.
Common use cases:
- Cloud storage: Upload directly to S3 buckets, Google Cloud Storage, or Azure Blob Storage using presigned URLs.
- CDN integration: Upload to content delivery networks for immediate distribution.
// S3 presigned URL for secure upload https://your-bucket.s3.amazonaws.com/generated/content.mp4?X-Amz-Signature=abc123&X-Amz-Expires=3600 // Google Cloud Storage presigned URL https://storage.googleapis.com/your-bucket/content.jpg?X-Goog-Signature=xyz789 // Custom storage endpoint https://storage.example.com/uploads/generated-image.jpgThe content data will be sent as the request body to the specified URL when generation is complete.
-
safety
object -
Content safety checking configuration for video generation.
Properties 2 properties
-
safety»checkContentcheckContent
boolean default: false -
Enable or disable content safety checking. When enabled, defaults to
fastmode.
-
safety»modemode
string default: none -
Safety checking mode for video generation.
Allowed values 3 values
- Disables checking.
- Checks key frames.
- Checks all frames.
-
-
ttl
integer min: 60 -
Time-to-live (TTL) in seconds for generated content. Only applies when
outputTypeisURL.
-
includeCost
boolean default: false -
Include task cost in the response.
-
numberResults
integer min: 1 max: 4 default: 1 -
Number of results to generate. Each result uses a different seed, producing variations of the same parameters.
Inputs
Input resources for the task (images, audio, etc). These must be nested inside the inputs object.
inputs object.-
inputs»referenceImagesreferenceImages
array of strings items: 1 -
List of reference images (UUID, URL, Data URI, or Base64).
-
inputs»frameImagesframeImages
array of strings or objects items: 1 -
An array of frame-specific image inputs to guide video generation. Each item can be either a plain image input (UUID, URL, Data URI, or Base64) or an object that pairs an image with a target frame position.
The
frameImagesparameter allows you to constrain specific frames within the video sequence, ensuring that particular visual content appears at designated points. This is different fromreferenceImages, which provide overall visual guidance without constraining specific timeline positions.When the
frameparameter is omitted, automatic distribution rules apply:- 1 image: Used as the first frame.
Examples 2 examples
Shorthand format: When you don't need to specify a frame position, you can pass a plain image input directly.
"frameImages": [ "aac49721-1964-481a-ae78-8a4e29b91402" ]Object format: When you need to specify a frame position, use an object with
imageandframe."frameImages": [ { "image": "aac49721-1964-481a-ae78-8a4e29b91402", "frame": "first" } ]Format 1: string[]
-
Image input (UUID, URL, Data URI, or Base64).
Format 2: object[] 2 properties
Generation Parameters
Core parameters for controlling the generated content.
-
model
string required value: minimax:1@1 -
Identifier of the model to use for generation.
Learn more 3 resources
-
positivePrompt
string min: 2 max: 2000 -
Text prompt describing elements to include in the generated output.
Learn more 2 resources
-
width
integer paired with height -
Width of the generated media in pixels.
Learn more 2 resources
-
height
integer paired with width -
Height of the generated media in pixels.
Learn more 2 resources
-
duration
float value: 6 -
Length of the generated video in seconds. The total number of frames produced is determined by duration multiplied by the model's frame rate (fps).
-
fps
integer value: 25 -
Frames per second for video generation. Higher values create smoother motion but require more processing time.
Provider Settings
Parameters specific to this model provider. These must be nested inside the providerSettings.minimax object.
providerSettings.minimax object.-
providerSettings»minimax»promptOptimizerpromptOptimizer
boolean default: false -
Enable automatic prompt optimization. Affects reproducibility.
Rain-Slick Alley Noodle Stall
{
"taskType": "videoInference",
"taskUUID": "f4ec6013-7b6d-4541-9a2e-497e9b653d48",
"model": "minimax:1@1",
"positivePrompt": "Using the provided first frame as the opening composition, create a 6-second cinematic shot in a rainy Japanese alley at night. The camera makes a slow gentle push-in toward the noodle stall. Steam rises continuously from pots, rain ripples across puddles, lantern light flickers softly, and neon reflections shimmer on the wet pavement. A scooter passes through the distant background once, slightly out of focus. The seated customer shifts posture and lowers the umbrella a little, while the cook turns to lift noodles from a pot. Keep the scene grounded and realistic, with stable framing, smooth motion, moody urban atmosphere, natural parallax, and high-detail textures.",
"width": 1366,
"height": 768,
"duration": 6,
"fps": 25,
"providerSettings": {
"minimax": {
"promptOptimizer": false
}
},
"inputs": {
"frameImages": [
{
"image": "https://assets.runware.ai/assets/inputs/36981d15-edbc-472c-8a17-ce37dc90ea24.jpg",
"frame": "first"
}
]
}
}{
"taskType": "videoInference",
"taskUUID": "f4ec6013-7b6d-4541-9a2e-497e9b653d48",
"videoUUID": "9ebeb19b-0fb5-4718-ae40-3df2915d32ba",
"videoURL": "https://vm.runware.ai/video/os/a23d05/ws/5/vi/9ebeb19b-0fb5-4718-ae40-3df2915d32ba.mp4",
"seed": 173317559,
"cost": 0.28
}Submerged Metro Terminal Evacuation
{
"taskType": "videoInference",
"taskUUID": "167649e3-92a1-4abc-a11d-128b75a158d6",
"model": "minimax:1@1",
"positivePrompt": "A tense cinematic sequence inside a partially submerged underground transit terminal, broken departure boards flickering above rising water, commuters in emergency ponchos guiding each other across overturned benches, orange hazard strobes reflecting in rippling puddles, a stalled silver train half-open at the platform, loose papers and plastic signage drifting past, camera starts with a wide establishing shot then slowly dollies forward at eye level through shallow floodwater, a child’s toy sailboat glides across the foreground, sudden shower of sparks from a damaged ceiling panel, one transit worker waves a glowing baton to direct people toward an exit stairwell, realistic water physics, suspended dust and mist, layered depth, dramatic contrast, grounded performances, natural motion, high detail, suspenseful disaster-film atmosphere",
"width": 1366,
"height": 768,
"duration": 6,
"fps": 25,
"providerSettings": {
"minimax": {
"promptOptimizer": false
}
}
}{
"taskType": "videoInference",
"taskUUID": "167649e3-92a1-4abc-a11d-128b75a158d6",
"videoUUID": "21ac81c8-b08b-43d9-9ea2-76468a54eac6",
"videoURL": "https://vm.runware.ai/video/os/a19d05/ws/5/vi/21ac81c8-b08b-43d9-9ea2-76468a54eac6.mp4",
"seed": 1211037798,
"cost": 0.28
}Submerged Grand Ballroom Reverie
{
"taskType": "videoInference",
"taskUUID": "2a073cc2-e4d9-4182-ac98-e72dc0ba13aa",
"model": "minimax:1@1",
"positivePrompt": "Animate the provided first frame into a six-second cinematic sequence inside a submerged grand ballroom. The camera makes a slow graceful dolly forward toward the lone dancer at center. Her layered silver costume and ribbons drift softly with the currents while small schools of fish spiral through the room. Chandeliers sway gently overhead, suspended dust and bubbles shimmer in beams of sunlight from the fractured ceiling, and loose papers glide across the marble floor. Preserve the ornate hall layout and subject placement from the first frame, with smooth natural underwater motion, realistic light caustics, elegant pacing, and a dreamlike yet grounded tone.",
"width": 1366,
"height": 768,
"duration": 6,
"fps": 25,
"providerSettings": {
"minimax": {
"promptOptimizer": false
}
},
"inputs": {
"frameImages": [
{
"image": "https://assets.runware.ai/assets/inputs/54e90c1c-2b12-4b57-93e9-f587711c82a2.jpg",
"frame": "first"
}
]
}
}{
"taskType": "videoInference",
"taskUUID": "2a073cc2-e4d9-4182-ac98-e72dc0ba13aa",
"videoUUID": "42d00628-232d-4670-9d32-2a69ec6559ef",
"videoURL": "https://vm.runware.ai/video/os/a14d18/ws/5/vi/42d00628-232d-4670-9d32-2a69ec6559ef.mp4",
"seed": 1148704534,
"cost": 0.28
}Glacier Monastery Signal Ascent
{
"taskType": "videoInference",
"taskUUID": "877999e1-1bf9-471b-8426-31f0ff94f097",
"model": "minimax:1@1",
"positivePrompt": "Using the reference image as the visual foundation, create a cinematic aerial approach toward an ancient monastery carved into a glacier cliff. The shot begins wide and slowly rises forward, passing fluttering banners and swaying rope bridges while fine snow streams through the air. Warm lantern light glows from carved windows against the blue-white ice. Tiny pilgrims climb the stairway, their cloaks shifting in the wind. Snow dust drifts from high ledges, clouds slide across the sky, and the camera finishes near the main gate with grand scale, realistic lighting, smooth motion, and strong depth.",
"width": 1366,
"height": 768,
"duration": 6,
"fps": 25,
"providerSettings": {
"minimax": {
"promptOptimizer": false
}
},
"inputs": {
"referenceImages": [
"https://assets.runware.ai/assets/inputs/25dc6e27-c905-4f29-981f-5c5074a0f514.jpg"
]
}
}{
"taskType": "videoInference",
"taskUUID": "877999e1-1bf9-471b-8426-31f0ff94f097",
"videoUUID": "0dcc4a4e-3870-461f-8d9a-922500ac4c58",
"videoURL": "https://vm.runware.ai/video/os/a02d21/ws/5/vi/0dcc4a4e-3870-461f-8d9a-922500ac4c58.mp4",
"seed": 1595861495,
"cost": 0.42
}Submerged Cathedral Aisle Passage
{
"taskType": "videoInference",
"taskUUID": "1dd507d3-bffc-46b7-a80b-b17a3a1d3da8",
"model": "minimax:1@1",
"positivePrompt": "A slow cinematic glide through a flooded cathedral nave beneath the sea, matching the reference image composition and architecture. The camera moves steadily forward down the central aisle as tiny fish weave between stone columns, suspended particles shimmer in shafts of filtered sunlight, and soft currents stir ribbons of sea grass around broken pews. Coral clings to carved arches, the mosaic floor emerges through clear water, and the mood feels hushed, sacred, and dreamlike. Realistic underwater caustics, stable framing, natural motion, rich environmental detail, atmospheric depth, cinematic realism.",
"width": 1366,
"height": 768,
"duration": 6,
"fps": 25,
"providerSettings": {
"minimax": {
"promptOptimizer": false
}
},
"inputs": {
"referenceImages": [
"https://assets.runware.ai/assets/inputs/9066b32e-5035-4851-aeba-855895ab6de2.jpg"
]
}
}{
"taskType": "videoInference",
"taskUUID": "1dd507d3-bffc-46b7-a80b-b17a3a1d3da8",
"videoUUID": "5eaa7f6c-9b39-4b9c-b0be-b6bd6a597638",
"videoURL": "https://vm.runware.ai/video/os/a13d12/ws/5/vi/5eaa7f6c-9b39-4b9c-b0be-b6bd6a597638.mp4",
"seed": 1619441410,
"cost": 0.42
}