how much does H3 Max Styles cost?

$0.08/sec (768P). you pay per run from your inference shell credits. no subscription is required.

how do I run H3 Max Styles via api?

call falai/minimax-h3-max-styles with the javascript or python sdk, the rest api, or `belt app run falai/minimax-h3-max-styles` from a terminal. the api reference on this page lists every input.

who makes H3 Max Styles?

H3 Max Styles is made by falai and runs on inference shell. every falai app is listed at https://inference.sh/apps/falai.

can my agent use H3 Max Styles?

yes. agents call it as a tool through the inference shell mcp server (https://api.inference.sh/mcp), the belt cli or the sdk, and pay per run like you do.

what are the alternatives to H3 Max Styles?

other video apps on inference shell include H3 Max Insert Video, H3 Max 3D to Video, H3 Max Recast, H3 Max Relight. they run through the same api, so switching is a one-line change.

H3 Max Styles

Stylized 768P video with audio from a prompt or first-frame image using fal's MiniMax H3 Max style pipelines. VHS, hand-drawn, 16-bit pixel, retro 70s toon, low-poly.

text to video image to video stylized

run in browser →run via API

run with your agent

# install belt

$curl -fsSL https://cli.inference.sh | sh

# sign in (or set INFERENCE_API_KEY on a headless machine)

$belt login

# view schema & details

$belt app get falai/minimax-h3-max-styles

# run

$belt app run falai/minimax-h3-max-styles --input '{"prompt":"A skateboarder cruises down a palm-lined boulevard at sunset, upbeat synth music."}'

maker: falai
category: video
input → output: text → video, image → video
price: $0.08/sec (768P)
runs via: api, javascript and python sdk, belt cli, mcp
updated: 2026-10-20

api reference

about

stylized 768p video with audio from a prompt or first-frame image using fal's minimax h3 max style pipelines. vhs, hand-drawn, 16-bit pixel, retro 70s toon, low-poly.

1. calling the api

install the client

the client provides a convenient way to interact with the api.

bash

1pip install inferencesh

setup your api key

set INFERENCE_API_KEY as an environment variable. get your key from settings → api keys.

bash

1export INFERENCE_API_KEY="inf_your_key"

run and get result

submit a request and wait for the final result. best for batch processing or when you don't need progress updates.

python

1import os2from inferencesh import inference34client = inference(api_key=os.environ["INFERENCE_API_KEY"])567result = client.run({8        "app": "falai/minimax-h3-max-styles",9        "input": {10            "prompt": "A skateboarder cruises down a palm-lined boulevard at sunset, upbeat synth music."11        }12    })1314print(result["output"])

stream live updates

get real-time progress updates as the task runs. ideal for showing progress bars, partial results, or long-running tasks.

python

1import os2from inferencesh import inference34client = inference(api_key=os.environ["INFERENCE_API_KEY"])567# stream=True yields updates as they arrive8for update in client.run({9        "app": "falai/minimax-h3-max-styles",10        "input": {11            "prompt": "A skateboarder cruises down a palm-lined boulevard at sunset, upbeat synth music."12        }13    }, stream=True):14    if update.get("progress"):15        print(f"progress: {update['progress']}%")16    if update.get("output"):17        print(f"output: {update['output']}")

2. authentication

the api uses api keys for authentication. see the authentication docs for detailed setup instructions.

3. files

file inputs are automatically handled by the sdk. you can pass local paths, urls, or base64 data.

automatic upload

the python sdk automatically detects local file paths and uploads them. urls are passed through as-is.

python

1# local file paths are automatically uploaded2result = client.run({3    "app": "falai/minimax-h3-max-styles",4    "input": {5        "image": "/path/to/local/image.png",  # detected & uploaded6        "audio": "https://example.com/audio.mp3",  # url passed through7    }8})

manual upload

you can also upload files manually and use the returned url.

python

1# upload and get a hosted URL2file = client.files.upload("/path/to/file.png")3print(file.uri)  # https://cloud.inference.sh/...

4. webhooks

get notified when a task completes by providing a webhook url. when the task reaches a terminal state (completed, failed, or cancelled), a POST request is sent to your url with the task result.

python

1result = client.run({2    "app": "falai/minimax-h3-max-styles",3    "input": {4        "prompt": "A skateboarder cruises down a palm-lined boulevard at sunset, upbeat synth music."5    },6    "webhook": "https://your-server.com/webhook"7}, wait=False)

webhook payload

your endpoint receives a JSON POST with the task result:

json

1{2  "event": "task.completed",3  "timestamp": "2024-01-15T10:30:05Z",4  "data": {5    "id": "task_abc123",6    "short_id": "abc123",7    "status": 10,8    "status_text": "completed",9    "output": { ... },10    "created_at": "2024-01-15T10:30:00Z",11    "updated_at": "2024-01-15T10:30:05Z"12  }13}

eventstringtask.completed, task.failed or task.cancelled

timestampstringiso timestamp of the delivery

data.idstringtask id

data.short_idstringshort task id

data.statusnumberterminal status (10=completed, 11=failed, 12=cancelled, 13=rejected)

data.status_textstringthe status as a word: completed, failed or cancelled

data.outputobjecttask output (when completed)

data.errorstringerror message (when failed, omitted otherwise)

data.session_idstringsession id (when the task ran in a session, omitted otherwise)

data.created_atstringiso timestamp

data.updated_atstringiso timestamp

5. schema

input

promptstring*

describe the scene, action, camera and sound. the chosen style is applied automatically.

example: "A skateboarder cruises down a palm-lined boulevard at sunset, upbeat synth music."

stylestring

visual style pipeline.

default: "vhs"

options:"vhs""hand-drawn""16bit-pixel""retro-toon-70s""low-poly"

imagestring(file)

optional first-frame image. artwork in the chosen style gives the most consistent look; its aspect ratio sets the canvas.

durationinteger

video duration in seconds (5-15).

default: 5min:5max:15

aspect_ratiostring

aspect ratio for text-to-video. ignored when an image is given.

default: "16:9"

options:"21:9""16:9""4:3""1:1""3:4""9:16"

damage_levelstring

vhs style only: tape wear from subtle analog noise (light) to strong tracking errors (heavy).

default: "medium"

options:"light""medium""heavy"

seedinteger

random seed for reproducible generation.

output

seedinteger

seed used for this generation.

videostring(file)*

the generated 768p stylized video with audio.

more from falai

H3 Max Insert Video

falai/minimax-h3-max-insert-video

$0.05/sec 480p, $0.06/sec 768p

H3 Max 3D to Video

falai/minimax-h3-max-3d-to-video

$0.50/request + $0.05/sec (480P), $0.08/sec (768P), $0.16/sec (1080P) + $0.10/generated ref; 5s min

H3 Max Recast

falai/minimax-h3-max-recast

$0.30/sec at 768P, $0.45/sec at 1080P

H3 Max Relight

falai/minimax-h3-max-relight

$0.05/s 480P, $0.08/s 768P, $0.16/s 1080P, $0.32/s 2K; up to 15s/video

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H3 Max Styles

api reference

about

1. calling the api

install the client

setup your api key

run and get result

stream live updates

2. authentication

3. files

automatic upload

manual upload

4. webhooks

5. schema

input

output

other video apps

more from falai

new models

ready to run H3 Max Styles?