minimax-t2v is MiniMax's text-to-video entry point, designed for creative tasks without a first-frame image that aim to turn text concepts directly into dynamic visuals. Enter descriptions of the subject, environment, actions, and visual atmosphere to start video generation and obtain a finished video link. It is suitable for concept previews, creative proposals, and asset exploration; compared with reference-image-driven minimax-i2v, the creative starting point is text rather than existing images.
Clarify capacity, inputs and outputs, and invocation methods before selecting a model.
This lists the actual creation and invocation methods on this platform and does not treat native parameters from other Hailuo versions as specifications for this model.
Learn what minimax-t2v can bring to your work.
minimax-t2v uses text as the starting point for creation, making it suitable for the concept stage when image assets are not yet available. You can structure descriptions around “who is doing what in which environment,” then add lighting, color tones, and visual atmosphere. Turn ideas into watchable dynamic drafts first, then decide whether to create reference images or proceed with further editing.
Creative input is centered on the prompt, making it easy to retain the same theme while progressively adjusting scene or action descriptions. It is recommended to specify one main change each time, such as changing a sunset coast to an overcast coast, and then compare the results. This iterative approach is suited to exploring visual directions, but it is not equivalent to precisely locking camera movement or controlling individual frames.
An asynchronous task workflow is supported: save the task_id after submission, then query the status or receive a completion callback. The video_url in the result is used to obtain the video, while the task identifier corresponds to the original concept. For applications that need to submit multiple pieces of copy continuously, this approach makes it easy to separate generation from preview, review, and asset archiving.
Start with specific tasks to find where the model can be effective.
Rewrite a visual segment in an advertising script as descriptions of the subject, scene, and action—for example, a person walking down a street on a rainy night—then generate a video draft for team discussion. The key deliverable is to present the creative direction and atmosphere, rather than directly replacing a final ad that requires accurate trademarks, packaging text, and character identities.
Enter scene descriptions from a story into the model to explore how environments, actions, and visual atmosphere can be combined. It is suitable for creating dynamic references for individual plot points before formal filming or animation production. If the story requires the same character to appear consistently, it is recommended to first use this model to explore directions, then plan subsequent shots in combination with a reference-image-based creation approach.
Write prompts around ocean waves, city streetscapes, or other themes, generate candidate videos, and preview them through links to select assets suitable for subsequent editing. Different prompts and task results can be archived together for easy comparison and reuse. Subtitles, music, voice-over, and full timeline arrangement should be planned as separate production steps.
Choose based on task complexity, input materials, and expected results.
If you only have a creative idea or a scene description, minimax-t2v better matches the current input conditions, so there is no need to prepare a first frame before submitting the task. If you already have a product image, character image, or a defined composition and want to use it as the starting point for a video, minimax-i2v is more suitable. The main difference between the two is the basis for creation; text-to-video should not be treated as animation processing for a specified image.
minimax-t2v is suitable for text-driven visual exploration and should not be used as Director mode. If the task starts with a reference image and focuses on enhanced creative control, consider minimax-i2v-director. When choosing, first consider whether image constraints are needed, then consider control requirements; do not assume that different models have the same camera capabilities simply because they all belong to the Hailuo series.
From a small-scale task to full integration.
Define the objective, required inputs, and output requirements, using real business examples as a starting point.
Open the trial page, confirm the parameters supported by this entry point, then submit a small-scale task to review the results.
Keep the complete model ID, use the request format specified in the documentation, and confirm billing rules on the Pricing page.
Before formal use, understand the output quality and capability scope.
Answers to common questions about using minimax-t2v.
No, it generates videos from text prompts. It is recommended to clearly describe the main subject, environment, and primary action first, then add atmosphere details. If your task must start from an existing image, choose minimax-i2v instead of treating a first-frame image as a required input for minimax-t2v.
minimax-t2v starts from a text concept; minimax-i2v starts from a reference image and requires an image link. Choose the former when you have no visual assets and want to explore creative ideas; choose the latter when you already have a specific image and want to create dynamic content around it, as it better fits the task requirements.
It should be used through the text-to-video entry point and should not be regarded as a Director model. For reference-image-driven Director creation, choose minimax-i2v-director. Even if you include camera movement requirements in the prompt, this does not mean you gain Director-exclusive preset shots or fine-grained motion control.
It is recommended to structure the prompt around a clear scene, describing the main subject, the action taking place, the environment, and the visual atmosphere. First avoid conflicting action requirements, then compare results by changing one element at a time. This approach makes iteration easier; it does not mean complex instructions can always be executed precisely.
When calling it, you can set async=true, first record the returned task_id, then query the task result; you can also configure callback_url to receive completion notifications. After obtaining the result, use the status information to determine whether the task succeeded, and retrieve the video through video_url in data; do not treat the task ID as the finished video URL.
Model information · Updated: 2026-10-01. For call parameters and billing rules, see the API and pricing sections.
Start with a clear goal and evaluate whether it suits your work based on actual results.
This is only a basic call example. See the full docs for more parameters and advanced usage.
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