A text-to-video model turns a description into motion without any supplied material. It has to invent the opening image, keep the subject consistent across the seconds that follow, and decide where the camera sits. The first of those is where the mode earns its cost: an image-to-video model already knows what the scene looks like, while a text-to-video model is choosing the subject, the setting, the light and the lens position from the prompt alone. Vendors describe the split in their own words. Vidu lists text to video, image to video, reference to video and start-end to video as separate functions with separate model lists. Kling publishes a text-to-video endpoint on each of its two 3.0 tiers. MiniMax documents its text-to-video models under their own request schema and states that image to video and reference to video are mutually exclusive.
The prompt is the only input, and every vendor constrains it differently. Kling caps a text prompt at 3,072 characters and recommends staying under 2,500. Vidu allows 5,000. MiniMax allows 2,000. Beyond raw length, two vendors define a notation for describing shots inside a single prompt. Kling accepts multi-shot text written as “shot n, m, words;”, where n is the shot number up to six, m is that shot’s duration with a one-second minimum, and the shot durations must add up to the clip’s total length. MiniMax reserves square brackets for fifteen camera commands such as [Truck left], [Push in] and [Tracking shot], and recommends combining no more than three inside one pair of brackets. Wan takes a third route and lets shots be written as timestamps, as in “Shot 1 [0–3 seconds] wide shot:”, while its own reference notes that the shot_type parameter has no effect because the prompt governs the structure. None of these notations is a field on this site.
Because the prompt carries everything, the rules that matter most are the ones about what a prompt can be trusted to control. Kling and Wan both expose a negative prompt: Wan documents one of up to 500 characters, accepting Chinese and English, and truncates anything longer automatically. MiniMax exposes prompt_optimizer, on by default, which lets a model rewrite a short prompt before generation; turning it off trades convenience for precision. Wan exposes prompt_extend with the same trade written into its documentation, noting that rewriting helps short prompts but adds latency. A prompt written for one of these systems is not automatically a good prompt for another, which is why the rules are listed here by the vendor that wrote each one.