What the model is actually doing
A text to video model reads your prompt and generates a sequence of frames that have to agree with each other: the same subject, the same lighting, the same world, moving coherently through time. It is doing two jobs at once, deciding what things look like and deciding how they move.
That double job is why T2V is the loosest of the video tasks. There is no reference frame anchoring the appearance, so every run is a fresh interpretation. Two generations from an identical prompt can differ completely in subject and framing. That is useful when you are exploring, and a problem when you are not.
T2V vs I2V: pick by how much you already know
| Text to video | Image to video | |
|---|---|---|
| Input | Prompt only | Image + prompt |
| Appearance | Model invents it | Locked by your image |
| Best for | Exploration, mood, ideation | Executing a known look |
| Identity drift | Higher | Much lower |
The practical rule: if you can describe the first frame precisely, you are better off generating that frame as an image and feeding it to an image to video model. You trade one extra generation step for a large gain in control.
How to compare text to video models
Four axes matter, and models rarely lead on all four at once.
- Motion quality. Does movement look physical, or does it slide and warp? This is where models differ most, and it is the hardest thing to fix with prompting.
- Prompt adherence. How many clauses survive before the model starts silently dropping them. Test with a deliberately over-specified prompt and count what made it through.
- Clip length and frame rate. Sets whether you are editing 4-second or 10-second building blocks, which changes how you plan a sequence.
- Temporal consistency. Whether faces, clothing, and backgrounds hold across the clip rather than drifting frame by frame.
Price interacts with all four. A cheaper model you can run six times often beats one expensive run, because T2V output has high variance by design.
Writing a prompt that holds
Keep it to one subject, one camera move, and one look. Order matters: models weight earlier tokens more heavily, so lead with whatever you are least willing to lose.
[camera move] on [subject] in [environment], [lighting], [lens/grade]
Adding a second camera move or a second subject is the fastest way to lose adherence. If you need both, generate them as separate clips and cut between them. Negative prompts help with recurring artifacts, but they cannot add control the model does not have; if you keep fighting the same drift, the fix is a different task, not a longer prompt.