The difference between text-to-video and image-to-video AI is where the picture comes from. Text-to-video builds the whole shot from your words: the people, the place, the light and the movement. Image-to-video starts from a picture you give it and only has to decide what moves. Use text-to-video when you are exploring ideas and the exact look does not matter yet. Use image-to-video when the shot has to match something: a character, a product, a location or the shot before it.
That one difference explains almost everything else, from why image-to-video is the default for films with recurring characters to why text-to-video still has a place at the start of a project. Below is the side-by-side comparison, then when to pick each, and how the current models handle both.
Text-to-video vs image-to-video at a glance
| Text-to-video (T2V) | Image-to-video (I2V) | |
|---|---|---|
| You give it | A written description | A still image, plus a short description of the motion |
| The model decides | Everything: faces, clothes, set, framing, light and motion | Mostly the motion. The look is fixed by your image |
| Consistency between shots | Weak. The same description gives a slightly different person each time | Strong. Every shot can start from a frame of the same character |
| Control over composition | Loose. Framing words are suggestions | Exact. The first frame is your composition |
| What the prompt describes | Who, where, what it looks like, and what happens | Only what happens: the camera move and the action |
| Best for | Ideas, mood tests, b-roll, abstract or impossible scenes | Stories with recurring characters, products, brand work, anything that must match |
| Main risk | Every clip looks like a different film | A weak or busy start frame becomes a weak clip |
There is also a third option most comparisons leave out: reference-to-video. It sits between the two, and it is covered further down.
How text-to-video works
With text-to-video, your prompt is the only thing the model has. It has to invent a face for “a woman in her thirties”, a room for “a small kitchen at night” and a camera position for “close-up”. It usually invents them well. It just invents them differently every time you ask.
That makes text-to-video excellent wherever variety is the point. When we’re looking for the look of a film, ten versions of “a rain-soaked street market at dusk” show us ten directions in a few minutes. It’s good for mood and pitch reels, where nobody needs to recognise the same person twice, and for establishing shots of skylines, weather and crowds. It’s also the only option for things that don’t exist yet, since there is no picture to start from.
A good text-to-video prompt reads like a director talking to a camera operator, not like a list of adjectives:
A low tracking shot of a fox sprinting through wet pine undergrowth at dawn. Mist drifts between the trees as the camera keeps pace beside it. Cool blue morning light, fast but controlled motion.
One shot size, one camera move, one clear action, then the light and the feel. The more a prompt piles on (“epic cinematic 8K masterpiece, dramatic, stunning”), the less control you actually have.
How image-to-video works
Image-to-video turns the job around. You decide what the shot looks like by making or choosing the first frame. The model’s job is to bring it to life: how the people move, what the camera does, how the light shifts.
This is why most narrative AI work runs through image-to-video. If your main character appears in thirty shots, you can make thirty first frames of the same person (from the same character references), and every clip starts from someone who looks right. Text-to-video cannot promise that.
It also changes how you write the prompt. The frame already shows what your character is wearing, so the prompt should not describe the clothes again. Describe the motion:
Slow push in. She sets the brass key on the workbench and lets go of it, hesitating. Dust drifts in the light from the bulb.
Describing what the image already shows is one of the most common image-to-video mistakes. It invites the model to “correct” the image into something else, and the jacket comes out a different colour.
Start frames, end frames and keyframes
Most current models accept more than one image, which gives you more control than a single start frame. With a start frame alone, the clip begins exactly on your image and the prompt drives it forward. That’s the everyday mode.
Add an end frame and the model fills in the motion between the two. We use this for reveals, transformations and any shot where the ending has to land exactly. Keep the two frames related, the same person in the same place with a similar camera, or the in-between gets strange.
A few models go further and take a series of stills as waypoints in one continuous take, which is the tool for choreographed moments that have to hit specific poses.
The third option: reference-to-video
Reference-to-video is the newest of the three modes, and it is often the right answer to the question behind “text-to-video or image-to-video”. You give the model reference images of your characters, props or location, but not a first frame. It composes a new shot while keeping the people and things from your references.
It is text-to-video’s freedom with much of image-to-video’s consistency. You still write the scene, but “the woman” is now the specific woman in your reference images. The trick is to give each reference one job, in plain words: “Image 1 is Mara. Use only her face, hair and clothing. Do not use the image background.” Without that last sentence the model tends to drag the reference photo’s background into your shot.
Which one should you use?
| If your shot needs… | Use | Why |
|---|---|---|
| The same character as the previous shot | Image-to-video | Start every clip from a frame of that character |
| A product or logo that must look exactly right | Image-to-video | The model animates your image instead of reinventing the product |
| A new composition, but with known characters | Reference-to-video | Keeps the faces without locking the framing |
| A precise ending (a hand closing on a key, a door clicking shut) | Image-to-video with a start and end frame | You set where the motion has to arrive |
| Fast exploration of looks and ideas | Text-to-video | Variety is the point, and there is nothing to match yet |
| Establishing shots and b-roll with no recurring people | Text-to-video | Nothing to keep consistent, so skip the extra step |
In practice, most projects use all three. A typical short film starts with text-to-video tests to find the look, moves to still frames once the cast is designed, and makes almost every final shot with image-to-video or reference-to-video.
How the current models handle each mode
Models change every few weeks, so treat this as a snapshot from late 2026 rather than a ranking.
- FLUX 3 covers all of it: text-to-video, image-to-video from a start frame, start and end frames, and up to ten keyframes in one take. It can generate sound and speech with the picture.
- Seedance 2.5 is strong at image-to-video and has a reference-to-video mode that accepts many references at once. In practice quality holds best with a handful of clearly labelled subjects, not dozens. It can also produce long single takes and speak dialogue.
- MiniMax H3 does image-to-video, start and end frames, and reference-to-video with up to nine images, at 2K with sound. It responds well to camera direction written as type, size and speed: “pushes in with small amplitude at slow speed”.
- Gemini Omni takes an end frame natively and always generates audio. It works in 16:9 and 9:16.
- LTX 2.5 is fast, which makes it useful for drafts, and it speaks dialogue. It prefers prompts in the present tense, with emotion shown through the body (“jaw tight, eyes on the floor”) rather than named (“sad”).
Each model reads prompts a little differently, but the 10 best practices for AI video prompts work across all of them.
Common mistakes
The one we see most is using text-to-video for a story with recurring characters, then fighting for consistency in every shot. Close behind is re-describing the image in an image-to-video prompt, which invites the model to change exactly what you wanted to keep.
Asking for two camera moves at once is another. “Orbit and push in while panning” usually gets you neither. A busy start frame causes trouble too, because crowds, text and clutter in the first frame turn into flicker and melting once they move. And long clips drift. Faces and hands go wrong most in the last seconds, so five-second shots with one action fail less often and cost less to redo.
FAQ
Is image-to-video better than text-to-video?
Neither is better in general. Image-to-video gives more control and consistency, so it is usually better for final shots in a story. Text-to-video is faster to start with and better for exploring ideas and for shots with no recurring people.
What does I2V mean?
I2V is short for image-to-video: a model that turns a still image into a video clip. T2V is text-to-video, and V2V is video-to-video, which restyles or edits an existing clip. Our I2V, T2V and V2V explainer covers all three.
Can I use both in the same project?
Yes, and most good projects do. Text-to-video for early tests and b-roll, image-to-video and reference-to-video for the shots that carry the story.
Why do my image-to-video clips change my character’s face?
Usually because the clip is too long, the motion is too big, or the prompt describes the character again in different words. Keep clips short, ask for one move, describe only the motion, and start from a clean, well-lit frame.
Is text-to-video cheaper?
Not by much. Most models charge per second of video whichever mode you use. Image-to-video adds the small cost of making the start frame, but it usually saves money overall because fewer clips end up in the bin.