Which AI video model should you use?

"Which AI video model is best" is the wrong question, and the reason is boring: whichever answer is true this month will not be true next month. Every few weeks something ships that reorders the top of the list.

The useful question is narrower. What does this shot need? That has a stable answer, because the structural differences between these models change more slowly than their raw quality does.

The difference that actually changes your workflow

Most model comparisons talk about fidelity. In practice, the property that changes how you work is far more mundane: how much of the shot you are allowed to specify.

Broadly, generators fall into three groups.

Text only. You describe a shot, you get a shot. Fastest to try, least controllable, and the hardest to make consistent across a sequence — every generation reinvents the world.

Start frame. You supply an image, and the model animates from it. This is the workhorse mode, and the single biggest lever on consistency: the same seed image across every shot means the same face, wardrobe and lighting, without fighting the prompt.

Start and end frame. You pin both ends of the movement and the model fills the middle. This is the one people underuse. It turns generation from a slot machine into something closer to animation — you decide where the shot begins and where it lands, and you are only rolling the dice on how it gets there.

That last group is smaller. In the current catalogue, Seedance 2.0 and Seedance 2.0 Fast, Kling v3 Standard, Kling O1 and LTX 2.3 all accept a start and an end frame. Veo 3.1 Fast, Sora 2 Pro and Happy Horse 1.1 take a start frame. If a shot has to arrive somewhere specific, that distinction narrows the choice for you before quality ever enters the conversation.

Text-to-video is for exploring, image-to-video is for building

A workflow that survives contact with a deadline usually looks like this:

1. Generate stills until the look is right. Stills are quicker and cheaper to iterate than video, so this is where exploration belongs.
2. Animate the stills you kept, using them as start frames.
3. Re-roll individual shots, not sequences.

People who find AI video frustrating are usually skipping step one — burning video generations on look development, which is the most expensive possible way to discover you wanted a different colour of jacket.

Speed, cost and the right to be wrong

Generation cost matters less as a per-clip number than as a re-roll budget. A model that is twice as cheap gives you twice as many attempts, and for most shots, attempts beat fidelity. The third roll of a mid-tier model routinely beats the first roll of a flagship.

So the practical trade is not "good model versus cheap model" — it is:

• Exploring a look? Cheaper and faster. You are going to throw most of these away.
• Final hero shot that will be on screen for four seconds? Spend the tokens.
• Background or B-roll under a voiceover? Cheaper. Nobody is studying it.

The tiered variants exist for exactly this. Seedance 2.0 Fast and Veo 3.1 Fast are there so that the exploration pass and the final pass do not have to cost the same.

A shot-by-shot decision list

An establishing shot, no characters. Text-to-video is fine. There is nothing to keep consistent, so the extra control buys you nothing.

A character who appears in more than one shot. Generate the character as a still, then use it as the start frame everywhere. Do not try to hold a character together through prompt wording alone — it does not work reliably, and it wastes generations proving it.

A shot that has to end in a specific pose or framing. Use a model that takes both a start and an end frame, and give it both. This is also the trick for a seamless loop: make the end frame the same image as the start frame.

Motion you already have. If the movement exists in a reference video, motion transfer will apply it to your subject rather than asking a generator to invent it from a description. Describing choreography in prose is a bad use of everyone's time.

A clip that is nearly right but soft or too short. Do not re-roll it. Upscale it, or interpolate frames to smooth and extend the motion. Fixing is almost always cheaper than regenerating, and it preserves the take you already liked.

What model choice cannot fix

Three things, all of which are cutting problems rather than generation problems:

Pacing. No model gives you rhythm. That is trimming, and it is done on a timeline.

Continuity of edit. Two beautiful shots that do not cut together are still two shots that do not cut together.

Audio. Generated video is either silent or comes with sound you did not choose. The mix — voiceover, music underneath, ducking so the words stay legible — is separate work, and it is most of the perceived quality of a finished video.

This is the real argument for generating inside an editor rather than downloading clips from a browser tab. The moment a shot is a clip on a lane, the fix for "this one is wrong" is a re-roll in place, and everything downstream of it stays put.

The short version

• Explore with stills, build with image-to-video.
• Reach for start-and-end frame models when a shot must land somewhere specific.
• Buy attempts, not fidelity, until the shot is final.
• Upscale or interpolate before you re-roll.
• Expect the ranking to change; the workflow above will not.