AI video production trends are moving incredibly fast right now, and Seedance 2.5 is a big part of why. Seedance 2.5 is still very new. At the time of writing this, the model has only been available for a matter of weeks.
So this isn’t intended to be a definitive Seedance 2.5 review. These are early observations about what we’re seeing from the model, what is being demonstrated in the video above, and what I think some of these developments mean from an actual production perspective.
I’ve been producing with Seedance 2.0 for months, with Kling for well over a year, and before and alongside those platforms with tools including Sora, Veo 3 and others as this technology has evolved. At this point, I’ve personally spent well over $10,000 on AI generations across these platforms.
That means a lot of successful generations, but also a lot of failed generations, experimentation, comparisons, and learning what these models actually do when you try to use them as production tools rather than simply generate an impressive clip.
That’s the perspective I’m bringing to the video above.
Some of what follows is based on what is being demonstrated and discussed in the video. Some comes from my own experience working with these tools. And some is simply where I think these developments may be taking us.
Those distinctions are important.
It’s easy to look at a new AI model and immediately focus on technical specifications:
Those things matter. But one of the things I find fascinating about AI video right now is how quickly some of those specifications can change.
There’s actually a great example of that in the video above.
At the time this video was recorded, only a couple of weeks ago, he specifically calls out Seedance 2.5 being limited to 720p through the Higgsfield workflow he was using at the time.
That was a legitimate limitation to point out at the time.
But we’re already seeing the available resolution options and upscaling workflows around Seedance 2.5 evolve across platforms.
That tells you something about trying to write definitively about AI video in 2026: a technical limitation discussed in a video can potentially change before you’ve even finished writing the article about it.
More importantly, though, I think his original comparison raises a much bigger question.
How much does resolution actually matter?
Obviously, resolution matters.
Given the exact same generation at 720p and 4K, I’m taking 4K.
But that’s rarely the choice we’re actually making with AI video.
What caught my attention watching the Seedance 2.5 examples wasn’t simply the number of pixels being generated. I was looking at:
And whether the generation actually accomplished what was being asked of it.
I’d rather start with an incredibly realistic lower-resolution generation with convincing human motion, strong prompt adherence and the camera movement I actually asked for than a technically higher-resolution generation where the person moves unnaturally or the model fundamentally misunderstands the shot.
Resolution is also something that can potentially be addressed elsewhere in a professional post-production workflow through upscaling.
Bad motion is much harder to fix.
A bad performance is harder to fix.
Incorrect physics are harder to fix.
A model completely misunderstanding what you asked it to do is harder to fix.
So when I’m comparing AI video models, I’m not simply asking, “Which one gives me the highest resolution?” I’m asking, “Which one gives me the best material to actually make the film?”
One of the things demonstrated in the video above is essentially the same creative idea being run through Seedance 2.0, Seedance 2.5 and Kling.
I like comparisons like this because seeing different models interpret similar source material makes their differences much easier to recognize.
But I don’t look at something like that simply to determine which model “wins.”
And I don’t want anyone reading this to think this is some unusual workflow that I’ve suddenly discovered from watching this particular video.
Comparing models is something I regularly do myself.
I’ve spent well over $10,000 generating across Seedance, Kling and other platforms partly because I’ve repeatedly found that different models interpret the exact same instructions and source material differently.
One model can fail miserably at something another model handles beautifully.
Then you give those same 2 models a completely different shot and the result can reverse.
There isn’t necessarily one model that’s universally better for every situation.
The question isn’t always “What’s the best AI video model?” Increasingly, I think the better question is: “What’s the best model and workflow for this particular shot?”
There’s still an idea surrounding generative AI that eventually we’ll discover some magical way of writing a prompt that makes everything work perfectly.
I don’t think that’s where professional AI video production is heading.
After spending well over $10,000 generating with these systems, one of the biggest things I’ve learned is that throwing more words at a model isn’t necessarily the answer.
The more I work with these tools, the more I find myself thinking like a traditional filmmaker:
That’s much closer to production design, directing and cinematography than simply “prompt engineering.”
A good prompt matters.
But the prompt is only one part of the process.
One of the developments demonstrated with Seedance 2.5 that immediately interests me is the increasing ability to provide a model with more information about what you actually want.
Instead of asking an AI model to invent everything from scratch, we’re increasingly able to define the world for it. That might include:
Or other pieces of visual information that help establish what should remain consistent.
This is why I’ve become increasingly interested in building what are essentially character sheets for AI video production.
Rather than continually asking the AI to reinvent a character, you create a collection of references that establishes who that person is:
The more useful visual information you can provide, the less you’re relying on the model to guess.
And in professional production, reducing those guesses matters.
AI can generate an incredible image or an incredible few seconds of video.
That’s impressive.
But filmmaking isn’t one shot.
If you’re creating a commercial, narrative piece or branded video, that character may need to exist across multiple shots, environments and camera angles.
That’s where consistency becomes incredibly important:
The more these models improve their ability to understand and maintain references, the more useful they become for actual filmmaking rather than simply creating isolated AI clips.
Another area demonstrated in the video that I think has enormous potential is video-to-video manipulation.
We’ve spent years thinking about generative AI primarily as a way to create something.
But increasingly, we’re going to use AI to change something that already exists.
Maybe you love the performance, camera movement, lighting and environment of a shot, but one element isn’t working.
Traditionally, depending on the change, you might be looking at visual effects, compositing or potentially even a reshoot.
AI increasingly introduces another possibility: keep the shot and selectively change the thing that’s wrong. That could be:
For commercial production, that becomes extremely interesting. We’ve actually put this exact kind of workflow to use ourselves, using one filmed shot to generate multiple camera angles and new elements from a single video shoot rather than reshooting from scratch.
This is also where I think some context gets lost when people watch impressive AI demonstrations online.
You see the result.
You don’t necessarily see everything that happened before the result:
That doesn’t diminish the technology.
It’s simply part of understanding how the technology actually works in a production environment.
I’ve spent well over $10,000 generating through these platforms, and a significant amount of what I’ve learned has come from generations that didn’t work.
You still pay for those.
That’s why part of becoming efficient with these tools is learning how to increase the probability that a generation works before you press the button.
Sometimes that means changing the prompt.
Sometimes it means changing the reference image.
Sometimes it means shooting or creating the source material differently.
Sometimes it means realizing that another AI model is simply better suited for that particular shot.
And sometimes the smartest decision is still to shoot something traditionally.
This may be the biggest point I took away from watching this demonstration.
There isn’t one correct workflow.
There isn’t one correct model.
And I don’t think there’s going to be.
I’ve repeatedly found that something one model struggles with can work beautifully somewhere else.
I’ve also found that the newest model isn’t automatically the correct model simply because the version number went up. The real questions are:
Those questions can lead you toward different tools.
And sometimes they lead you away from AI entirely.
The skill increasingly becomes understanding the strengths and weaknesses of the tools available to you and figuring out how to combine them.
That’s why I think the conversation around AI replacing filmmakers misses something important.
The tools are becoming extraordinarily capable.
But greater capability doesn’t eliminate decision-making.
In many ways, it creates more decisions.
My early impression isn’t simply that Seedance 2.5 can potentially create better-looking AI video.
What interests me is the continuing movement toward control:
Those are the developments that start moving AI video away from novelty and toward an actual production tool.
And I think the resolution discussion from the video is actually a perfect example of why evaluating these models requires more nuance than reading a specification sheet.
When this video was recorded, the creator was working within a 720p Seedance 2.5 workflow on Higgsfield.
Only weeks later, the resolution landscape around Seedance 2.5 is already changing, with different platforms offering different generation and upscaling options.
But even when I was watching his 720p examples, resolution wasn’t necessarily what caught my attention. I was watching the people, the motion, the camera, how closely the model followed the direction, and whether the result felt real.
Because ultimately, when I’m evaluating one of these generations for an actual production, my question isn’t “How impressive is this AI model?” My question is: “Can I use this shot?”
That’s the standard that matters to me.
We’re still extremely early with Seedance 2.5, and I’ll undoubtedly learn considerably more as I spend more time actually working with it. My opinions may change as the technology itself changes.
But one thing I’m increasingly convinced of after producing with these tools for well over a year and spending well over $10,000 generating across them is that professional AI video isn’t going to be about finding the perfect prompt and pressing a button.
It’s going to be about combining filmmaking judgment with an expanding collection of incredibly powerful tools.
And knowing which tool to use, when to use it, what to give it, and when not to use AI at all may ultimately matter just as much as the model itself. That’s the same judgment we bring to every AI video production project at Top Notch Cinema, matching the right tool and workflow to the shot instead of forcing one model to do everything.
