Now that Seedance 2.5 is out, choosing the right model for the job will increasingly become an important part of your workflow.

More of a food for thought post - but something that I've been thinking about as I've seen many of the Seedance 2.5 generations coming across my timeline.

For quite some time - and I've been guilty of this, too - I would just default to using the "best" model available. And as a new model came out, I'd quickly adapt to using it.

For example, I was using Veo for quite some time when they were clearly the category leader. But things have changed over the last 6-8 months. Kling quickly became, in my opinion, the best model as of the start of 2026.

Then Seedance 2.0 was released, dethroning Kling - though at a price that was 4-5x the per-second Kling price.

And now that Seedance 2.5 is released (which, although I can't confirm firsthand, I believe is close to ~2x the price of Seedance 2.0 at a per-second basis (someone fact check me if you have firsthand knowledge)), there is potentially a ~10x cost gap between what you might be able to generate from Kling's top-tier model and what Seedance 2.5 can deliver.

Once it's released in the US, I'll be doing some stress-testing to see how it holds up vs. some of the Seedance 2.0 generations I've done, but more just a food for thought post.

This isn't the last of the "next gen" models that we'll see - so in the same way that you might use a frontier LLM (i.e. Fable) for certain high-leverage tasks, while downgrading execution to Opus, Sonnet or even Haiku, I'd recommend finding an equivalent for your generative content workflow.

If money is no object, then this probably isn't relevant to you - but if you're trying to generate AI Films while being conscious of budget, it's probably worth spending some time deciding:

1. What is your "frontier" model stack?
2. What is your "day to day" model?
3. When is the quality gap big enough to justify frontier usage vs. day to day usage?

I'm continuing to ask myself these questions whenever a new model comes out. May be worth the same for you.

Curious how you're all approaching this in your workflows.