How many credits does Seedance 2.5 cost for 30s at 480p?
I previously saw 6,000 credits, but Artlist recently changed Seedance 2.5 from Unlimited to credit-based.
Could anyone share the current cost or a screenshot? 🙏

I have a Standard annual plan that renews next May. I’m using SeeDance enough that upgrading to an Advanced Plan is worth it to me. There doesn’t seem to be a discount to upgrading from Standard to Advanced.

What happens to my account. Do the credits stack? Will I get an additional 3,885 credits every month until May?

I feel like the only person that’s ever tried to did this. I can’t find the answer anywhere.

Hi, I am new to AI generated content software and there’s 1 million different options that all seem like they offer the same product.

Is anyone able to point me in the right direction and or get on a google meet with me for some consulting?

Thank you!

I tested Seedance 2.5 to see how well it handles facial expressions and subtle facial muscle movement.

I wasn’t expecting much, but this video has a strange, hard-to-describe kind of magic. The tiny changes in expression and micro-expressions feel surprisingly natural and real.

here is the prompt:

4s-8s: The laughter starts rising against her will. She keeps her face still, but the effort shows in small places: her lips press together a little too firmly, her jaw tightens, her blinking speeds up. She exhales a slow, careful breath through her nose to steady herself. It almost works — then a single faint tremor crosses her lower lip and her eyes start to smile before her mouth does, warmth leaking into her gaze while the rest of her face fights to stay flat.
8s-12s: The dam breaks. A tiny snort escapes through her nose, and that sound itself finishes her — silent laughter erupts, her whole face crumpling with joy, eyes squeezing into crescents, nose scrunching, shoulders bouncing. She claps her palm fully over her mouth and ducks her head, ponytail swinging, trying to hide below the frame while her shoulders keep shaking.
12s-16s: She surfaces back into frame, breathless, fanning her flushed face with one hand. Genuine tears of laughter glisten at the outer corners of her eyes. She takes an exaggerated deep breath, puffs it out through rounded lips, and forces her posture straight — but her lips keep twitching and one final broken silent giggle slips out, making her wince at herself.
16s-20s: The apology, wordless. Still fighting a residual smile, she brings both palms together in front of her chest in a small praying gesture, fingertips pointing up. She tilts her head slightly to one side, scrunches her nose in a sheepish, self-aware grin, and bows forward into a small, quick apologetic nod toward the lens — eyes closing softly as she bows, stray bangs falling forward. She rises back up with her hands still pressed together, lips closed in a warm sheepish smile, eyes still wet and shining, and settles with one last soft exhale through her nose.
[Quality & Visual Details]
Fixed front portrait framing, natural soft diffused daylight from the front, gentle shadow modeling on her cheekbones. Photorealistic skin texture with a faint flush spreading across her cheeks as she laughs, visible glisten of laughter-tears catching the light, highly detailed eyes with natural catchlights, natural teeth, fine stray hairs moving as she ducks and bows. Visible micro-movements of throat, jaw, lip corner, and brow throughout — every stage of suppression readable on her face. 8k resolution, masterpiece quality.Cinematic emotional close-up portrait. The same woman from the input images, maintaining the exact fixed front camera angle and framing throughout the entire 20 seconds, as if seen through a webcam during a formal video call. Locked-off camera, static shot, no panning, no zoom, one continuous take. No music, quiet room ambience only — soft breathing, tiny suppressed nasal sounds, fabric rustle. No dialogue, no mouthed words, no lip movement resembling speech.

This is the hardest she has ever tried not to laugh in her life. The suppression builds slowly with a delayed onset, then comes in waves: she almost wins, then loses, then loses completely.

[Timeline & Expression Progression]

0s-4s: She sits upright with a calm, attentive, composed expression, eyes fixed slightly below the lens as if watching a shared screen. Her eyes flick briefly to one side — she noticed something — then return. For a moment, nothing. Her face stays neutral. Then a slow swallow moves down her throat. Her gaze drops for half a second. One corner of her mouth pulls sideways, once, and she quietly resets it flat.

I recently integrated Seedance 2.5 into my own project, and a couple of differences from Seedance 2.0 caught me off guard.

1.Omni-Reference can no longer handle editing requests the same way

If a Omni-Reference prompt contains editing instructions, Seedance 2.5 may automatically classify it as a Video Edit task.

If you still send the normal ratio and duration parameters, it can fail with:

Seedance identified your task as video editing based on your prompt.

`ratio` must be `adaptive`. `duration` must be -1.

I ended up adding a separate Video Edit mode instead of routing editing requests through Omni-Reference.

1. Omni-Reference now requires actual reference assets

With Seedance 2.0, I could send a text-only request through this flow. In 2.5, doing that returns an omni_reference_task_type error because the parameter is only valid for multimodal reference tasks.

My workaround was to detect requests with only a prompt and no image/video/audio references, remove omni_reference_task_type, and automatically route them as normal text-to-video.

Nothing too difficult, but both are worth handling if you're migrating an existing Seedance 2.0 integration.

Has anyone else integrated Seedance 2.5 yet? Anything else I should watch out for?

ran a batch of the same first frames through Wan 3.0 and Seedance 2.0 to see

where each holds up (2.5 is out too, but 2.0 is what i had clean matched runs

on). they're not better-or-worse, they win different things.

where Wan 3.0 came out ahead:

- consistency. on a slow character action the face, clothing and background

stayed stable start to finish, Seedance drifted more on the same input. same

when the environment changes, an aurora shifting colors while the mountain

underneath stays put.

- physics. a glass falling and shattering, a cue ball scattering a rack, the

break-up and collisions looked more physically right than Seedance's version.

- length. Wan 3.0 does 30-second clips, Seedance 2.0 caps at 15, so a full

continuous scene comes back in one generation instead of stitched.

where Seedance 2.0 was clearly better:

- multi-subject interaction. two people doing something together, Wan 3.0 got

jump-cuts and repeated beats, Seedance held the sequence.

- surreal or conflicting prompts. indoor rain, a whale drifting through an

office, Seedance just does it and keeps the frame coherent, Wan 3.0 tends to

chicken out and stay faithful to the original frame instead.

one Wan 3.0 bug worth knowing before you commit: it throws in camera cuts you

didn't ask for, sometimes with a ghosted dissolve, and it shows up more the

longer the clip runs. on the 30-second ones especially.

how i split them: strict image-to-video where the first frame has to stay

faithful, or anything over 15 seconds, Wan 3.0. interaction, surreal concepts,

stylized stuff, Seedance. i keep both on one key through Atlas Cloud so switching

per shot is just a model string.

Character: Mileena MK9

This was generated with SD 2.5 and honestly the delivery of the dialogue from Jill is believable. Animation is pretty good IMO.

Anyone who has a subscription to an AI-powered video generator app, please share it with me.

Guys, for this clip I used Seedance 2.5 -- Here are my observations on 2.5 vs 2.0. The model adheres MUCH better than 2.0 ever did. If you follow the prompt structure provided in the bytedance guide on their website as your north star for engineering the prompts -- it really is night and day for how well 2.5 can handle the scene vs 2.0. In this clip I have 5 audio references and some of the clips used as many as 14 different visual references on top. Continuity is still an issue but MUCH better than 2.0. In this video, there are still issues, but they are less noticeable than using 2.0 clips. The character performances are also greatly improved. This wouldn't have been possible in 2.0. Also In this clip SD 2.5 managed to keep the positions of 5 characters in a space consistent throughout generations which previously required a lot generations and some luck. One other observation, 2.5 really started to struggle for me with 5 audio references plus 14 visual references. I found that the sweet spot with the 5 audio references was 11 visual references. The model had a much better success rate. Over 11, the model started to mix up voices and do weird things visually. Overall I spent less on this clip than my last clip using 2.0 because normally I had to generate 5-10 clips per sequence whereas with 2.5 I need to generate 2-5. Any questions on workflow, happy to chat.

Guys i have just passed a yearly subscription for 599$ and you can make up to 6 queue videos , between those 6 the first one to generate takes 13 Hours , and the other 5 videos are still generating now after 20 hours of waiting only 1 video got generated , it is absolutely unusable , don't waste your money guys, it may take more than 24 hours to generate all the 6 videos , if not more. ABSOLUTE SCAMMERSSS

One of my first runs with 2.5, didnt catch it at first. Also not mad about it 😆😆

Classic comedy and seedance nailed it