MiniMax H3 vs Seedance 2.0: Specs, Cost, 4 Tests

Seedance 2.0 has been the model to beat for most of a year. Since ByteDance shipped it, serious video generation work either used Seedance or benchmarked against it. MiniMax H3 arrived on 31 July and made that a two-horse race again.

This piece has two halves. First, what the published specs and prices actually say — which turned out to be a lot closer than the pre-launch talk suggested. Then four hands-on tests stressing multi-image composition, style mixing, reference-video replication, and long-prompt continuity.

One thing to be upfront about: only the first test was run on both models side by side. The other three are H3 runs, and I have labelled them as such rather than inventing an opponent's score.

Specs at a glance

Before the tests, a quick comparison of what each model accepts and produces.

Seedance 2.0 specs are from Replicate's model page and fal.ai's API reference, checked 6 August 2026. Third-party hosts price the same model differently, so treat the cost row as one provider's rate rather than a list price.

The headline surprise is how little separates them on input. Both take 12 reference files in the same 9 images / 3 videos / 3 audio split, both cover the same six aspect ratios, both run 4–15 seconds, and both generate audio together with the picture rather than requiring a separate sound pass. Anyone expecting H3 to unlock inputs Seedance cannot accept will not find that here.

The two rows that do separate them are resolution and cost. H3 outputs 2K where Seedance tops out at 720p, and H3's per-second rate is roughly a third of what fal charges for Seedance. Those two differences drive most of what follows.

Test 1: Game UI animation

This test checks whether a model can handle structured graphic elements — menus, panels, HUD overlays, equipment slots — without breaking their spatial relationships or corrupting text.

Input: 3 images — a character portrait, a UI panel layout, and a full game interface screenshot. The prompt described a sequence: menu panel slides in from the right, user scrolls through equipment options, selects one, panel slides out, and the 3D environment loads behind it.

MiniMax H3 result: The UI panel animated cleanly. It slid in and out with correct edge alignment, the menu text stayed readable throughout, and equipment switching preserved the slot grid without distortion. When the environment loaded, objects appeared at correct depth layers — foreground UI remained sharp against the blurred background.

Seedance 2.0 result: The initial animation was comparable. Panel movement and text rendering were acceptable. But during the equipment switching sequence, the character model grew an extra arm. It is the kind of artifact that makes a clip undeliverable no matter how good the rest of it looks.

Scoring:

Winner: H3. The extra-arm bug is disqualifying for any production use. Even without that artifact, H3's spatial handling of layered UI elements was tighter. This run also came in at about half the cost — a platform-credit ratio on the tool I was using, not a list-price comparison; see the cost section below for published rates.

Test 2: 2D cartoon + 3D scene fusion

This tests whether a model can maintain a flat illustration style for one element while placing it inside a photorealistic 3D environment — two rendering paradigms in one frame.

Input: A flat 2D cat meme sticker and a desktop wallpaper-style 3D landscape. The prompt asked the cat to interact with the environment while keeping its original flat art style — no 3D shading on the cat, no 2D flattening of the background.

I ran the same prompt four times and kept the strongest result, which is how this kind of shot usually gets made.

MiniMax H3 result: The style boundary held. The cat stayed flat-shaded with clean outlines while the 3D environment kept its depth, lighting, and texture. The cat's movement respected the environment's perspective — it walked along surfaces at the correct angle rather than floating.

The native audio came out with the picture. Footstep-like sounds aligned with the cat's movement, and ambient background audio fit the landscape. One pass, no separate sound step.

Seedance 2.0: Not run head-to-head on this prompt. Seedance 2.0 also generates audio with the video, so the workflow saving here is not an H3 exclusive — it is a property both models share and older video models do not.

Scoring:

Result: H3 handled it. Holding a flat 2D element against a rendered 3D background is the kind of instruction that usually needs a retry or two, and it came through. No competitive claim here — this test says what H3 can do, not what Seedance cannot.

Test 3: Viral video replication (multi-person real scene)

This is the hardest test. It checks whether a model can reproduce the performance, timing, and visual feel of an existing video using only still images and a reference clip.

Input: 2 character photos (full body, clear faces), 1 background image, and 1 reference video showing two people in a choreographed interaction. The prompt instructed the model to match the reference video's action sequence, expression timing, and performance rhythm while using the provided character photos for identity and the background image for the environment.

MiniMax H3 result: The output matched the reference video's rhythm surprisingly well. Character actions followed the choreography. Facial expressions hit the right beats. Lighting matched the reference. Even subtle video-style effects from the reference (a slight vignette, color grading) transferred to the output.

Character consistency was high — both faces stayed recognizable throughout. The environment matched the background image without obvious compositing artifacts. This was a first-attempt success; no re-rolling was needed.

Seedance 2.0: Not run head-to-head on this prompt. Worth stating plainly, since this is the test where you would most expect an input-limit story: Seedance 2.0 accepts the same 12 reference files in the same 9 / 3 / 3 split, so four inputs is well within its limits. Nothing here was structurally out of reach for it.

Scoring:

Result: H3 handled it on the first try. Four inputs, two faces to keep straight, and a performance to match — landing that without re-rolling is the part worth noting. Whether Seedance clears the same bar is an open question, not one this test answers.

Test 4: One-take with frequent style transitions

This tests long-prompt comprehension and the model's ability to hold continuity across dramatic style changes within a single generation.

Input: 1 character photo and a prompt of just over 800 Chinese characters. The prompt described a continuous shot that moved through repeated, abrupt style changes, with a different camera movement and lighting scheme called for at each turn.

The question was whether the model could hold on to instructions buried in the middle of a long prompt, and whether visual continuity would survive the style switches.

MiniMax H3 result: The transitions were smooth. Scene changes happened at the points described in the prompt, with correct camera movements for each segment. The character's face and clothing stayed consistent throughout. Style shifts were handled as gradual transitions rather than hard cuts, which matched the "continuous shot" instruction.

What this actually tests is mid-prompt instruction following. At 800 characters the prompt is nowhere near H3's ~7,000-character ceiling, so nothing was competing for room — the question was whether directions given halfway down still landed. They did.

Seedance 2.0: Not run head-to-head on this prompt. Seedance does not publish a prompt-length limit, so there is no basis for claiming this brief would have been too long for it.

Scoring:

Result: H3 followed a long prompt end to end. Instructions from the middle of the brief still showed up in the output, which is where long prompts usually fall apart.

Cost comparison

Cost is where the gap is widest, and it is the one competitive claim here that rests on published numbers rather than on my own runs.

H3's official rate comes from MiniMax; the Seedance figures are fal.ai's published rates for the same model. That comparison is not perfectly clean — fal is one host among several, and ByteDance's own platform prices differently — so read it as "what one major provider charges" rather than a list price. Even allowing for that, H3 at 2K lands under half of fal's fast tier.

The gap compounds when you iterate. Twenty attempts at 768P run about $16 on H3. The same twenty on fal's Seedance fast tier run about $48.

The official H3 API pricing is documented in our MiniMax H3 API guide. Free credits on third-party tools like this site run on their own ledger.

Verdict

One head-to-head, three solo runs, and a spec sheet that turned out to be much closer than expected.

Test 1 is the only round where both models ran the same brief, and H3 took it — Seedance grew an extra arm mid-transition, which is disqualifying for delivery regardless of how the rest of the clip looked. Tests 2 through 4 show H3 handling hard prompts. They do not show Seedance failing them, and it would be dishonest to present them that way.

On input capability there is very little between these two. Same 12-file ceiling, same 9 / 3 / 3 split, same six aspect ratios, same 4–15 second range, and both generate audio with the picture. If you came here expecting H3 to accept material Seedance chokes on, that story does not hold up.

Where H3 separates is resolution and price. It outputs 2K against Seedance's 720p, and it costs roughly a third of what fal charges per second. For work that gets iterated — twenty attempts to find the one — that gap is the practical difference.

MiniMax has also announced that H3's open weights are available — 33 billion parameters on Hugging Face with ComfyUI support. That changes the math again for teams that can run inference locally, though territory restrictions in the license apply. Details are in the open weights guide.

Seedance 2.0 set the bar and still holds it on capability. H3's case is that it matches that capability, outputs at a higher resolution, and costs less per second. Whether that is worth switching for depends on whether you need 2K and how many attempts your workflow burns — so benchmark both on your own material rather than taking four tests, or any spec table, as the answer.

More on MiniMax H3

• MiniMax H3 API guide — endpoints, input limits, and async workflow
• What is MiniMax H3 — capabilities, pricing, and honest limitations
• MiniMax H3 open weights — download, ComfyUI setup, and license territory restrictions
• MiniMax H3 prompt guide — how to structure prompts that actually control output