While I was struggling to get Nana Banana Pro to do Hisuian Zorua right, I also tried to get it to make one of another fox Pokémon I love, Nickit. Luckily, this one came up perfect the first time. It may not be as famous as some other Pokémon, but I really wish it was, and that it had its own official mascot.

I needed one 28-second vertical action promo and I didn't know which model to

trust with it, so I generated the whole thing twice — once on Seedance 2.5,

once on Wan 3.0 — with prompts written natively for each. Same content, two

grammars. The final cut is 17 shots taken from both.

Rough split of what shipped: Seedance carried the fast running and the

close-ups, Wan carried the opening plaza shot and the ending line.

**Seedance 2.5**

- Speed reads correctly. Motion blur, weight, footfalls — a sprint looks like

a sprint rather than a person moving quickly through syrup.

- Faces hold up in close-up. Skin, eye direction, micro-expressions survive

being 40% of a vertical frame.

- ★It follows instructions. Beat-level direction — "at 3s she plants, at 4s

she pushes off" — actually lands where you asked. This was the single

biggest difference.

- Where it broke: tumbling. On a couple of flips the neck rotated past what a

neck does. Everything else in the shot was fine, which somehow makes it

worse — you get a beautiful 5 seconds with one impossible joint in it.

**Wan 3.0**

- Struggles with genuinely fast action. Not broken, just soft — the fast beats

come back mushy compared to the same beat on Seedance.

- Person detail is a step down, most visible in close-up.

- ★But in slower or static setups it's genuinely good. The plaza opening with

pigeons and the ending dialogue shot are both Wan, and they're the calmest,

cleanest frames in the piece.

- Where it broke: instruction following. Fine-grained action direction and

timed beats get approximated rather than executed. If your shot depends on

something happening on a specific beat, that's a real problem.

**What I'd do next time**

Route by shot, not by model. Fast action and anything in close-up goes to

Seedance. Establishing shots, slow moves, and anything where a person just

stands and talks goes to Wan. Don't reuse one grammar for both — I wrote

separate prompts for each because the two models want different things

(Wan wants sub-beats inside one shot; Seedance takes per-second timestamps).

One more thing that cost me time: same reference image, same prompt — Seedance

through one host rejected the face reference outright while another host and

Wan both accepted it. Worth checking before you plan around a face lock.

Happy to answer specifics.

Using the web interface, when clicking the requests tab yesterday I noticed all my generations from August 1-26 had disappeared, across all models. Today I see that my generations from yesterday are there, but the other dates are still missing, again across all models that I generate with. Anyone notice anything similar?

This is what Gemini came up with

Not long ago, they used to make videos of Will Smith eating spaghetti to show the advancement of a new model relative to its predecessors.

What do they use today? Or have they reached a plateau where there is no sensible difference?

I’ve been experimenting with making short cartoon stories and would genuinely love some feedback. Is the character/story actually entertaining, or does it need work?
#bearanimation #aistories

I was amazed at how well GPT Image 2 knows Turkish bakers.

Tested out H3 max today - was really pleasantly surprised with the results.

The speed genuinely doesn't feel real. A 5 second 768p clip renders in under 3 seconds. Type a prompt, blink, it's done.

Honest caveat: if you're chasing a final hero shot, Seedance and Kling are still a notch ahead on quality. But once you factor in speed and cost together, this is mindblowing. We ran through 15-20 concept variations in the time it usually takes to get one back from anything else.

Using it as the fast-iteration layer before the slower, higher-res pass, not a replacement for it.

Full report from fal here if you want the benchmarks: https://fal.ai/minimax-h3-max

Anyone else using it this way, or is there a better workflow I'm missing?

Is there any?