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same prompt. same site. two very different directors.
kling 4.0 (90 credits) cut it into 4 shots on its own.
wan 3.0 prime (79 credits) held one long tracking take.
which one would you post?
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6 stills on nano banana 2.1, each animated with wan 3.0 image-to-video at 480p. 90 credits total.
make your own 1973 trailer: fastmoroai.com/image-to-vide…
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how we test text in AI images:
1. hide a "wrong" detail in the sign, like "sourDOUGH".
2. same prompt, same 1K tier, one shot each. no rerolls.
3. read it letter by letter.
most models quietly fix the typo. the good ones copy it.
what would you hide in yours?
gpt image 2.6 vs 2.5, one casing trap.
the window had to read "CroiSSants 3.50 / sourDOUGH 6 / 1/2 off after 7pm".
2.6 (1) nailed every letter. it even turned 1/2 into a real fraction.
2.5 (2) wrote "SourDOUGH". so close.
6 credits vs 2 per 1K image. Which one do you prefer?
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run the same trap on gpt image 2.6 and 2.5 here:
fastmoroai.com/chatgpt-image…
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gpt image 2.6 vs 2.5, one casing trap.
the window had to read "CroiSSants 3.50 / sourDOUGH 6 / 1/2 off after 7pm".
2.6 (1) nailed every letter. it even turned 1/2 into a real fraction.
2.5 (2) wrote "SourDOUGH". so close.
6 credits vs 2 per 1K image. Which one do you prefer?
prompt, word for word, same in both:
A rainy-evening shop window of a tiny corner bakery. Hand-painted gold lettering on the glass reads exactly: "CroiSSants 3.50 / sourDOUGH 6 / 1/2 off after 7pm". Warm light inside, wooden shelves of bread, a bicycle leaning against the wall outside, a passing tram reflected in the glass. 35mm photo, shallow depth of field, warm tungsten light.
both models are on one page:
fastmoroai.com/chatgpt-image…
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prompt duel #1
i hid a trap in the prompt: the chalkboard had to say "TONight only", broken casing and all.
nano banana 2.1 (1) politely fixed it to TONIGHT ONLY.
gpt image 2.5 (2) wrote "ToNight only". still wrong, but it clearly noticed something was weird. it also doodled a ramen bowl i never asked for.
both got all three prices right. 2 credits a picture.
for a sign that has to say exactly what i typed, i'd hand it to 2. the nicer street photo is 1 though. 1 or 2?
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prompt, word for word, same in both:
A hand-painted chalkboard menu outside a tiny ramen stall on a rainy night in Tokyo. The board reads exactly: "SHOYU 900 / MISO 950 / TONight only: YUZU SHIO 1100". Steam drifting from the stall, red paper lanterns reflected in wet cobblestones, a cat sleeping under the awning. 35mm photo, shallow depth of field, warm tungsten light.
both models are in the same text-to-image tab:
fastmoroai.com/text-to-image…
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friday night job for two video models: one photo of a dog in a velvet blazer, make him run the party. 8 seconds, one run each.
top: minimax h3, 96 credits
bottom: wan 3.0, 40 credits
h3 actually walks him to the edge and lands the bow, but skips the paw. wan does the paw and the bow, but fakes the walk with a fast zoom and the balloons pop in from nowhere. neither dropped them from the ceiling.
for a clean one-take party clip i'd pay the 96 for h3. wan is the cheap first draft.
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no prompt version: the preset party effect takes one clear photo and makes the performance for you, 10 seconds:
fastmoroai.com/ai-effects/bu…
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pixel fox, part 2.
last time both models held the pixel look for 10 seconds. this time it's 12 seconds and three things to do in a single run: rooftop sprint, drop into the lantern street, stop and look back while it snows.
top: wan 3.0, 60 credits
bottom: kling 4.0, 107 credits
same start frame, one run each.
first crack was kling, in beat 1: the fox jogs on the spot for about 3 seconds while the camera sits still. wan scrolled the whole rooftop run, then dropped into a different street and swapped the paper lanterns for wall lamps.
kling takes the ending: it dives through the hanging lanterns, turns to the camera, and the snow piles up on every roof. i'd post the bottom one.
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one start frame, one three-beat prompt, both models in the image-to-video tab:
fastmoroai.com/image-to-vide…
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prompt duel #2
the test was one tiny detail: whiskers carved into a pumpkin, not painted on. three image models, one run each, 2 credits a picture.
nano banana 2.1 (1), gpt image 2.5 (2), qwen image 2.1 (3).
2 cut all six whiskers clean through, they glow like the eyes. 1 carved them as shallow grooves, so they barely catch the candle. 3 scratched a few faint lines and forgot the cat was supposed to be sleepy. nobody painted them on.
2 goes on my porch: it's the only one where the whiskers actually light up. 1, 2 or 3?
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prompt: a carved pumpkin on a wooden porch step at dusk, the face is a sleepy cat with whiskers cut into the skin, candle glow inside, light drizzle, 35mm photo
all three are in one text-to-image tab:
fastmoroai.com/text-to-image…
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saw this and ran the same test on the two video models on our site.
same pixel-art start frame. same prompt.
left: wan 3.0 (50 credits)
right: kling 4.0 (90 credits)
both held the pixel look for the full 10 seconds. the fox stays a fox.
wan sends it over the chimney stacks. kling keeps the lantern street glowing the whole run.
for 50 credits i'd ship the left one.
Here's why ai video models like Sora or Veo can't animate pixel art - they understand motion, but they don't understand pixels. Here's Veo side by side with the animation model I trained. Veo makes very smooth video, but it looks completely unnatural for pixel art, it doesn't keep a pixel grid or get the frame rate right. Meanwhile pixel engine kinda nails it.
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same start frame, same prompt, both models in one place if you want to try it yourself: fastmoroai.com/image-to-vide…
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one photo in, pick a costume or edit the prompt. 3 credits a swap at 1K: fastmoroai.com/ai-halloween-…
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both made on FastMoro AI: text-to-video, 720p, 10s, same prompt. try kling 4.0 here: fastmoroai.com/video-model/k…
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