Flowers looked like a category that would expose AI. Organic, delicate, irregular, a bouquet is dozens of translucent petals, recognizable species at the right scale, and the subtle imperfection that reads as fresh. The classic AI failure is the opposite of all that: waxy petals, blooms fused into each other, duplicated impossible flowers, and a too-perfect plastic look that says "generated" instantly. So I expected the uncanny tell to be the story.
It was not. I ran one brief, a hand-tied bouquet of blush garden roses, white ranunculus, and eucalyptus wrapped in kraft paper, in soft morning light, through four of the strongest image models with the same prompt: Nano Banana 2, GPT Image 2, Seedream 4.5, and FLUX.2 Pro. All four produced believable, fresh, coherent bouquets. Flowers turn out to be one of the categories AI is genuinely good at, and the only real separator was how alive the petals looked. This is the flowers entry in our product-photography series, alongside the skincare, jewelry, supplements, makeup, food and beverage, footwear, candles, clothing, furniture, electronics, handbags, sunglasses, glassware, watches, perfume, packaging, pet products, toys, textiles, cookware, stationery, drinkware, soap, ceramics, art prints, earbuds, houseplants, knives, and automotive wheels tests and the broader best AI image model for product photography roundup.
Quick answer
- Best overall, and cheapest photoreal: Seedream 4.5. The best petal translucency and freshness, including dew on the blooms.
- The good news: all four produced fresh, coherent, species-accurate bouquets. No waxy or fused-bloom failure. Flowers are a reliable category.
- The only real separator: petal translucency. Seedream made the flowers glow; the others were fresh but flatter up close.
If you only remember one thing: flowers are the easy case now, so you can trust most models for the arrangement and choose on petal realism. Use a reference photo only when a specific recipe has to match.
The test, model by model
One brief, four models, same prompt. I judged realism first, freshness versus the uncanny waxy look, then petal translucency, then arrangement coherence.
Seedream 4.5 made the most alive bouquet. The blush roses carry dew, the petal edges transmit light the way a fresh petal does, and the whole arrangement reads as just-cut rather than rendered. This is petal translucency, the floral equivalent of the in-glass refraction it nailed on glassware, and it is the detail that separates a premium floral hero from a flat one. Best result, lowest cost of the photoreal options.
Nano Banana 2 produced the most complete, true-to-life florist shot: the garden roses have the right ruffle and scale, the ranunculus and eucalyptus are recognizable, and the kraft wrap with twine finishes it. The blooms are distinct, not fused, and the whole thing looks fresh. Up close its petals are a touch flatter than Seedream's dewy edges, but as a finished, ready-to-list bouquet it is excellent.
GPT Image 2 gave a clean, slightly more studio-polished bouquet: the petals are soft and accurate, the layered ranunculus read correctly, and the arrangement is coherent. Its only faint tell is the opposite of waxy, a touch too even and tidy, the sort of perfection a slightly looser prompt would relax. Fresh, believable, and the priciest of the four.
FLUX.2 Pro produced a believable bouquet in a softer, wider composition, laid on its side with the kraft wrap. The blooms are coherent and the species are accurate, and it is fresh rather than waxy. Its tradeoff is detail: a softer rendering with less of the petal translucency the macro shots showed, which is the usual FLUX pattern of a strong overall image with the fine detail dialed back. Good for a quick catalog or social shot at the lowest cost.
The comparison
| Model | Realism (fresh vs waxy) | Petal translucency | Arrangement coherence | Rough cost/image |
|---|---|---|---|---|
| Seedream 4.5 | Best, dewy and alive | Best, real dew | Coherent | ~4.8 credits |
| Nano Banana 2 | Fresh, true to life | Good | Coherent, distinct | ~9.3 credits |
| GPT Image 2 | Fresh, slightly tidy | Good | Coherent | ~26.4 credits |
| FLUX.2 Pro | Fresh, softer | Less detail | Coherent | ~3.6 credits |
Credit costs are first-hand from this test on Masonry; per-image rates move, so check current pricing.
Why flowers are the category AI handles best
After a series of tests where the model quietly broke something, a label, a shade, a logo, a screen, flowers are a refreshing counterpoint: the category where from-scratch AI is most reliably strong.
The thing that would expose a fake is the thing AI now does well. A bouquet has no exact text, no regulated panel, no precise brand silhouette to get wrong. It needs organic irregularity and convincing petal texture, and flowers are so abundant in training data that all four models produce that fluently. The uncanny waxy-flower look is largely a thing of older models; here, none of them fell into it.
Translucency is the premium tier. The one real differentiator is how alive the petals look, whether light transmits through the edges and the bloom carries dew. Seedream rendered that; the others were fresh but flatter. For a high-end floral brand that is the detail to chase, and it is worth judging at full zoom.
Identity is the only caveat, and it is mild. A text prompt gives you a beautiful generic bouquet, not your signature arrangement. Bloom counts and placement vary by model. For a specific recipe you sell, generate from a reference photo of the real bouquet. But for concepts, seasonal variations, and catalog volume, flowers are about as safe as AI product photography gets.
How to shoot your floral line without the wilt clock
Flowers add a logistical reason to use AI beyond cost: real stems wilt, so studio shoots are time-pressured and have to be redone every season. AI never wilts and produces seasonal variations on demand. Run your concept through two models and choose on petal realism, since the arrangement will be believable on all of them. For a signature bouquet, feed a reference photo so the flower mix and composition are yours. And if a result looks a touch too perfect, loosen the prompt toward a hand-tied, just-gathered look.
With the Masonry CLI you can fire the same bouquet prompt at every model and compare petal realism side by side, or pass your real arrangement as a reference:
masonry image "hand-tied bouquet of blush garden roses and ranunculus, kraft wrap, soft morning light, dewy petals, photoreal" --model seedream-4-5 masonry image "recreate this exact bouquet in a sunlit shop scene, keep the flower mix" --ref ./real-bouquet.png --model gpt-image-2
The bottom line
Flowers are the easy case, in the best sense. All four models produced fresh, coherent, believable bouquets with no uncanny tell, so you can trust the arrangement and choose on petal realism: Seedream 4.5 for the dewiest, most alive macro at the lowest cost, with Nano Banana 2 and GPT Image 2 close behind. Use a reference photo only when a specific recipe has to match. See how the same fidelity-first logic plays out across every product type in our best AI image model for product photography roundup, or run your own arrangements from one place with the Masonry CLI.
