The problem: too many image models, which one do I use?

New image-generation models keep landing — Nano Banana, Nano Banana Pro, GPT Image 2, ByteDance's Seedream — and each claims to be the best. But when you actually need one good image, the real questions are:

• Same request, different model — how much does the output actually differ?


• Re-tuning the prompt for every model is exhausting.


• Comparing them means hopping between platforms and signing up over and over.

So I ran a small, reproducible test: fix one prompt, feed it to several models, look at the differences, and boil it down to a selection cheat sheet. To avoid the multi-platform shuffle I did the comparison on cvy.ai, where you switch models from a dropdown on the same prompt — no re-registering, no rewriting.

Step 1: make the prompt reproducible with a template

A fair comparison needs a stable, reusable prompt — otherwise the differences you see are just you writing it differently each time. I break prompts into fixed slots:

[subject] + [style/medium] + [composition/lens] + [light/mood] + [details] + [aspect ratio]

A portrait example:

Subject: a young woman in a casual wool coat, half body, glancing toward camera

Style: cinematic realism, warm film tone

Composition: 85mm telephoto, shallow depth of field

Light: golden-hour, a glowing storefront neon sign reading "CAFE" in the blurred background, rim light on hair

Details: natural skin, windswept hair strands, no over-smoothing

Aspect ratio: 3:4 vertical

Note the neon sign reading "CAFE" — it's deliberate. Text inside an image is one of the clearest ways models differ, so keeping a short word in the scene makes the text-rendering comparison below much more telling.

Flatten that into one continuous prompt and every model gets identical input — that's what makes the comparison mean something.

💡 Tip: instead of staring at an empty prompt box, keep a few reusable templates (portrait / product / scene / social cover) and just swap the subject. cvy.ai ships a set of editable templates I use as a starting point — faster than writing from scratch.

Step 2: same prompt, four models side by side

GPT Image 2 — the winner.

• 
  Prompt Adherence: Excellent. The "windswept hair" is dynamic and natural. The pose of the subject glancing over her shoulder adds superb narrative depth.


• 
  Text Rendering: The spelling of "CAFE" is perfect. The neon glow and bokeh effect integrate flawlessly with the optical physics of an 85mm lens.


• 
  Lighting & Vibe: Perfectly captures the "golden-hour" backlight. The rim light on the hair is spot-on, and the warm film tone is rich and cinematic.


• 
  Details & Textures: The skin retains authentic texture without feeling over-smoothed. The wool coat texture is slightly soft but generally solid.


• 
  Review: The absolute winner of this test. It completely nails the "cinematic realism" requirement with a perfect balance of atmosphere and accuracy.

Seedream 4.5 — biggest visual impact.

• 
  Prompt Adherence: Follows the composition well, though the "windswept hair" feels a bit forced and slightly clumpy rather than naturally blown by the wind.


• 
  Text Rendering: "CAFE" is perfectly legible, featuring a very strong and bright neon glow.


• 
  Lighting & Vibe: Takes a highly aggressive approach to the "golden-hour" and "rim light" prompts with intense backlighting. This creates massive visual impact, though it sacrifices a bit of the soft film vibe requested.


• 
  Details & Textures: The wool coat texture is well-rendered. However, while freckles are present, there is still a faint hint of "AI smoothing" on the skin, making it feel slightly less than 100% natural.


• 
  Review: The strongest visual impact. While slightly over-rendered, it is incredibly polished and ready for direct commercial use.

Nano Banana Pro — texture king, lopsided.

• 
  Details & Textures: The undisputed king of textures. The coarse, pillowy grain of the wool coat, the natural facial imperfections, and the ultra-realistic skin pores demonstrate terrifying microscopic rendering capabilities.


• 
  Text Rendering: "CAFE" is clear, but the neon tube structure looks somewhat stiff and doesn't blend seamlessly into the background lighting.


• 
  Lighting & Vibe (Major Deduction): It completely missed the "golden-hour" and "warm film tone" instructions. The lighting is incredibly flat (resembling an overcast afternoon), and the requested rim light on the hair is almost non-existent.


• 
  Prompt Adherence: The hair is messy, but it lacks the dynamic motion implied by "windswept."


• 
  Review: A hyper-realistic but lopsided specialist. It is visually flawless if you only care about micro-textures, but it completely failed to follow the core lighting and atmospheric instructions.

Nano Banana — baseline.

• 
  Prompt Adherence (Major Deduction): The hair is perfectly neat and tucked away, entirely ignoring the "windswept hair" prompt.


• 
  Text Rendering: Suffers from AI hallucination; an extra glowing accent mark appeared above the 'E', spelling "CAFÈ" instead of "CAFE".


• 
  Lighting & Vibe: The lighting is dull with only a very faint hint of a sunset glow. It lacks cinematic tension, and the background blur feels rigid and artificial.


• 
  Details & Textures: The coat feels more like flat felt than coarse wool, and the skin details are the flattest among the four candidates.


• 
  Review: Baseline performance. It missed multiple instructions and falls significantly behind the other models in this test.

Step 3: the cheat sheet

Same prompt across the board, distilled (★ = relative strength from this test):

The takeaway: no single model wins on every axis. The skill is matching the model to the job — cinematic storytelling from one, raw texture from another, high-impact commercial polish from a third. That's exactly why I didn't want to juggle separate platforms: compare once, and you know which job goes where.

A reusable generation workflow

What the experiment settled into as my default:

1. 
   Start from a template — pick the closest prompt template, swap the subject.


2. 
   Pick the model by task — use the cheat sheet; don't default to the same one every time.


3. 
   Add a reference image when you need direction — when text alone won't land it, upload a reference so the result follows an existing look.


4. 
   Iterate in small steps — keep prompts, styles, model choices, and good samples together so each render informs the next.

I run the whole flow on cvy.ai — templates, multiple models, and text-to-image / image-to-image in one workspace — which suits a "compare fast, iterate often" habit. The method itself is platform-agnostic, though; any multi-model tool works.

Wrap-up

• Don't pick a model by vibes — run one identical prompt across all of them.
  


• Keep prompts structured and templated so comparison is fair and reuse is cheap.


• Remember: match the model to the task, don't worship one.

If "which image model should I use?" keeps tripping you up, spend ten minutes running your own same-prompt comparison — the conclusion beats any review. The portrait template and cheat sheet above are yours to copy.