Which AI Image Model Fits Your Work? Run a Small, Fair Comparison

One model gives you a beautiful poster; another preserves your product better. Which is better for your work? A single favorite image cannot answer that question, especially if the prompts and settings were different.

Compare the task you repeat, not a general promise of quality. A catalog editor, illustrator, and social designer may reasonably choose different models from the same set of results.

Define a pass before generating

Choose two or three briefs drawn from real work. For example: a new still life, a background edit of an actual item, and an illustration with a fixed palette. Decide which errors make an output unusable before seeing it.

For a product edit, an altered cap may be an immediate failure even if the lighting is excellent. For an illustration, a slightly different texture may be acceptable if the composition works. Do not let visual polish silently change your acceptance criteria.

Use the same inputs in DrawNew

Review the model library to choose candidates that support the task. GPT Image 2, Nano Banana 2, and Nano Banana Pro provide image creation and editing options to inspect.

Open the creator and keep the prompt, uploaded source, and requested ratio consistent. Use comparable supported sizes. If a setting is unavailable on one candidate, record the difference instead of calling the comparison identical.

Check each credit estimate before submitting. This article proposes a comparison process; it does not report a benchmark or declare a measured winner.

Try one brief you can score

For a text-to-image task:

A matte white ceramic cup on a pale blue table. Soft window light from the left, a believable contact shadow beneath the cup, and generous empty space on the right for a headline. No lettering, logos, or extra objects.

For a reference edit, use a real product photograph and request a simple neutral background while keeping its shape, label, and viewing angle. Do not compare one model's text-generated product with another model's reference edit as though they received the same task.

Use AI Product Photography if that is the workflow you actually plan to repeat.

Keep a review sheet, not just a favorites folder

Record	What to write
Inputs	Prompt version, source filename, model, ratio, size
Hard requirements	Passed or failed, with the exact reason
Useful qualities	Composition, material treatment, or style fit
Rework	What still needs manual correction
Cost	Credits displayed and number of attempts

Save rejected results too. Otherwise the folder remembers only the lucky output and hides how many attempts it took to get there.

A useful personal measure is total generation credits spent divided by accepted outputs. For example, 24 credits spent for three accepted images is eight credits per accepted image. That is arithmetic for your own experiment, not a price or performance claim about any model. If none pass, report that rather than inventing an average.

Separate prompt quality from model choice

If every model misunderstands the same instruction, inspect the brief and source before expanding the comparison. Contradictory constraints, an unreadable reference, or a missing layout requirement can affect the whole experiment.

Once a candidate looks promising, repeat a small sample within your budget to see whether the outcome is dependable enough for your task. Avoid treating one success as a guarantee.

For a decision driven by failed edits, read why unwanted changes happen. For a composition problem, fix the ratio and crop plan before comparing more models.