How to Compare AI Image Models Without Cherry-Picking

A gallery of best-case images tells you what a model can sometimes do. It does not tell you how often it will meet your brief. A useful evaluation keeps the task, inputs, and acceptance criteria visible, including the unsuccessful attempts.

Define a small test set

Prepare five original briefs drawn from real work: one product scene, one illustration, one text-heavy poster, one reference-image edit, and one small series of related images. Each brief should contain observable requirements. Avoid vague criteria such as “professional quality” unless you define what reviewers should inspect.

For every model, record the provider, exact identifier, test date, output size, quality settings, and input images. Use equivalent controls when possible and document meaningful differences. Generation-only endpoints should not receive an editing score as if they had failed an unsupported task.

Set the rules before generating

Allow the same number of attempts per task, for example four. Save every output rather than only the strongest one. Use identical initial briefs; if you later optimize prompts for a model, report that as a second round with its own time budget.

A practical review sheet can score each applicable criterion from zero to two: zero means failed, one means repair needed, two means accepted. Check instruction adherence, text accuracy, visual coherence, reference preservation, and readiness for the intended crop. Mark non-applicable criteria separately instead of treating them as perfect scores.

Review blind where possible

Hide model names and randomize the output order before asking colleagues to review. Separate aesthetic preference from hard requirements. A pleasing picture with the wrong product label should not pass a label-fidelity test.

Keep reviewer disagreements. They can reveal an unclear brief or a tradeoff worth discussing. With only a few images, describe the result as a pilot evaluation rather than a universal ranking.

Calculate cost per accepted asset

Add generation charges and the cost of cleanup time, then divide by the number of accepted images. If no image passes, report the task as unsuccessful; do not divide by zero or replace the result with a flattering estimate. Track elapsed time separately when queues or retries affect delivery.

Use the findings to choose by task. One workflow might suit text-heavy artwork while another fits reference edits. Repeat the relevant portion when a model version changes. A transparent, modest test is more useful than an impressive leaderboard with hidden settings.

For concrete task ideas, read our Qwen Image vs FLUX comparison and Qwen Image vs GPT Image comparison. Build your own brief from the prompt library, then begin a trial in the image generator.

Cover: editorial illustration generated with Codex, not an output from the models compared or a benchmark sample.