# A Text-and-Layout Benchmark for Free AI Image Generators
Readable words are one of the clearest tests of an AI image generator. A visually polished poster can still be unusable when a headline is misspelled, letter spacing changes between words, or the layout ignores the requested safe area.
Contents
A fair text benchmark should measure more than whether one lucky sample contains correct letters. It should test repeatability, hierarchy, and preservation during edits.
# Create four difficulty levels
Use the same visual style across four copy tasks:
- Single word: a short brand-neutral word with common letters.
- Headline: four to six words in one line.
- Headline plus subtitle: two levels of hierarchy with exact copy.
- Structured poster: headline, date, location, and short call to action.
Avoid trademarks and recognizable brand designs. The goal is to measure text handling, not imitation.
# Control the layout
For each prompt, define:
- canvas ratio and output resolution;
- text location as a region, such as upper-left or centered lower third;
- alignment and maximum line count;
- minimum empty space around the copy;
- contrast requirement between type and background;
- areas that must contain no text.
Use a simple composition first. Busy backgrounds can make even correct typography difficult to judge.
# Score five dimensions
| Metric | Question |
|---|---|
| Character accuracy | Are all letters, numbers, punctuation, and capitalization correct? |
| Word accuracy | Are words complete and in the requested order? |
| Hierarchy | Is the headline clearly stronger than supporting text? |
| Placement | Does the copy stay inside the required region and safe area? |
| Repeatability | How often does the full layout pass across repeated runs? |
Generate five outputs for every task. Count a strict pass only when the exact copy and mandatory layout constraints are correct. Publish the pass rate instead of selecting one successful example.
# Add an edit-preservation round
Take one passing poster and request a single copy change. For example, change only the event date while preserving the headline, subtitle, colors, type style, spacing, and background.
Then make a second edit that changes only the headline. Record whether the model rewrites correct supporting text or moves the layout. This reveals whether text generation remains controllable after the initial image.
# Include export usability
Text may be correct at preview size but break after export. Inspect the full-resolution file for malformed edges, inconsistent baselines, doubled characters, and compression artifacts. Record whether the platform adds a watermark or restricts the available resolution.
When comparing models, a free AI image generation workspace with multiple model options can keep the prompt, aspect ratio, outputs, and reviewer notes in a single test flow. The linked workspace is PhotoArtify, which is built by the authors of this benchmark.
# Use blind review when possible
Rename output files and hide the model source before scoring. Reviewers should see the brief and image, not the provider name. This reduces the tendency to forgive errors from a favored model.
Report the first-pass rate, median attempts to a strict pass, edit-preservation rate, and the most common error type. Separate aesthetic preference from copy accuracy.
# Conclusion
Typography tests expose whether a generator can follow exact constraints, not merely create attractive pixels. A useful benchmark repeats the same copy tasks, checks full-resolution exports, and measures whether correct text survives later edits. That evidence is more valuable than a gallery containing one unusually successful poster.
Disclosure: Written by PhotoArtify Team. We build PhotoArtify and may benefit if readers use the linked workspace.