I asked 6 image models the same thing. Only 1 followed the brief.

Six image generation APIs were tested in a two-week period. Each model had its own rate limits and potential for generation errors. On a single Tuesday, the same prompt was sent to all six models simultaneously, with their results displayed in a grid format. Of the six outputs, five were visually appealing, while one was the only one that could be used as-is for a fintech landing page hero image with specific requirements.

This discrepancy highlights the importance of brief adherence over aesthetic quality when evaluating image generation models. One model performed exceptionally well with straightforward prompts and conventional visual styles, while another excelled in scenarios requiring typography or precise layout. A third model stood out for concept art and consistent character design, albeit at the expense of speed.

The remaining three models were situational, each best suited for specific tasks. The author argues that comparing AI image models in a generic sense is unproductive, as the optimal choice depends on the specific brief and output requirements for each project. To address this issue, the author developed a browser console that integrates all six models, allowing users to input their brief once and receive a grid of outputs from all models side-by-side.

This approach not only saves time and resources but also provides a clearer picture of which model best adheres to the specific brief, making the decision-making process more transparent and project-specific.