Design Creativity Bench

In our benchmarks, GPT-6 Astra leads all evaluated models with an overall creativity score of 65.5, compared with 92.3 for human designers.

Across models working on identical briefs, their originality score sits at 0.592, compared to 0.764 when a human reference was included.

The difference is even more pronounced when the product context changes. Models show limited variation in their approach for two similar domains (clinic vs pharmacy), with a mean cross-pair distinctiveness of 0.575. Human designers adapt their approach more contextually, and this reflects in a score of 0.902.

This gap is evident even in the top-performing model. GPT-6 Astra produces the most original designs compared with other models, yet shows little variation across paired briefs, with a creative range of 0.472. GPT-6.1 Terra shows the least variation, with the lowest creative range of 0.469.

This highlights a fundamental limitation in the creativity of AI models. Models in general explore a narrower range of creative directions, and this limitation is also reflected in their adaptation to different contexts - still far behind from human creatives.

While AI models fall short of human designers on creativity, the best of them meet 99.2% of a brief’s requirements, a shade above our human designers at 98.0%. All 15 frontier models we tested clear 90%.

When models miss requirements, the errors are typically small and specific. Among the failures we observed, 33% involved missing controls or interface elements explicitly requested in the brief, while 26% involved inconsistencies in generated sample data.

Our results also suggest an interesting trend across model generations. Newer versions of GPT-Sol, Claude Opus, and Grok demonstrate stronger instruction following than their predecessors.

This highlights an important distinction between design execution and design creativity. Models have become remarkably capable of building interfaces that meet a brief’s requirements, but their ability to explore diverse creative directions has not advanced at the same pace. Improving design creativity remains a separate challenge from improving functional accuracy.