01 / Protocol
Image quality
Same prompts, three planned attempts per enrolled model, separate quality, adherence, cost and latency measures.
Inspect image quality →Donatello AI Benchmark / Protocol & evidence
A transparent testing programme for AI images, references and real creator workflows. Inspect the protocol, download the inputs and see exactly what has—and has not—been measured.
Initial release: protocols and documentation estimates only. No comparative generation results, public votes or quality winners have been published.
01 / Protocol
Same prompts, three planned attempts per enrolled model, separate quality, adherence, cost and latency measures.
Inspect image quality →02 / Protocol
Inspect source provenance, readiness and checks for identity, products, style and object consistency.
Inspect reference fidelity →03 / Documentation simulation
Explore $0, $5, $10 and $20 against three creator briefs. Feature availability is not a completed workflow.
Inspect what does $10 actually buy? →04 / Accountability
Sampling, failure accounting, independent review, cost formulas, privacy gates and versioning.
Inspect every rule in the open →The existing gallery proves that particular Donatello outputs were generated. It does not compare equal prompts and attempt counts across providers. The platform comparison pages document features and licences. Neither is relabelled here as a measured quality benchmark.
This protocol release makes the next experiments inspectable before execution. Image testing needs an enrolled model cohort and approved generation budget. Reference testing also needs cleared source assets. A protocol alone cannot answer which tool makes the best image.
Spanish/English voice, text-to-video, image-to-video, faceless YouTube, music and complete-ad workflows remain future tracks. Native-audio video will be separated from silent video; provider voices from controlled voice; and instrumental music from songs. Public voting and measured leaderboards are not active.