Model architecture and capabilities, Inference latency and output quality metrics, and Price per image or token usage are the correct criteria to emphasize when comparing foundation models in Amazon Bedrock for high resolution marketing visuals.

Model architecture and capabilities determine whether a model can produce photorealistic images and support the style controls and guidance features you need. You should evaluate support for high output resolution prompt conditioning fine tuning or adapters and any built in tools for controlling composition and color because these factors directly affect the final marketing imagery.

Inference latency and output quality metrics show how the model performs in real world workflows and how visually convincing the images are. Measure latency under expected load and track image quality using automated metrics such as FID and complemented by human review because automated scores do not always capture aesthetic suitability for marketing use.

Price per image or token usage matters for a 90 day comparison because cost scales with volume and with provider specific pricing in Bedrock. Compare estimated costs for your expected output rates and include any extra costs for higher resolutions or multiple generation passes so you avoid surprises when you scale.

BLEU score is incorrect because it is a text based metric for machine translation and other NLP tasks and it does not assess image quality or visual fidelity.

Amazon Rekognition is incorrect because it is an image analysis service rather than a generative foundation model and it is not an evaluation criterion for selecting which Bedrock image model will create marketing visuals.

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