WHY THIS SECTION EXISTS
Almost every conversation about generative imagery quality assumes there is a single quality dimension, that one model is 'better' than another in some absolute sense. This is wrong, and the wrongness causes systematic mistakes in product decisions, in model selection, and in evaluating generated outputs. Quality is multidimensional, and the dimensions that matter are different for different use cases. A model that is excellent for editorial illustration may be terrible for product photography. A model that wins benchmark comparisons may produce outputs that fail the brand-safety review of a Fortune 500 client. This section unpacks what quality actually means in this space, how to evaluate it for your specific use case, and how to think about brand-safe generation for commercial work.
A note on how this section relates to the two previous sections. The Craft of Generation covers the ten dimensions of good versus great from the perspective of the artist producing the work. The Operator's Playbook covers the infrastructure and business decisions of running a generative imagery company. This section covers the orthogonal question of how to evaluate whether a given output is actually good for a given use case, which matters for both practitioners and operators but is not reducible to either the craft or the business framing. If you are reading linearly, you arrive here with an intuition for what great work looks like (from Craft) and a plan for how to ship it at scale (from Operator), and this section gives you the vocabulary for assessing outputs against specific commercial and creative requirements.
Quality in generative imagery has at least eight distinct dimensions, and any given model is good at some and bad at others. Picking the right model for a use case is mostly a question of matching the use case's quality requirements to the model's quality profile.
The eight dimensions
Dimension
What it measures
Best for
Photorealism
Indistinguishable from a photo
Product, editorial, advertising
Stylistic distinctiveness
Has a recognizable aesthetic identity
Artistic, brand-driven work
Prompt adherence
Output matches text prompt accurately
Complex multi-element compositions
Compositional control
Respects layout and spatial language
Editorial layout, design
Text rendering
Generates legible, accurate text in images
Posters, signage, design
Coherence
Internally consistent (anatomy, physics, etc)
Anything with people or complex objects
Speed
Time per generation
Real-time, interactive, iteration-heavy work
Cost
Dollars per generation
High-volume production
Notice that no single model is best on all dimensions. FLUX is excellent on photorealism, prompt adherence, and coherence; weak on stylistic distinctiveness (its outputs are technically correct but aesthetically generic); medium on text rendering. Midjourney is excellent on stylistic distinctiveness; weaker on prompt adherence (it interprets prompts loosely in service of its aesthetic); medium on coherence. Ideogram is excellent on text rendering (it was specifically built for this) but weaker than FLUX on photorealism. Nano Banana Pro is excellent on prompt adherence and compositional control because it inherits Gemini's language understanding.
The right question for a use case is: which two or three of these dimensions matter most for me, and which model has the best profile on those specific dimensions? Asking 'which model is best' without specifying a use case is the wrong question.