AI Image Generator Selection Matrix
Choose an image model by the constraint that matters most.
- Difficulty
- Easy
- Time to result
- ~days to results
- Steps
- 5
- Confidence
- 97%
The AI Image Generator Selection Matrix begins with the creative constraint rather than a favorite model. Marketers define whether success depends most on aesthetic polish, realism, exact prompt adherence, pose control, editable vector output, or legible text. They then route the task accordingly: Midjourney for distinctive aesthetics and realism, DALL-E 3 for including many specified elements, Leonardo AI for Stable Diffusion-style control through a web interface, Recraft for scalable vectors, and Ideogram for text inside images. After selecting a model, the marketer generates multiple candidates, checks mandatory elements and brand fit, and tests viable variants in the actual channel. This converts tool choice from an open-ended comparison into a repeatable decision rule tied to the required output and measurable campaign performance.
Origin
Extracted from Marketing Against The Grain during a comparison of five image-generation tools and their strongest marketing use cases.
Core principles
- 01Match the model to the dominant creative constraint.
- 02Separate aesthetic quality from instruction accuracy.
- 03Use editable formats when downstream design work matters.
- 04Test generated images against campaign outcomes.
How to run it
- 1
Define the image's job
Specify where the image will appear and what action it should support. Identify whether it is an ad, thumbnail, logo, illustration, or reusable design component.
Pro tip Write down the required dimensions and downstream editing needs before generating anything.
Watch out A visually impressive image may still fail if it is unsuitable for its placement.
- 2
Identify the dominant constraint
Rank aesthetic quality, realism, prompt accuracy, customization, vector editing, and embedded text. Select the one requirement that cannot be compromised.
Pro tip Separate mandatory elements from optional creative details.
Watch out Trying to optimize every dimension simultaneously makes tool selection ambiguous.
- 3
Route to the matching model
Use Midjourney for aesthetics and realism, DALL-E 3 for complex prompt adherence, Leonardo AI for control, Recraft for vectors, or Ideogram for legible text. Switch tools if the primary constraint changes.
Pro tip Keep a simple internal table of model strengths and update it as products evolve.
Watch out Model capabilities change quickly, so periodically retest old assumptions.
- 4
Generate and inspect variants
Create several options and verify every mandatory element. Reject outputs with incorrect text, missing objects, unusable composition, or weak brand fit.
Pro tip Generate variants with room for headlines, logos, or interface overlays when needed.
Watch out Do not mistake prompt compliance for conversion performance.
- 5
Validate in the channel
Deploy qualified variants in the actual ad, thumbnail, or campaign context. Compare click-through, conversion, or engagement data and retain the winning creative pattern.
Pro tip Test the image alongside stable copy to isolate its impact where practical.
Watch out Do not generalize one channel's winning aesthetic to every audience and placement.
In the wild
The host reports using Midjourney to create images specifically matched to Facebook ad copy. Rather than settling for generic stock photography, the team produced a closer visual fit and observed a substantial improvement in performance.
→ Prompt-specific creative outperformed the previous image approach.
A marketer needs an illustration containing a presenter, a product box, a city backdrop, and several precise props. Because including every requested element is more important than achieving a signature artistic style, the marketer chooses DALL-E 3, checks each requirement, and generates alternatives for testing.
→ The final creative contains the required campaign elements without repeated manual compositing.
A designer needs an AI-assisted logo that can be enlarged for signage and edited in Illustrator. The designer chooses Recraft for vector output, refines the generated shape manually, and adds the final typography outside the generator.
→ The team receives a scalable starting asset suitable for professional design work.
Common mistakes
Choosing only by visual beauty
The most attractive model may omit required objects or produce unusable text. Select according to the campaign's non-negotiable constraint.
Expecting complex prompts to transfer
The same detailed prompt can behave very differently across models. Adjust both the tool and prompt structure to the intended output.
Publishing without channel testing
Generated creative should be evaluated by real marketing outcomes, not merely by internal preference.
Is it for you?
Best for
It is best for marketers producing ads, thumbnails, illustrations, logos, or campaign assets across recurring workflows.
Not ideal for
It is not ideal when brand, legal, or product-accuracy requirements demand fully controlled photography or manual illustration.
From the transcript
“the most flexibility customizability Leonardo a the most prompt adherent if you want all of these elements in your image Dolly 3 if you want…”
“recraft that is going to be your your friend because they're not just giving you a plain image format like a JPEG or a bit…”
“idiogram is probably the best in the market for adding text to your image”
From the episode
Matt Wolfe Ranks The Best AI Tools For Marketers In 2024
Matt Wolfe