u/adarshaadu

This has to be one of the most common frustrations in AI image generation and yet most discussions about it stay surface level without actually explaining why it happens or which models have made real progress on solving it.

The core issue is that most image generation models learn visual patterns statistically. They understand what the letter "A" looks like visually but they don't understand spelling or language structure. So they can approximate text that looks like text from a distance but falls apart on close inspection, swapped letters, missing characters, gibberish that almost looks right but isn't. It's essentially the same reason early AI struggled with hands, the model is pattern matching rather than understanding structure.

The models that have made the most progress on text rendering, roughly ranked by accuracy:

ideogram at roughly 90% accuracy, the highest in the industry. Clearly prioritizes typography and layout as core competencies. Best for logos, posters, ad designs, anything where text IS the design.

nano banana pro handles text well, particularly in image to image contexts where you're editing or adding text to existing visuals. Strong for photorealistic scenes with text elements.

seedream 4 renders text reliably across multiple generations, useful for consistent branding elements in a series.

flux 1 kontext takes a different approach, letting you upload an image and edit/add text while keeping everything else consistent. Not generating text from scratch but practically useful.

The models that still struggle most are the ones optimized purely for artistic quality or photorealism. Midjourney, standard flux versions, and most open source models still produce unreliable text because that wasn't their training priority.

Going through the Global Talent process in the digital tech category taught me something I did not expect: it wasn’t enough to list projects and roles, I had to clearly explain why they mattered. For example, instead of just saying I spoke at conferences, I included how many people attended, feedback I received, and what opportunities came from it. That context made a subtle but real difference in how my work was perceived.

If anyone here is wrestling with how much detail to include, talk less about titles and more about measurable impact.