AI image models are great at creating images, and text generation is improving fast, but results still vary depending on the model you choose.
Words in a generated image often look fine at first glance, but then you might notice the spacing between the letters is uneven, the letters warp or disappear altogether, and spelling changes. While the generated image might still look good, the AI text in images might not. That’s why knowing which model to use for AI text in images is so important.
In this article, we will break down how to choose and use the right model to get the best results when creating text for your images and videos. But first, let’s delve into what typography is and why it challenges gen AI. Knowing where AI text breaks and where it usually works will help you choose the right model and not waste time fixing text mistakes later.
What typography actually means
Prompt: “Editorial photograph of a concrete studio wall covered in large printed text, like a creative manifesto. The text reads: “Design is not decoration. It’s structure, rhythm, and intention. When words lose clarity, meaning collapses with them.” The text is arranged in multiple lines, left-aligned, modern sans-serif type. Natural window light, soft shadows, realistic print texture on the wall. High-end design magazine aesthetic, calm but serious tone.”
Typography isn’t just putting words on an image. It’s how the letters are shaped, spaced, aligned, and arranged so the text is clear and readable. Good typography affects everything from how fast you read to what you notice first, as well as how professional something feels.
Fonts have fixed rules for letter shapes, spacing, weight, and rhythm, and headings, body text, and captions each behave differently, but they stay consistent across sizes and layouts.
Why does AI struggle with text?
Prompt: “Double-page magazine spread layout viewed from above. Background image: modern creative workspace with books, sketches, and soft daylight. A large block of editorial text overlays the image. The text reads: “Good typography disappears when it works. You notice it only when something feels off, when spacing breaks, when letters stop behaving like language.” Clean margins, realistic print layout, premium design magazine style.”
Most AI image models are great at creating the look of text, but they treat typography as visuals instead of a rule-based system. That’s because most AI image tools work in pixels, so they treat typography as an image. They draw something that resembles letters, without knowing how those letters should work together across different image sizes, crops, or versions.
This means that text that looks fine in one image might change in the next, especially when you regenerate, resize, or animate it. The letters can drift or disappear, the spacing changes, or words suddenly become unreadable when you zoom in or use the image for animation.
AI is great for exploring, but it’s not as reliable when you need your text to stay the same every time you create AI-generated text in images.
8 typography tips for success
AI can handle text, but different models prioritize different things, like speed, flexibility, or precision. If you’re patient and regenerate a few times, most models can get short text right, especially if you keep the layout simple and the text isn’t very detailed.
1: Be clear about what you actually need the text to do
Before you choose a model or write a prompt, stop and think about what the text is doing in the image. That decision shapes everything that follows, including which model makes sense.
Expressive or experimental text gives you more freedom. Headlines and thumbnails are stricter because the words still need to read clearly at small sizes and survive cropping or motion.
Logos, brand visuals, and text that move inside video are the hardest to get right. These cases need consistent letters and clean spacing, which only some models can handle well.
2: Know where AI text generation works well
AI text is most reliable when there is not much to read. Short headlines, bold words, and simple layouts usually hold together.
Once the text needs to be exact, even small errors start to matter. At that point, the text should always be checked, and often adjusted by hand in your editing software.
3: Know where AI typography still struggles
AI font generation still needs more guidance across different models. Small text is harder for models to keep stable, meaning letters can blur together, spacing can collapse, or individual characters can change shape. Letters blur, spacing falls apart, and mistakes become hard to fix without starting over. Long sentences create similar trouble, especially when line breaks need to stay clean.
Logos and brand systems are another weak spot. Fonts depend on the same letter shapes appearing every time, and most models cannot guarantee that. In these cases, AI can suggest a direction, but final text will likely still need manual work.
4: Keep text short
The more words you add, the harder it is for the model to keep the text stable. Short text gives AI less to manage and fewer places to make mistakes. It also makes problems easier to see and faster to fix.
5: Don’t expect AI to handle paragraphs
Paragraphs ask a lot from image models. They need steady spacing, clean line breaks, and consistent letters across many words. Most models still struggle to keep all of that stable.
If your design needs sentences or body copy, treat AI text as a rough stand-in. Use it to shape the image, then rebuild the text manually.
6: Know when to stop regenerating and just fix it
Knowing how to fix AI-generated text helps. Regenerating the same image over and over isn’t the best way to get perfect text. Small mistakes tend to move around instead of disappearing. It’s up to you how long you want to spend playing around with the model to produce something perfect.
The best way to fix AI-generated text is to stop when the image is mostly right. Fix spelling, spacing, or alignment outside the model, where you have full control.
7: Treat AI typography as a starting point, not the final
AI is really good at suggesting direction, and for testing layout and scale, but not for final text.
Use it to explore ideas quickly, but plan for a human pass before anything goes live.
8: Understand how different models handle text
Some models are better than others at AI typography. Some handle spelling better but struggle with layout. Others look solid at first, then fall apart under closer inspection.
Once you understand how a model behaves with text, it becomes easier to know when to trust it and when to step in.
How we’re comparing AI typography tools
We looked at each model’s AI text accuracy, including:
- Does the text come out spelled correctly?
- Does spacing stay even?
- Do results stay stable across a few generations?
The examples below were generated using the same prompt: “Create a scene of the Hollywood hills, except instead of the Hollywood sign, the letters now read ‘This is the sign you’ve been waiting for…Do the thing. Book that trip. Sing that song. Tell that person you love them.” Realistic, soft daylight, candid photography.”
Nano Banana Pro: the safest option for readable text
The text generated with Nano Banana Pro text to image is consistently readable across generations, with minor shifts in letter spacing and shape. Short phrases hold up well, but longer sentences show small inconsistencies.
Nano Banana Pro is currently the most reliable option when spelling and legibility matter. Headlines, short phrases, and clear callouts usually come out readable, even at smaller sizes. It generates usable text more often than most other models.
Spacing and layout hold together better across generations, although you can still see uneven letter spacing or a broken letter now and then. This makes Nano Banana Pro a great choice for thumbnails, ads, and social visuals where words need to be read fast.
It can be less accurate when you give it longer text and very specific branding. Paragraphs, fine print, and exact font control are still tricky to control.
Flux 2.0 Pro: good-looking text with accuracy trade-offs
With Flux 2.0, letter shapes and layout look good, but spelling and spacing vary between generations. Visual consistency is stronger than text accuracy.
Flux 2.0 Pro often generates text that’s readable at first. For expressive visuals or bold headlines, this can work well.
Things get a little trickier when it comes to accuracy. Spelling mistakes show up more often, and small inconsistencies appear when you compare multiple generations. This makes Flux less predictable when text must be exact.
Flux 2.0 Pro makes sense when the text needs to be more visual than informational. It’s better for mood, style, and impact than for anything that needs careful reading.
GPT Image 1.5: improving fast, but still uneven
Short words often render correctly, but individual letters change between versions, with GPT Image 1.5. Text accuracy improves in some generations, while it changes in others.
GPT Image 1.5 has improved a lot in how it handles text. Short words and simple phrases sometimes come out correctly. Otherwise, it seems that the model is trying its best to keep the text accurate and that it doesn’t put as much effort into generating the image, as can be seen with the three almost-identical generations here.
Kling O1: Image-to-image
Prompt: “Refine the ‘Do the thing’ in the Hollywood Hills image (generation 1 with Nano Banana Pro) while preserving the existing text exactly. Do not change spelling, letter shapes, letter spacing, line breaks, or placement. Improve lighting, contrast, texture, and realism. Keep the layout identical.”
Prompt: “Keep the text exactly the same. Change the background to a rainy night. Preserve the typography layout, size, and placement. No changes to the words.”
Prompt: “Keep the text exactly the same. Change the setting to Westminster Bridge with Big Ben in the background. Preserve the typography layout, size, and placement. No changes to the words.”
You can generate text from scratch with Kling O1. But the best use case when it comes to typography is when you create text you’re happy with elsewhere, and then use Kling O1 to adjust the image around the text. Kling O1 will reliably keep the text consistent across many interactions.
This is great for branding, motion, or any project where text needs to stay consistent across frames. Small adjustments and visual refinements are easier than full regeneration.
Kling O1 performs better with some types of prompts than others. For example, in generation 3, you can see that the text is much less accurate.
How creators combine AI typography models in real workflows
Most creators don’t stick to one model from start to finish, and this is true when creating AI-generated text, too. Use the less exact models early on to play around with layouts, tone, and type placement without worrying too much about errors. Then, they switch to a model that handles spelling and spacing more reliably. You can also move to an image to image to stabilize what already works. For example, use a quicker model to create a rough draft of a thumbnail. Then, once you have the general mood and layout down, switch to a more text-stable model, or even image-to-image, to make sure the wording and placement are where you need them to be.
What to expect next for AI typography
AI image models are getting better at short words, clear letters, and basic layout. They can still be unreliable when generating long and repeatable text in exact fonts,
Your model choice makes a real difference. Accuracy always matters, but not every stage of a project needs absolute perfection. Earlier drafts need to be quick and flexible, while more final or production-ready assets need to be accurate, stable, and precise
The goal isn’t finding a model that “does text perfectly”. It’s knowing when AI helps to save you time, when it slows you down, and when to take over before small errors turn into bigger fixes.
Models are improving, but for now, some are better than others. In the meantime, knowing how Artlist’s different models handle AI text can help you choose the right model for each stage of your workflow.
FAQs
Did you find this article useful?
