You asked for a poster that says GRAND OPENING and got one that says GRNAD OPENIING, in a typeface that does not exist, with a fourth letter that appears to be melting. Then you tried again, and burned twenty generations on six words.
The short version: Ideogram is still the specialist for text inside an image, Adobe Firefly is the best of the big models if you need commercial safety alongside legible type, and DALL·E handles short phrases reliably. Flux renders type better than anything that shipped two years ago. And the answer most working designers actually use — the one no image-model marketing page will give you — is to generate the image without text and set the type in a real editor.
Why AI image models cannot spell
Understanding what goes wrong explains which models fixed it and which never will.
A diffusion model does not draw letters. It starts with noise and iteratively denoises toward something matching your prompt, producing pixels — not glyphs, not characters. Nothing in the architecture knows that an E is a specific object with three horizontal arms that must all be present. It knows that images captioned "sign" tend to contain certain high-contrast shapes.
Three things compound the problem:
Tokenization works against letterforms. The text encoder reading your prompt does not see individual characters. It sees sub-word tokens — "OPENING" might arrive as one chunk. The model gets no reliable signal about the sequence of characters it must reproduce, which is exactly the information required to spell.
Text is high-frequency, low-tolerance detail. A tree with one branch wrong still reads as a tree. A word with one letter wrong reads as a mistake, instantly, to everyone. Diffusion models are tuned to produce plausible textures; human eyes are unforgiving spell-checkers. Almost no other object in an image has this property.
Training captions rarely transcribed the text. For years, an image of a shop sign was captioned "a storefront" — not "a storefront with a red sign reading GRAND OPENING." The model never learned the mapping between requested strings and rendered characters because the supervision was not there.
What changed was mostly boring engineering: captions that transcribe visible text, larger text encoders that preserve prompt structure, and higher-resolution latents so a 12-pixel-tall letter has room to exist. Newer models spell better for those reasons. Older ones never will, however you prompt them.
Which models actually handle text
Ideogram — the specialist
Ideogram was built around this problem, and it shows. Reach for it when the text is the image: logos, posters, memes, book covers, packaging mockups. It handles longer strings than most competitors and tends to place them where a designer would, rather than across the subject's face.
- Who should use it: anyone whose brief is "make an image with these exact words on it."
- Who should not: anyone chasing photographic realism or a specific painterly style. Ideogram's aesthetic range is narrower than the best of the field, and you will notice.
- Honest trade-off: a model that is excellent at one hard thing and merely fine at everything else.
Adobe Firefly — legible type, plus indemnification
Firefly is trained on Adobe Stock and licensed content, which is the entire pitch: Adobe has offered enterprise indemnification around its generative features, and that matters if the output is going on a billboard. Text rendering is solid rather than spectacular, and it lives inside Photoshop and Express — where the type gets finalised anyway.
- Who should use it: marketing teams, agencies, anyone whose legal department has opinions.
- Who should not: hobbyists and concept artists. You are paying for provenance you do not need.
- Honest trade-off: the safest output, not the most exciting. Firefly's images are often the most corporate in a lineup, and that is sometimes exactly right.
DALL·E — reliable at short phrases
DALL·E's strength was always prompt comprehension rather than raw beauty, and that carries over to text. Short phrases come out clean, and because it is reachable inside ChatGPT, you can iterate conversationally: "same image, fix the second word." That loop beats re-prompting from scratch.
- Who should use it: people already paying for ChatGPT who need a sign or label to be readable.
- Who should not: anyone needing a paragraph, dense typography, or control over a typeface.
- Honest trade-off: convenient and accurate for short strings; a lower aesthetic ceiling than Midjourney's.
Midjourney — got much better, still not a typesetter
Midjourney spent its early life producing the most beautiful images and the most illegible text. Recent versions render short text far more reliably. It is still not where you go when the words must be exactly right, and iterating on a single word is tedious.
- Who should use it: anyone who wants the best-looking image and can add the text afterwards.
- Who should not: anyone with a client who will proofread the render.
Flux — the open-weight surprise
Flux, from Black Forest Labs, changed expectations for what an open-weight model can do with type. It sits behind several third-party interfaces and self-hosted setups, and renders text far better than the Stable Diffusion generation before it. If you run your own pipeline, this is the credible option.
Leonardo — control over consistency
Leonardo is less a text specialist than a workflow: model choice, fine-tuning, and consistent characters and styles across a set. Text quality depends on the underlying model you pick. Worth knowing if you are producing a series rather than a one-off.
Side by side
| Tool | Text rendering | Best for | Watch out for |
|---|---|---|---|
| Ideogram | Strongest, including longer strings | Posters, logos, packaging, memes | Narrower aesthetic range than rivals |
| Adobe Firefly | Reliable, plus commercial safety | Brand and marketing work | Safe-looking output; subscription tied to Adobe |
| DALL·E | Accurate on short phrases | Quick signs and labels inside ChatGPT | Struggles with dense or long text |
| Midjourney | Much improved, still not exact | Best-looking image, text added later | Iterating on one word is painful |
| Flux | Strong for an open-weight model | Self-hosted and custom pipelines | You assemble the surrounding workflow |
Swipe the table sideways to see every column →
The workflow professionals actually use
Nobody shipping paid work generates final typography with a diffusion model. The sequence that wastes the least time:
- Generate the image with a hole in it. Prompt for the scene and ask for empty space where the text belongs: "a clean matte poster with a large empty band across the upper third." Leave the words out of the prompt entirely.
- Set the type in a real editor. Canva, Figma, Photoshop — whatever you have. You get the exact font, the exact kerning, the exact hex colour, and a typo you can fix in four seconds instead of forty generations.
- *Let the model render text only when it must be part of the world*** — a weathered shop sign, a t-shirt in the scene, a neon tube. That is where an editor cannot easily fake the lighting and perspective, and where Ideogram earns its place.
- Keep it short. Accuracy falls off a cliff as the string gets longer. Three words is a good bet. A sentence is a gamble. A paragraph is a joke.
- Quote the string, and use capitals. Prompting a sign reading "OPEN LATE" improves the odds across most models, and capital letterforms are simpler and more consistently represented than lowercase.
If you need text and a layout, a presentation tool such as Gamma will get you there faster than any image model, because it treats text as text.
Which one should you actually open?
If the words are the point, open Ideogram. If a legal team will see the output, open Firefly. If you are already in ChatGPT and need a legible label, use DALL·E and stop shopping. If you want the most beautiful image on the page, use Midjourney and set the type yourself — the right answer more often than the previous three combined.
Browse the AI image tools category for the full set, or AI design tools if the job is layout rather than generation. Designers should see our designer use-case guide, and our best AI image generators roundup covers the wider field. Found a model that spells better? Submit it.
FAQ
Which AI image generator is best for text?
Ideogram, if the text is the reason you are generating the image. It was designed around typography and handles longer strings and better placement than general-purpose rivals. Adobe Firefly and DALL·E are dependable for short phrases, and Midjourney has improved but still should not be trusted with words a client will proofread.
Why does AI still spell words wrong in images?
Because diffusion models generate pixels, not characters. Nothing in the architecture represents a letter as a discrete symbol that must appear intact, and the prompt is read as sub-word tokens rather than a sequence of characters. Models improved when training captions began transcribing visible text and text encoders got large enough to preserve prompt structure — but the underlying process is still drawing shapes that resemble letters, not typesetting.
Can I generate a poster with AI and use it commercially?
It depends on the tool's terms and your jurisdiction, and this is a genuine risk area rather than a formality. Adobe Firefly is the mainstream option that leans hardest on licensed training data and commercial indemnification for business customers. Check the current terms of whichever model you use — and see our guide to AI images you can use commercially.
How do I get exact text in an AI image?
Do not. Generate the image with deliberate empty space, then set the type in an editor where you control the font and can fix a typo instantly. Reserve model-rendered text for words that must sit inside the scene — a sign, a label, a shirt — with the perspective and lighting to match.
Is Ideogram free?
Ideogram has offered a free tier with a capped number of generations alongside paid plans, though the specifics move around; check current pricing before committing. If a free tier is the constraint, our free tools list beats a trial that expires mid-project.