Text inside a generated image is the last widely broken thing in image generation. Most models produce letterforms that look like writing and are not: a plausible alphabet, wrong words, invented characters, and spelling that changes between attempts. A handful of 2026 models handle it well enough to ship, and the cost difference between them is large.
The honest headline: on 8frame, Nano Banana Pro at 21 credits is the model to reach for when the words must be right. Everything else is cheaper and less reliable with type.
TL;DR
- Nano Banana Pro, 21 credits: the pick when text has to be correct. Three to four times the price of the volume models
- Seedream 5 at 6 credits, Seedream 5 Pro at 7: good general image models, unreliable with longer text
- FLUX.2 [pro] at 5, Imagen 4 Fast at 4: volume tiers, treat text as decorative
- The free workaround that always works: generate the plate without text, add type in the edit. Exact control, zero credits
- Short beats long. One or two words land far more often than a sentence
The cost of correct type
Canon 8frame prices, a credit is $0.01 at pack rate:
| Model | Credits | Text reliability |
|---|---|---|
| Imagen 4 Fast | 4 | Decorative only |
| FLUX.2 [pro] | 5 | Short words sometimes |
| Seedream 4 | 5 | Decorative only |
| Seedream 5 | 6 | Short words sometimes |
| Seedream 5 Pro | 7 | Better, still not dependable |
| Imagen 4 | 8 | Short words sometimes |
| Nano Banana Classic | 6 | Decorative only |
| Nano Banana 2 | 11 | Reasonable on short text |
| Nano Banana Pro | 21 | The reliable option |
| Imagen 4 Ultra | 12 | Better than Fast, not a type model |
| Flux Edit (FLUX.2 pro) | 7 | For correcting an existing image |
| Kontext Max | 13 | Stronger editing, including text fixes |
Reliability labels here are our own working assessment from production use, not a benchmark. The ordering has been stable; treat the specifics as guidance and test your own strings.
Why models struggle with words
A diffusion model learns what text looks like, not what it says. It has seen millions of images containing letters and has learned the visual statistics of typography: stroke weights, spacing, how words sit on a sign. What it has not learned is that a specific sequence of glyphs is the only correct one. So it generates something with the texture of writing.
Models that do handle text well generally have stronger text conditioning and were trained with that as an explicit objective. That capability costs compute, which is why the model that gets type right is the expensive one.
What makes type succeed or fail
Length. One word lands often. Three words land sometimes. A sentence almost never survives intact, even on a good model.
Prominence. Large text on a clean surface works. Small text, text at an angle, text on a curved or textured surface, and text in the background all degrade.
Language and character set. Latin script is best served. Cyrillic, Arabic, and CJK are materially worse across the board, and non-Latin text is the case where the plate-plus-type route is not optional.
Specificity in the prompt. Naming the text in quotes and stating where it sits helps. Asking for "a sign" gets you a sign with nonsense on it.
Attempt count. Even on a reliable model, generate three and pick the one that is correct. At 21 credits that is 63 credits to be sure, about $0.63.
The workflow that sidesteps the whole problem
For anything that must be exactly right, this is faster, cheaper and better:
- Generate the plate with no text at all. Describe the scene and explicitly leave the area where type will go visually quiet. Seedream 5 at 6 credits.
- Add the type in your editor. Your real font, your real kerning, your exact words, and a version you can change later without regenerating.
- If it needs to look printed onto a surface, generate the plate with a blank sign, poster or label in frame, then composite the type onto it with a blend mode.
This costs 6 credits instead of 21, gives you exact brand typography rather than an approximation of it, and produces an editable asset. For anything with a logo, a price, a URL or a legal line, it is the only defensible route.
When to pay for Nano Banana Pro instead
- Text integrated into the scene in a way compositing cannot fake: reflected, wrapped around a curve, lit by the scene.
- Speed over control, when you need twenty social cards today and small imperfections are acceptable.
- Exploration, when you are finding out what a composition with words in it should look like before building it properly.
The check before you ship
Read the text out loud from the image. Not from your prompt, from the image. Generated type fails in ways your brain autocorrects when you already know what it should say, which is exactly why people ship images with a misspelled product name.
FAQ
Which AI image model is best at text? On 8frame, Nano Banana Pro at 21 credits. Test your specific strings, since results vary by length and language.
Can I fix text in an image I already generated? Sometimes. Kontext Max at 13 credits or Flux Edit at 7 can correct type, with mixed results. Regenerating or compositing is usually cleaner.
Why is non-Latin text so much worse? Less representation in training data. For Cyrillic, Arabic or CJK, generate the plate and set the type yourself.
Generate the plate, set the type yourself. The 8frame canvas is free and unlimited, and generation is paid from $19/month.