GPT Image 2 versus Nano Banana 2 for legible type
When the still must contain readable words, pick the model that actually renders them. Pretty texture is the wrong test.
If the still has to contain words a stranger can read, texture is the wrong bake-off. A creamy bottle, a cinematic sky, a face that would win an arena vote — none of that tells you whether "SUMMER SALE" is spelled, kerned, and still a sentence at thumbnail size. For this job you pick the model that renders the string. Then you read the string aloud at 100 percent zoom.
This is not a general GPT Image vs Nano Banana review. GPT Image 1 is the family deep-dive (the 2-line is the current generate/edit pair). Nano Banana Pro is the Google image deep-dive. Text to image is where you run both. Here the only test is type-in-image.
The job is the quoted string
Type-in-image means the words are in the pixels, not overlaid later. Headline on a poster, price on a tag, three words on a YouTube thumbnail, a label on a bottle, a quote card. If you can composite the type in Figma and should, do that — legal lines, tiny UI, licensed brand fonts. This router is for the cases where the type has to live in the scene: perspective on a shop sign, print on a garment, a poster that is the image.
Quote the exact text in the prompt. Unquoted copy gets paraphrased. Then look at three things, in order, and ignore everything else until they pass:
- Spelling. The string is the string. Near-correct ("SUMMMER") is a fail.
- Hierarchy. Headline, subhead, and small line are different sizes, not three textures of the same weight.
- Survival at the real size. Thumbnail, Story, pack-shot crop. If you have to zoom to know what it says, it does not say it.
Pretty grain, "AI look," even identity lock — later. Those are different jobs.
Who actually renders the words
Both families were built to treat text as something other than denoising leftovers. They still fail differently.
GPT Image 2 is the catalog's text-to-image leader as of the current ranking snapshot, and the model's whole pitch is prompt adherence plus world knowledge. For type, that shows up as: it follows a layout instruction ("headline top third, price lower left") more often than a diffusion-only engine, and it will put a real brand of object in the scene without a 400-word visual essay. It is billed by resolution, with a 4K tier. Use it when the words have to sit inside a reasoned scene — a named object, a specific composition, a sign that is part of a room.
Nano Banana 2 (Gemini 3.1 Flash Image) is the daily edit model: natural-language changes, identity that survives a jacket swap, up to 14 references. Type is competent, not the reason you open it. Nano Banana Pro (Gemini 3 Pro Image) is the family member whose headline claim is studio-quality text, including non-Latin scripts, at up to 4K. If the still is a poster, a recipe card, or a multilingual graphic and you are already in the Banana family, Pro is the type rung; 2 is the "also spell this while you edit the person" rung.
Do not run this test on Lite. Lite is the cheap from-scratch tier with no edit path. A type job that needs a second pass — and type jobs do — does not belong there.
A fourth shelf exists and is not this comparison: Ideogram for short display lettering as a design object, Seedream 5.0 Pro for dense boards and multilingual strings in a layout. If the still is a poster with a grid, you may already be past GPT vs Banana. Stay here when the still is a picture that contains words, not a document that happens to be an image.
The decision rule
The words are part of a scene — neon in a window, a tote, a thumbnail with a face and three words, a product with a short callout in the environment. Start GPT Image 2 and Nano Banana Pro (or 2, if you already have a reference face you must keep) on the same quoted string. Keep the one you can read. Do not keep the one with better skin.
The words are the design — quote card, event graphic, price-forward poster. Banana Pro, or leave this pair for Ideogram / Seedream. If GPT Image 2 already spells and hierarchies, ship it. If it spells but looks like a photograph of a poster, you wanted a layout model.
You will edit the words after. Banana 2: "change the headline to X, keep everything else." GPT Image 2 also edits. Do not regenerate the whole still to fix one letter.
More than one script. Test Banana Pro first. A native reader still has to sign off. Do not ship CJK or Arabic from an English-only eyeball test.
Quote the string, one text element per clause, cap the word count, "no extra lettering on props." Past about six words, expect retries. Past a paragraph, overlay real type.
Do not pick a winner from an arena still that has no words in it. Do not treat Banana 2's edit path as a type model. Two engines, same quoted string, read aloud. If both fail, the prompt is a paragraph or you are in layout-model territory.
FAQ
Which one spells better in English right now?
Neither is a finished typesetter, and snapshots move. GPT Image 2's advantage is following a layout instruction inside a scene. Nano Banana Pro's advantage is type as a studio output, including other scripts. Run the quoted string on both. Keep the one you can read at the size you will ship. That bake-off is cheaper than a generic "which image model is best" argument.
Can I use Nano Banana 2 instead of Pro if the type is only three words?
Yes, if the three words pass the read-aloud test on 2 and you needed 2 anyway for a reference or an edit. No, if the still is a type-forward graphic and 2's lettering is the part you are fighting. Three words is not a reason to skip the test. It is a reason the test should pass on the first try.
Should I overlay the text instead of generating it?
Yes, whenever the type must be a licensed font, a legal line, or dense body copy. Generate the plate with a clean region ("empty upper third, no lettering"). Composite real type. Generate in-pixel type when it has to occupy the scene — perspective, material, a poster that is the photograph. Hybrid is normal. Pride in "the model did the words" is not a brief.
Does winning text-to-image Elo mean GPT Image 2 wins type?
No. Elo is a blind preference on whole images. A still can win on lighting and lose on the one word the campaign needed. Type-in-image is a separate test you run with a quoted string. Use the rank as a hint to include GPT Image 2 in that test, not as a substitute for reading the output.