If you need several blocks of legible text in one image, a packaging label, a UI mockup, a labeled diagram, reach for GPT Image 2. If you need one short, beautifully set headline or a logo-with-text, Ideogram V3 is the specialist. There's no single best model anymore, and that's the actual news in 2026.
Text used to be the embarrassing failure of every image model. Melted letters, invented characters, "PRESMIUM CFOFEE" on a coffee bag. That problem is largely solved now, but it got solved unevenly. The models that nail a single bold word on a poster are not always the ones that hold five separate labels steady across a busy infographic. So the useful question isn't "which model is best at text," it's "which model is best at the kind of text I'm generating."
Quick answer: match the model to the text job
- One short headline or tagline (a poster, an ad banner, a social graphic): Ideogram V3 or FLUX.2. Both set short Latin text cleanly and cheaply.
- Dense, multi-element text (UI screens, infographics, packaging with several copy blocks, labeled diagrams): GPT Image 2. It holds the most separate text regions without one of them sliding into nonsense.
- Poster or logo-with-text, design-forward layout: Ideogram V3. It was built for typography and treats text as a first-class design element, not an afterthought.
- Clean marketing graphic with correct spelling: Imagen 4 or Imagen 4 Ultra. Reliable spelling, professional kerning, photoreal-friendly.
- Text sitting inside a photoreal scene (a sign in a street shot, a label on a real-looking bottle), where the photo matters more than the words: Nano Banana 2 or Seedream 4. Great scene, decent text, just keep the word count low.
- Non-Latin or CJK scripts: GPT Image 2 first, Seedream 4 second. Everything else is still shaky here.
The comparison at a glance
| Model | Best text for | Spelling accuracy | Dense multi-element text | Max resolution | Rough fal price/image | Watch out for |
|---|---|---|---|---|---|---|
| GPT Image 2 | UI, diagrams, packaging, multilingual | Top tier | Best in class | Up to 4096px | ~$0.03 medium, ~$0.13 high (tiered) | Slower; price climbs fast at high quality / 4K |
| Ideogram V3 | Posters, logos-with-text, short headlines | Very high on short text | Good, not its focus | Up to ~2048px | ~$0.04-0.08 by tier | Long paragraphs less reliable than GPT Image 2 |
| Imagen 4 / 4 Ultra | Marketing graphics, signage, captions | Very high | Good | ~2K (up to 4K on some tiers) | ~$0.05 (Imagen 4), ~$0.06 (Ultra) | Fewer edit/mask controls than GPT Image 2 |
| FLUX.2 | Short headlines, UI mockups, hex-exact brand color | High on short text | Decent | Multi-megapixel | ~$0.03 at 1024px ($0.03/MP, pro) | Long/dense copy can still drift |
| Nano Banana 2 | Text inside a photoreal scene | Good | Garbles past ~3-5 text blocks | 1K / 2K / 4K | ~$0.08 (2K = 1.5x, 4K = 2x) | Not for copy-heavy layouts |
| Seedream 4 | Bilingual/CJK, 4K, matching sets | Good, strong on CJK in cases | Solid | Native 4K, up to 4096px | ~$0.03 | People can skew glossy; verify long strings |
Prices and specs are pulled from fal.ai's model listings, OpenAI's GPT Image 2 materials, Ideogram, and Google's Imagen 4 launch posts, confirmed in mid-2026. fal changes hosted pricing often, and several of these models charge by megapixel or quality tier rather than a flat per-image rate, so check the live page before you budget a big run. GPT Image 2 in particular ranges from under a cent at low quality to roughly forty cents at 4K high.
Why text is genuinely hard for image models
Diffusion models learned to draw text the way they learned to draw everything else: as shapes. They never saw a character set or a font file. They saw millions of pictures that happened to contain letters, and they learned the visual texture of "wordiness," the spacing, the contrast, the rough silhouette of a sentence. That's why early models produced text that looked right from across the room and fell apart up close. It was a convincing impression of writing, not actual writing.
Three things make it harder than it sounds:
Letters are precise where pixels are forgiving. A face can be off by a few percent and still read as a face. Swap one stroke on a "B" and you get an "R," or garbage. Text has almost no error tolerance, so the model has to be far more exact than it does anywhere else in the frame.
Errors compound with element count. One word is a coin flip the model usually wins now. But each separate text region is another independent chance to fail. A packaging mock with a brand name, a flavor, a weight, an ingredient line, and a barcode caption is five rolls of the dice. This is exactly where the models split: GPT Image 2 stays coherent across many regions, while a scene-first model like Nano Banana 2 tends to keep the first few blocks clean and then start inventing characters in the rest.
Non-Latin scripts are the deep end. CJK characters, Arabic, Devanagari, and others have larger glyph sets, contextual shaping, and far less clean training data than English. Most models that look great in English produce believable-but-wrong characters in these scripts. GPT Image 2 is the most reliable here, and Seedream 4, built by ByteDance, does better than average on Chinese in particular, but "proofread by a native reader" is still mandatory.
Per-model verdicts
GPT Image 2. The one to default to when text accuracy is the whole point. It renders dense layouts, signage, headline copy, UI labels, and multilingual scripts more reliably than anything else, and it outputs up to 4096px. The bigger deal for production is its edit precision: you can mask a single region and regenerate just that, swapping a background or fixing one label, while leaving the rest of the frame pixel-stable. Best for: infographics, app screens, packaging with multiple copy blocks, anything multilingual. Not for: fast, cheap throwaway drafts; high-quality 4K runs get expensive. Our GPT Image 2 guide goes deeper on the edit workflow.
Ideogram V3. The text specialist, and it shows. Its typography engine handles kerning, alignment, and multiple font styles within a single image, and Magic Prompt quietly expands a short prompt into a layout-aware one. For a poster, a logo lockup, an album cover, or a single hero headline, it often beats GPT Image 2 on sheer typographic taste. Best for: short, design-led text; logos-with-text; posters. Not for: long paragraphs or dense multi-field layouts, where it's good but not the leader.
Imagen 4 / 4 Ultra. Google's models render clean, correctly spelled text with natural kerning that integrates well into photoreal scenes, brand names, product labels, storefront signage, captions. Ultra is the higher-fidelity tier. They're a strong, safe choice for marketing graphics. Best for: polished marketing visuals where spelling has to be right. Not for: heavy region-level editing; the mask-and-regenerate control is thinner than GPT Image 2's.
FLUX.2. A real jump over earlier FLUX on text. It renders crisp short headlines for UI mockups, posters, and marketing material, and it'll honor exact brand hex codes, which is genuinely useful. It's fast and priced by megapixel, so cheap at 1024px. Best for: short headlines on a budget, brand-color-exact work. Not for: long copy, where it can still drift. See our GPT Image 2 vs FLUX head-to-head, which covers text directly.
Nano Banana 2 (Gemini 3.1 Flash Image). Excellent at photoreal scenes and good at text when the text is secondary. Put one sign or a short label in a beautiful street scene and it shines. Push past roughly three to five separate text elements and it starts to garble. Best for: text living inside a photo, low word count. Not for: copy-heavy layouts. Read the Nano Banana 2 guide and the Nano Banana 2 vs GPT Image 2 comparison, where text is one of the clearest splits.
Seedream 4. ByteDance's model folds generation and editing together, outputs native 4K, and holds text better than most while staying cheap. It's notably stronger than average on Chinese text. Best for: bilingual or CJK work, 4K output, matching image sets at low cost. Not for: portraits where its glossy lean shows; long English strings still need a proofread. Full breakdown in the Seedream 4 guide.
How we'd actually make a text-critical image
Say the job is a product ad: a real-looking bottle on a kitchen counter, with a brand name and a tagline that has to be spelled correctly. Here's the workflow that survives contact with reality.
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Decide what carries the weight. If the words are the point (a poster, a UI, a diagram), generate the whole thing on a text-strong model. GPT Image 2 for dense or multilingual text, Ideogram V3 for a single styled headline. Skip the compositing dance entirely.
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If the photo is the point, split the layers. Generate the photoreal scene on the model that makes the best image, even if its text is mediocre, then handle the text-bearing layer separately on a text-strong model. You get the realism of one model and the typography of another.
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Proof the spelling before anything else. Read every word out loud, zoom to 100%, and have a native speaker check non-Latin text. Models are confident and wrong. This step is non-negotiable.
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Fix text in place with a mask edit, don't reroll the whole image. When one label is wrong, mask just that region in GPT Image 2 and regenerate it. The rest of the frame stays pixel-stable, so you don't lose the composition you liked while chasing a fixed word. This is the single biggest time-saver and the reason GPT Image 2 earns its place in a text workflow even when another model made the base image.
The friction in steps 2 and 4 is that they usually mean two or three different tools and a round-trip through an editor. A multi-model canvas removes that. In Masonry, you can generate the photoreal base on one model, drop a text layer from a text-strong model onto the same canvas, and run the mask-and-fix edit, without exporting and re-importing between apps. The point isn't any one model. It's putting the right model on each layer in one place. If packaging is your use case specifically, our AI product photography tools roundup covers the label-and-bottle side in more depth.
FAQ
Which AI image model is best at text? For dense or multilingual text, GPT Image 2. For short, design-led typography and logos-with-text, Ideogram V3. Imagen 4, FLUX.2, and Seedream 4 are all solid on short Latin text. There's no universal winner, which is the honest answer.
Why do AI images still misspell words? Because most image models learned text as visual shapes, not as a character set. They reproduce the look of writing rather than spelling words from a font. That's why a single short word usually comes out right now, while a paragraph or a busy layout still drifts, every extra text region is another chance to fail.
Can AI render logos with text? Yes, and Ideogram V3 is the strongest for it. It handles kerning, alignment, and font styling well enough for a clean wordmark. Just confirm the spelling and exact letterforms at full size, and expect to do a mask edit (GPT Image 2) to correct any single character that comes out wrong.
What's the best model for non-English or CJK text? GPT Image 2 is the most reliable across scripts, with Seedream 4 a strong second and notably good on Chinese. Even so, treat all non-Latin output as a draft and have a native reader proof it, the models will produce characters that look plausible and mean nothing.
