AI image models are finally reliable at depicting things. They are still unreliable at typing them. Get the text wrong and your poster, ad, or infographic is unusable — even if the visual is perfect.
This is the production guide for getting readable text in AI-generated images: the prompt structure that works, the typography language the models understand, the per-model differences, the failure modes, and the workflow for shipping text-heavy work without manual fixes.
Why text-in-image is hard
Image models are trained on a vast distribution of images. Most of those images do not have crisp, readable, well-typeset text. The training signal for "what the letter 'A' looks like" comes from millions of photos where the 'A' is at an angle, in shadow, in motion, or partly occluded. The model has a vague concept of "A" but no precise glyph model.
GPT Image 2 changed this with a typography-specific training pass — the model now has a near-perfect glyph model for short text. But the rest of the field still has the legacy "approximate letterforms" problem.
The fix is not "use a different model." The fix is prompt structure that constrains the text + typography language the model understands + a model picked for the use case.
The 5 prompt patterns that work
These work across GPT Image 2, Nano Banana 2, Seedream 5.0 Lite, and Grok Imagine. The patterns are the same; the model preference differs.
Pattern 1: Quoted text in quotes, explicit in the prompt
The single most important rule. Put the literal text in quotes. The model needs to see exactly what you want it to render.
Vague (breaks): "A poster that says summer sale 50% off"
Structured (works):
A poster with the headline "SUMMER SALE" in large bold serif type at
the top, and "50% OFF" in slightly smaller text directly below it.
The quotes tell the model: this is literal text, not a description of text. The model knows to render the exact characters.
On GPT Image 2 specifically: this is the most reliable pattern. GPT Image 2 has near-perfect text rendering on short text in quotes.
On Nano Banana 2 specifically: quotes work, and the model is also good at "Text: 'SUMMER SALE'" format.
On Seedream 5.0 Lite specifically: the reasoning model interprets quoted text well. Specifying "literal text" in the prompt helps.
On Grok Imagine specifically: quoted text is the most reliable format here too, but Grok Imagine is weaker on long text and on dense layouts.
Pattern 2: Specify the font, size, weight, and color
The model does not know what "nice font" means. It does know what "Helvetica Neue Bold 96pt" looks like. Be specific.
Vague (breaks): "A poster with stylish text"
Structured (works):
A poster with the headline "NEW ARRIVALS" in Helvetica Neue Bold,
96pt, deep navy (#1A2A4A), centered at the top, with 24pt of
letter-spacing for emphasis.
The font name, weight, size, color, and spacing are all named. The model has no room to guess.
The font names the model actually knows:
- Helvetica / Helvetica Neue / Arial (sans-serif workhorse)
- Times New Roman / Garamond / Georgia (serif)
- Courier / Monaco / Roboto Mono (monospace)
- Futura / Avenir / Gotham (modern sans)
- Playfair Display / Didot (high-end serif)
- Comic Sans (the model recognizes it, that's the only recommendation)
On GPT Image 2 specifically: named fonts work reliably. Use them.
On Nano Banana 2 specifically: the model handles named fonts but may struggle on very specific weights. "Bold" or "Regular" is more reliable than "Black" or "Hairline."
On Seedream 5.0 Lite specifically: the reasoning model interprets font names well. Specify the style ("modern sans-serif" or "high-end serif") as a backup.
Pattern 3: Specify the text layout and hierarchy
The model needs to know where the text sits, how big each piece is, and what the visual hierarchy is.
Vague (breaks): "A poster with a headline and a subhead"
Structured (works):
A poster with the following text layout:
- Headline: "FRESH COFFEE" — Helvetica Bold, 80pt, white, top third, left-aligned
- Subhead: "Open Daily 7am-7pm" — Helvetica Regular, 32pt, white, directly below headline
- Footer: "123 Main Street" — Helvetica Regular, 18pt, white, bottom right
- Hierarchy: Headline > Subhead > Footer (size and weight difference)
The layout is named in detail. The model knows where each piece of text sits, how it relates to the others, and what the visual emphasis is.
On GPT Image 2 specifically: the official cookbook pattern. Use this structure.
On Nano Banana 2 specifically: the model is good at this. Specify the text zones ("title zone: top third," "body zone: middle third").
On Seedream 5.0 Lite specifically: the reasoning model benefits from explicit "Layout:" sections.
Pattern 4: Use "text zones" with surrounding visual context
For complex layouts (infographics, magazine spreads, signage with multiple text elements), the model does better when you name the text zones explicitly.
Vague (breaks): "A magazine spread with article text"
Structured (works):
A two-page magazine spread with the following text zones:
- Top of left page: large headline "URBAN RENEWAL" in bold sans-serif
- Below headline: 200-word article in justified body text
- Right page: pull quote in italic, large size, set apart from body
- Bottom of right page: caption "Photo: Jane Doe, 2026"
- White background, professional editorial layout
The model is told where each text element is. This is the difference between a usable magazine spread and a "looks like text but isn't readable" output.
On GPT Image 2 specifically: handles 4-6 text zones per image well. Beyond 6, accuracy drops.
On Nano Banana 2 specifically: handles 3-5 text zones well. Specify the zones explicitly.
On Seedream 5.0 Lite specifically: strong on text zones with explicit "Layout:" sections.
On Grok Imagine specifically: 2-3 text zones is the practical limit.
Pattern 5: Use "text rendering" trigger words
Some trigger words engage the model's typography mode. Use them when you need extra reliability.
- "legible text" — engages the legibility mode
- "crisp typography" — engages the typography mode
- "readable from a distance" — engages the high-contrast mode
- "editorial layout" — engages the editorial typography mode
- "professional print quality" — engages the high-fidelity typography mode
- "sharp letterforms" — engages the glyph mode
Adding "legible text in quotes" or "crisp typography" to the prompt is a small lift that often improves the result.
On GPT Image 2 specifically: the trigger words work as a backup if quoted text is not enough.
On Nano Banana 2 specifically: the model is good at typography by default; trigger words are less needed.
On Seedream 5.0 Lite specifically: the model handles typography well in 1-5 word chunks. Trigger words help for longer passages.
The model pick: which model for which text job
| Use case | Best model | Why |
|---|---|---|
| Poster with 1-3 word headline | GPT Image 2 | 99% text accuracy on short text |
| Magazine / editorial layout with body text | GPT Image 2 | Best at dense text layouts, multilingual support |
| Multilingual signage (e.g. "Café" in 6 languages) | GPT Image 2 | 48+ language support, strongest glyph model |
| Infographic with labels and values | GPT Image 2 | Best at structured text in layouts |
| Logo concept (text + symbol) | GPT Image 2 or Seedream 5.0 Lite | Both handle logo-style text well; GPT Image 2 for English, Seedream for stylized |
| Quick social graphic with one phrase | Nano Banana 2 | Fast, accurate on short text |
| Marketing visual with stylized typography | Seedream 5.0 Lite | Strong on artistic typography |
| Large display text in a scene (signage) | GPT Image 2 | Most reliable for in-scene text rendering |
The pattern: GPT Image 2 is the default for any text-heavy work. The other models are the choice when you need a specific other strength (Nano Banana 2 for speed, Seedream for stylistic typography, Grok Imagine for consumer-grade speed).
The common failure modes
1. Text is in the prompt but not in quotes. The model interprets the words as description, not literal text. Always put literal text in quotes.
2. Font is not specified. The model picks a default font that does not match your brand or the design. Name the font.
3. Layout is vague. "A poster with text" gives the model no spatial guidance. Specify zones (top/bottom/left/right, sized relative to the image).
4. Text is too long. Models have a "legible text" cutoff. GPT Image 2 holds up at ~12 words. Nano Banana 2 at ~6 words. Seedream 5.0 Lite at ~5 words. Beyond that, accuracy drops. Break long text into multiple images or composites.
5. Text and visual conflict. A headline in white over a light background. The model has to pick. Specify the contrast ("white text on dark background" — name the contrast).
6. Text size is too small for the model. "Tiny text" produces unreadable text. Specify the size in pt or pixels, and the relation to the image ("headline 80pt on a 1080x1080 image is ~9% of height").
7. The model picks a font that does not exist. Some model outputs include "fonts" that look made-up. Verify the font is real, or specify the style ("modern sans-serif") as a backup.
8. The model adds extra text. "A poster with a headline" produces a poster with the headline AND a fake subhead AND fake body copy. Specify the negative: "no extra text, no fake body copy, no invented subhead."
9. The model renders the text correctly but the layout is wrong. The font is right, the size is right, but the text is centered when you wanted it left-aligned. Specify the alignment.
10. The model renders the text at the wrong color. "Bold red" produces a different red than you expected. Specify the hex code ("#C8102E" for a specific red).
The text-in-image pre-flight checklist
Before you ship a text-heavy AI image:
- All literal text is in quotes in the prompt.
- Font family is named (Helvetica, Times, Garamond, etc.).
- Weight is specified (Bold, Regular, Light, Black).
- Size is specified (80pt, 32px, 9% of image height).
- Color is specified (white, deep navy, hex code).
- Layout zones are explicit (top third, centered, left-aligned).
- Hierarchy is named (Headline > Subhead > Footer).
- Negative rules are included (no extra text, no fake body copy).
- Text length is within the model's reliable range (5-12 words depending on model).
- Contrast is specified (white on dark, black on light, named colors).
Skip any of these and the text is likely to come out wrong.
The iteration loop when text drifts
Even with a perfect prompt, text often drifts on the first pass. The fix is structured iteration.
Step 1: Identify what drifted. Look at the output. Common drifts:
- Wrong characters (e.g., "COFFEE" rendered as "COFFEL")
- Right characters, wrong font
- Right characters, wrong size
- Right characters, wrong position
- Extra text added that wasn't requested
Step 2: Name the drift in the next prompt. "In the previous output, the headline said 'COFFEL' instead of 'COFFEE'. The new prompt must render 'COFFEE' exactly."
Step 3: Add constraints to the next prompt. "The text 'COFFEE' must be spelled exactly as shown, with two E's. The font must be Helvetica Bold, not a substitute. The size must be 80pt, not smaller."
Step 4: Iterate. Pass 1, 2, 3. Most text issues resolve by pass 3-4. If a model cannot get the text right after 4 passes, switch models (GPT Image 2 is the most reliable).
Do not re-roll from scratch. The convergence loop is faster.
The text-only fail-safe: render text separately and composite
For the most demanding text work — exact brand fonts, exact letter spacing, exact alignment — render the text in a design tool (Figma, Photoshop, Canva) and composite it over the AI-generated background. The model generates the visual; the design tool handles the text.
When to use this:
- Logos that need exact brand typography
- Infographics with precise numerical data
- Posters with exact brand fonts
- Multilingual layouts where letterforms matter
- Anything that will be printed at large size
Workflow:
- Generate the visual with the AI model (no text in the prompt)
- Render the text in a design tool at the exact size, font, color
- Composite the text over the visual
- Verify on the target platform
This is the only way to get pixel-perfect text. The AI model's text is "close enough for digital use"; for print, brand, or precision work, composite.
The summary
Readable text in AI images requires:
- Quoted literal text in the prompt (the single most important rule)
- Named font, weight, size, color (not "nice font")
- Explicit text zones (top third, centered, etc.)
- A model picked for the use case (GPT Image 2 is the default for text-heavy work)
- Iteration when the first pass drifts (4 passes is the typical convergence)
- Compositing for pixel-perfect text (for brand, print, precision)
The model is not the problem. The prompt is underspecified. Add the structure, the typography language, and the iteration loop, and "AI image with readable text" becomes a reliable production workflow.
