You generate a poster, a t-shirt design, a sign in a scene, or a logo concept, and the text comes back as nonsense: OPEM, RESPCT, DESEPTION — letters that look the right shape but are not real words. Or the middle letters fuse, the kerning is off, or the font shifts mid-word.
Fastest fix (June 2026): regenerate the same prompt in GPT Image 2 (inside ChatGPT) or Ideogram V3. Both now spell short text correctly on the first try ~90-99% of the time, so a model swap alone usually solves it. If the text must be exact (logo, legal, brand), skip the model entirely and add the text in Figma or Photoshop afterward — that is the only 100% reliable path.
Older general-purpose models (Midjourney, SDXL checkpoints, the retired DALL-E 3) still spell text correctly only about 30-40% of the time, which is why most “garbled text” reports trace back to model choice.
Which bucket are you in?
| Symptom | Most likely cause | Go to |
|---|---|---|
| First/last letters right, middle mangled | Subword tokenization | Step 1 + Step 2 |
| Short word fine, long phrase drifts | Length accumulation | Step 1 |
| Word is correct, font/style is wrong | Vague style prompt | Step 3 |
| Multiple signs, all bad | Text elements competing | Step 4 |
| Text must match a brand/logo exactly | Wrong tool for the job | Step 5 + Step 6 |
| Chinese / Japanese / Arabic comes out wrong | Non-Latin script | Step 2 (use GPT Image 2 or Nano Banana 2) |
Common causes
Ordered by what most often produces unreadable output.
1. Model tokenization splits the text
Most image models pass your prompt through a text encoder that tokenizes by subword units, not by character. The word RESPECT may be tokenized as RES+PECT, and CLIP-based encoders cap at 77 tokens, so the model never sees clean letter-by-letter information. It guesses how the chunks should look and gets the middle letters wrong. Newer typography models (GPT Image 2, Ideogram V3, Imagen 4) were specifically trained to bridge this gap, which is why they spell far better.
How to spot it: Compare your intended word to the output. Are the first and last letters usually correct, with the middle mangled? That is the tokenization symptom.
2. Long phrases drift across characters
The longer the text string, the more letters drift. A 4-letter word almost always works; a 20-character phrase almost always has errors. The error accumulates with length.
How to spot it: Count the letters. Under 8 characters and a strong model handles it; over ~15 characters and you need a different strategy (split the lines or add the text in post).
3. Stylized fonts confuse the model
“Gothic blackletter,” “graffiti tag,” “neon script,” “handwritten cursive” — these push the model into low-data territory where letter shapes vary widely in training, so the model is less confident about specific glyphs. Note the difference between style and content: the model may spell the word right but render the wrong font, or vice versa.
4. Multiple text elements in one image
A storefront sign + a chalkboard menu + a price tag — each text element competes for the model’s text-rendering capacity. Quality drops on all of them at once.
5. Text in unusual placements
Text wrapped around a curved object, in deep perspective, or reflected in a window or mirror requires the model to deform letter shapes coherently, which it rarely does well.
6. Wrong model for text-in-image
This is the single most common cause. Older SDXL checkpoints, anime-focused models, and most stylized checkpoints are very weak at text. Midjourney still lands only ~30-40% text accuracy on short phrases as of mid-2026. DALL-E 2 and 3 were retired by OpenAI on May 12, 2026 and replaced by GPT Image 2. The strongest text-in-image models as of June 2026 are GPT Image 2, Ideogram V3, Imagen 4, Nano Banana 2, and Recraft V4.
7. Text in a non-Latin script
Chinese, Japanese, Arabic, Cyrillic, and similar scripts used to be near-hopeless. As of June 2026 this has improved: GPT Image 2 renders Chinese, Japanese, Korean, Hindi, Bengali, and Arabic at roughly 90% character accuracy, and Nano Banana 2 supports multilingual rendering and localization. Older and stylized models still fail on these scripts, so the fix is almost always “switch to GPT Image 2 or Nano Banana 2,” not “give up and post-edit.”
Before you change anything
- Save the prompt, the model name, and the broken-text output.
- Decide: is the exact text critical (logo, legal, brand), or is the visual concept enough (background sign, atmospheric detail)?
- If exact text is critical, plan for either a strong text model or a post-edit pass.
- If the text must match a specific brand font, AI generation is the wrong tool for the final asset.
- Back up the current prompt template before editing it.
Information to collect
- Full prompt and the intended text string.
- The model and tier used to generate the broken output.
- 3-4 broken outputs so you can see the failure pattern.
- The intended use case (poster, logo concept, scene detail).
Shortest path to fix
Step 1: Cut the text to 5 characters or fewer
The single biggest reliability gain. Short single words work far more reliably than phrases:
OPENinstead ofOPEN FOR BUSINESS2026instead ofLIMITED EDITION 2026SALEinstead ofBIG SUMMER SALE
If the use case allows it, ship the short version. If you need a full phrase, break it into separate short lines in the prompt (for example Line 1: "GRAND" / Line 2: "OPENING") rather than one long string.
Step 2: Switch to a text-strong model
In rough order of text-rendering quality as of June 2026:
| Model | Maker | Notes |
|---|---|---|
| GPT Image 2 | OpenAI | ~99% character accuracy claimed; “thinks before drawing”; multilingual (CJK, Hindi, Arabic, Bengali). In ChatGPT for Plus/Team/Enterprise; replaced DALL-E 3. |
| Ideogram V3 | Ideogram | Purpose-built for typography; ~90-95% accuracy on short phrases; best for clean single-word and multi-line layouts. |
| Imagen 4 | Strong English text; GA in the Gemini API and AI Studio; Fast/Standard/Ultra tiers. | |
| Nano Banana 2 | Gemini 3.1 Flash Image; ~95% accuracy on 1-4 word text; default in the Gemini app; multilingual + can localize text. | |
| Recraft V4 | Recraft | Design-grade text; good for branding/marketing layouts. |
| Flux 1.1 Pro | Black Forest Labs | Solid text, far better than older Flux Dev; strong if you are already on a Flux workflow. |
For non-Latin scripts, start with GPT Image 2 or Nano Banana 2. For open-source CJK rendering, Qwen Image (Alibaba) is purpose-built for long Chinese and English text.
Step 3: Quote the exact text and specify the font
Most strong text models look for quoted text and respond well to explicit font descriptions:
A vintage neon sign that reads "OPEN", bold sans-serif, glowing red on a brick wallWithout quotes, the model is more likely to treat open as a concept than as literal text. If the word is right but the font is wrong, add concrete font traits (“bold white sans-serif on dark background”) instead of vague words like “nice text.”
Step 4: Generate the background separately, type the text yourself
The most reliable workflow whenever exact text matters:
- Generate the image without the text (or with placeholder text).
- Open it in Figma, Canva, Photoshop, or Affinity.
- Set type with a real font.
- Place it where the AI text would have gone.
This takes about 60 seconds and is 100% reliable.
Step 5: For logo / brand text, never generate
A real logo needs vector output, exact kerning, and brand-color compliance. AI raster output satisfies none of these. Use AI for logo concepts only; produce the final logo in Figma or Illustrator.
Step 6: For stylized text, run image-to-image from real text
- Type the exact text in Photoshop or Figma using a normal font.
- Export at 1024x1024.
- Use it as image-to-image input with denoise
0.3-0.4and a stylization prompt:graffiti spray-paint style, neon glow on brick wall.
This anchors letter shapes to a known-good text image and stylizes around them — far better than pure text-to-image for stylized text.
Step 7: For unfixable cases, inpaint the text region only
Generate the rest of the image first. Mask the text region (Photoshop generative fill, or SDXL/Flux inpaint). Provide the exact text as a separate prompt. A couple of inpaint passes can usually nail short text strings.
How to confirm it’s fixed
- The text reads cleanly with no garbled middle letters.
- A second viewer can read the text without any context.
- Every instance of multi-word text is spelled correctly and consistently across the image.
- The font style matches your intent (script, serif, sans).
- Zoom to 100% before approving — text errors that hide at thumbnail size show up at full size.
If it still fails
- Drop to the shortest possible text (one word, ideally 3-4 letters).
- Switch to the most text-strong model on your list (GPT Image 2 or Ideogram V3 first).
- Generate the background separately and place text in post — the 100% reliable path for any production use.
- For non-Latin scripts, try GPT Image 2 or Nano Banana 2 before falling back to post-edit.
- Package the prompt, model, intended text, and broken output before asking for community help.
FAQ
Why can’t AI image models spell when chatbots can? Different problem. The text encoder tokenizes your prompt into subword chunks (and CLIP-based ones cap at 77 tokens), so the image model never gets clean letter-by-letter information. It learns letter shapes from training images rather than spelling, then guesses how the chunks connect. Newer models like GPT Image 2 and Ideogram V3 were trained specifically to close that gap.
Which AI image generator is best for text in June 2026? For general use, GPT Image 2 (in ChatGPT) leads with a claimed ~99% character accuracy and multilingual support. Ideogram V3 is the typography specialist at ~90-95% on short phrases. Both beat Midjourney, which still lands around 30-40% on text.
Can AI now generate Chinese or Japanese text correctly? Much better than before. GPT Image 2 renders Chinese, Japanese, Korean, Hindi, Bengali, and Arabic at roughly 90% character accuracy, and Nano Banana 2 supports multilingual rendering and localization. Older and stylized models still fail, so switch models rather than giving up.
Is DALL-E 3 still an option? No. OpenAI retired DALL-E 2 and 3 on May 12, 2026 and replaced them with GPT Image 2, which is integrated into ChatGPT for Plus, Team, and Enterprise users.
How long should the text be for reliable results? Under 8 characters is safe on a strong model; one word of 3-5 letters is most reliable. Phrases over ~15 characters drift — split them into short lines or add them in post.
The word is spelled right but the font looks wrong. What now? That is a style problem, not a spelling one. Replace vague style words with concrete traits (“bold white sans-serif,” “condensed slab serif”) or use Step 6 (image-to-image from real text) to lock the glyphs and stylize around them.
Prevention
- Default text-in-image to “add the text in post” rather than “ask the model for it” whenever the text must be exact.
- Standardize on a text-strong model (GPT Image 2 or Ideogram V3) for any project where text matters.
- Never rely on AI for legal, branded, or trademark-sensitive text — generate without it and add the text in post.
- Keep a short “text-in-image workflow” note with your model choice, prompt structure, and post-edit step.
- Re-check model recommendations every few months; text rendering is improving fast, and the best model has changed twice since early 2025.