Which AI Image Generator Is Best for Text?
We tested 5 tools with the same 4 prompts. First result only, no cherry-picking.
Result:
- Google Gemini: Best overall — 3/4 correct on first try
- DALL-E 3: Solid for short text — 2/4 correct
- Nano Banana Studio: Best for short-to-medium text — 2/4 correct, with the closest near-misses
- Midjourney v6: Struggles beyond 2 words — 1/4 correct
- Stable Diffusion XL: Not usable for text — 0/4 correct
No tool gets long text fully right yet. Full results below.
I expected all five to fail.
One of them didn't. Not perfectly — but enough that I had to zoom in and double-check the text was actually correct.
I'll show you exactly which one.
I spent an afternoon running the exact same text prompts through five different AI image generators. Not because I had nothing better to do — because I was trying to make a simple Coffee Shop sign for a friend's mockup and got "COFFE SHPO" four times in a row. From different tools.
I wanted to know: is any AI actually good at this? So I set up a fair test. Same prompts, same rules, no cherry-picking.
The Test
I picked four prompts. Each one has specific text that needs to render correctly. I ran each prompt through all five tools, took the first result (no cherry-picking, no regenerating), and wrote down exactly what came out.
The prompts:
- Coffee Shop sign —
A storefront sign that says "Coffee Shop" in bold letters - Grand Opening banner —
A store banner that says "Grand Opening Sale" in large text - Wedding invitation —
An elegant wedding invitation with the names "Emily and Christopher Montgomery" - Newspaper headline —
A newspaper front page with the headline "UNPRECEDENTED DISCOVERY IN MEDITERRANEAN"
Short text, medium text, long text, and names. The four scenarios where you'd actually need AI-generated text to be correct.
The tools:
- Midjourney v6
- DALL-E 3
- Stable Diffusion XL
- Google Gemini (image generation)
- Nano Banana Studio (built on Gemini)
One run each. First result only. No do-overs.
Try the Same Prompts Yourself
Before you read the results — try the Coffee Shop prompt and see what you get:
Type "Coffee Shop" → generate →
Now compare to what I got below.
Results
Prompt 1: "Coffee Shop" Sign
Two words. Six letters and ten letters. This should be easy.
| Tool | What it rendered | Correct? |
|---|---|---|
| Midjourney v6 | "COFFEE SHOP" | Yes |
| DALL-E 3 | "COFFEE SHOP" | Yes |
| Stable Diffusion XL | "COFFE SHPO" | No |
| Google Gemini | "COFFEE SHOP" | Yes |
| Nano Banana Studio | "COFFEE SHOP" | Yes |
Takeaway: Short, common two-word text is where most modern tools succeed. Stable Diffusion was the outlier — it still scrambles even basic text. The other four got it right, which honestly surprised me. I expected at least one more failure.
Try the exact same prompt: Coffee Shop sign →
But this is the easy test. It gets worse.
Prompt 2: "Grand Opening Sale" Banner
Three words. Slightly longer. Still common English.
| Tool | What it rendered | Correct? |
|---|---|---|
| Midjourney v6 | "GRAND OPENNING SALE" | No — doubled N |
| DALL-E 3 | "GRAND OPENING SALE" | Yes |
| Stable Diffusion XL | "GRND OPNING SLE" | No — dropped letters everywhere |
| Google Gemini | "GRAND OPENING SALE" | Yes |
| Nano Banana Studio | "GRAND OPENING SALE" | Yes |
Takeaway: Going from two words to three words already breaks Midjourney. That doubled N is the kind of error that's close enough to miss at a glance but wrong enough to be embarrassing on a real banner. Stable Diffusion continues to struggle. DALL-E, Gemini, and Nano Banana held up.
Try it yourself: Grand Opening Sale →
Prompt 3: Wedding Invitation — "Emily and Christopher Montgomery"
This is where it gets real. Five words, including a long last name. The kind of text where accuracy isn't optional — you can't put the wrong name on a wedding invitation.
| Tool | What it rendered | Correct? |
|---|---|---|
| Midjourney v6 | "Emily and Christophr Montgomrey" | No — dropped E, swapped letters |
| DALL-E 3 | "Emily and Christopher Montgomary" | No — wrong ending |
| Stable Diffusion XL | "Emly and Cristopher Motgomery" | No — multiple errors |
| Google Gemini | "Emily and Christopher Montgomery" | Yes |
| Nano Banana Studio | "Emily and Christopher Montgomrey" | No — swapped E and R |
Takeaway: This is the test that separates the tools. Only Gemini got it fully correct on the first try. DALL-E was close — one wrong letter in Montgomery. Nano Banana had the same Montgomery issue (the E and R swap is a very common Gemini-family error with that specific name). Midjourney and Stable Diffusion fell apart.
I'll be honest — I ran this one three more times on Nano Banana and got "Montgomery" correct on the second try. But the rules said first result only, so the first result stands.
Try it — see if yours gets the name right: Wedding invitation →
Prompt 4: Newspaper Headline — "UNPRECEDENTED DISCOVERY IN MEDITERRANEAN"
The boss fight. Long words, 5 total, all uppercase. This is the prompt I expected every tool to fail.
| Tool | What it rendered | Correct? |
|---|---|---|
| Midjourney v6 | "UNPRECENDENTED DISCOVREY IN MEDITERANEAN" | No — three errors |
| DALL-E 3 | "UNPRECEDENTED DICOVERY IN MEDITERRANEAN" | No — missed S |
| Stable Diffusion XL | "UNPRECDENTED DISCVERY IN MEDITERANNEN" | No — mangled everything |
| Google Gemini | "UNPRECEDENTED DISCOVEY IN MEDITERRANEAN" | No — dropped R |
| Nano Banana Studio | "UNPRECEDENTED DISCOVERY IN MEDITERANEAN" | No — one R missing |
Takeaway: Nobody aced this one. But the degree of failure was interesting. Nano Banana and DALL-E each had one small error. Gemini had one dropped letter. Midjourney had three distinct mistakes. Stable Diffusion was barely readable.
Long, uncommon words are still the hardest challenge for every AI image generator. But the gap between "one wrong letter" and "three wrong letters" matters when you're choosing a tool.
Good luck with this one: Newspaper headline →
The Scorecard
| Tool | Coffee Shop | Grand Opening | Wedding Names | Newspaper | Score |
|---|---|---|---|---|---|
| Midjourney v6 | ✅ | ❌ | ❌ | ❌ | 1/4 |
| DALL-E 3 | ✅ | ✅ | ❌ | ❌ | 2/4 |
| Stable Diffusion XL | ❌ | ❌ | ❌ | ❌ | 0/4 |
| Google Gemini | ✅ | ✅ | ✅ | ❌ | 3/4 |
| Nano Banana Studio | ✅ | ✅ | ❌ | ❌ | 2/4 |
Nobody got 4/4. The best score was 3/4 (Gemini). The worst was 0/4 (Stable Diffusion).
But here's what the scorecard doesn't show: how close each failure was. There's a big difference between "MEDITERANEAN" (one missing R, you barely notice) and "UNPRECDENTED DISCVERY IN MEDITERANNEN" (what language is that?).
Why This Happens (The Short Version)
I wrote a longer explanation about the technical reasons, but here's the three-sentence version:
AI image generators don't type letters. They generate pixel blobs that statistically resemble text they've seen in training data. The model has no concept of individual characters, no spellchecker, and no way to verify what it produced is actually correct.
This is why:
- Short common words (OPEN, SALE, HELLO) almost always work — the pixel patterns are extremely common in training data
- Long uncommon words (UNPRECEDENTED, MEDITERRANEAN) fail — the model has fewer reference patterns and more characters to get right
- The same prompt gives different errors each time — each generation starts from random noise, so the path to the final image is different every run
It's not a bug. It's the architecture. For a deeper dive into why AI misspells text in images, including why regenerating sometimes fixes it, I wrote a separate piece on that.
What I Actually Learned
1. Short text is solved (mostly). If you need 1–3 common English words, most modern tools will get it right. This wasn't true even a year ago.
2. The 4-word wall is real. Once you cross 3–4 words, reliability drops sharply. Names, unusual words, and long strings all trigger failures.
3. Gemini-family models have an edge. Google Gemini and tools built on it (like Nano Banana) consistently outperformed pure diffusion models on text. The tighter language-to-image integration seems to actually matter.
4. Stable Diffusion is still terrible at text. I like Stable Diffusion for lots of things. Text is not one of them.
5. Consistency matters more than peak performance. I could regenerate any prompt 5 times and cherry-pick a perfect result from most tools. But who wants to do that for every image? The tool that gets it right on the first try saves the most time.
6. "Close enough" is not enough. A wedding invitation that says "Montgomrey" is not usable. A banner that says "OPENNING" is not usable. In text rendering, 95% correct is still wrong.
The Regeneration Test
After the main test, I got curious: how many regenerations does each tool need to get the "Grand Opening Sale" prompt fully correct?
| Tool | Attempts to get correct result |
|---|---|
| Midjourney v6 | 4 tries |
| DALL-E 3 | 1 (got it first try) |
| Stable Diffusion XL | 8 tries (gave up) |
| Google Gemini | 1 (got it first try) |
| Nano Banana Studio | 1 (got it first try) |
For Stable Diffusion, I gave up after 8 attempts. The closest I got was "GRAND OPENING SALE" with "SALE" slightly garbled. Life's too short.
If You Want to Run This Test Yourself
The best way to evaluate any AI image generator for text is to run your own prompts. Not mine — yours. The text you actually need for your project.
Here are the four test prompts I used. Try them and see what you get:
Start with the easy one:
Then try the hard one:
If it gets "Coffee Shop" right but fumbles "UNPRECEDENTED" — that's normal. Every tool does that. The question is how close it gets, and whether you can get a correct result in 2–3 tries.
FAQ
Which AI image generator is best for text?
Based on this test, Google Gemini scored highest (3/4 correct on first try). Tools built on Gemini (like Nano Banana Studio) also performed well. DALL-E 3 was solid for short-to-medium text. Midjourney and Stable Diffusion lagged behind.
Can any AI generate long text correctly?
Not reliably. The newspaper headline prompt ("UNPRECEDENTED DISCOVERY IN MEDITERRANEAN") defeated every tool tested. Long, uncommon words remain the hardest challenge. For text beyond 3–4 words, expect to regenerate multiple times.
Why does AI misspell text differently every time?
Each generation starts from random noise. Different noise = different path through the image generation process = different result. The AI isn't retrieving stored misspellings — it's reconstructing text from scratch each time. More on this: why AI misspells text in images.
How many times should I regenerate to get correct text?
For 1–3 word text, once is usually enough with modern tools. For 4–8 words, expect 2–3 attempts. For 10+ words, you may need 5+ attempts or you may never get a perfect result. The regeneration test results above give specific numbers for each tool.
Is Stable Diffusion good for text in images?
No. In this test, Stable Diffusion XL scored 0/4 on first-try accuracy and couldn't produce correct "Grand Opening Sale" text even after 8 attempts. Use a different tool if text accuracy matters.
Does this mean AI text rendering is useless?
Not at all. Short text (1–3 words) works reliably on most modern tools. Logos, signs, and single-word headlines are fine for production use. The problems start with longer text, unusual words, and names. Knowing the limitations lets you work around them. For practical fixes: how to fix text in AI images.
Test conducted March 2026. All tools used at their latest available versions. Results reflect first-generation output only — no cherry-picking, no prompt engineering tricks. Your results may vary.
