What is the best AI image model for readable text in images?

In our FLORA test, GPT Image 2.5 Flare, Ideogram 4.5 and Nano Banana 2.1 all spelled a five-line cafe menu correctly. GPT Image 2.5 Flare kept every line exactly as written for $0.055, so it is the one to start with. FLORA runs all three on one canvas, so you can check the spelling side by side before you pick.

At a glance

Key features

• Side-by-side spelling checks: Send one prompt to several text-capable models in a single call and compare them on one canvas.
• Exact-text prompts: Quoted text and line breaks in the prompt reach the model unchanged.
• Reference edits: Fix one wrong word with an image-to-image edit instead of a full rerun.
• Dollar quotes: FLORA prices every run in dollars before it starts.

Best for

• Posters, menus and signage mockups
• Social posts and ads with a headline in the image
• Packaging and label concepts
• Presentation and pitch visuals with short captions

The options

1. FLORA

   A visual AI canvas with the main text-capable image models in one place. Run the same text prompt through several of them, keep the one that spells and lays it out right, and fix a stray letter with an edit.

   Best for: Checking text accuracy across models before you commit, then editing the winner

   Pros

   • GPT Image 2.5, Ideogram 4.5, Nano Banana 2.1, Recraft V4 and more from one account, so no single model's quirks decide the result.
   • One call sent our menu prompt to three models for $0.227 in total; all three came back letter-perfect.
   • A wrong word is fixed with an image-to-image edit (about $0.064 on GPT Image 2.5) instead of a full regeneration.

   Cons

   • Even letter-perfect models wrap lines their own way, so state the layout in the prompt and proofread before print.

2. GPT Image 2.5 Flare

   OpenAI's image model and FLORA's default image model. OpenAI's GPT Image models also power image creation in ChatGPT. In our test it reproduced all five menu lines with prices aligned on the right, for $0.055 in 35.3 seconds. Model id in FLORA: t2i-gpt-image-2-5-flare.

   Best for: Text that must follow the exact line breaks and layout you wrote

   Pros

   • Matched our line breaks exactly and set the prices in a column.
   • Edits image-to-image with the result as the reference, about $0.064 per edit in FLORA.

   Cons

   • Not the fastest: 35.3 seconds against 28.3 for Nano Banana 2.1 in our run.

3. Ideogram 4.5

   Ideogram's text-to-image model, from a company known for typography; Ideogram also runs its models in its own app and API. In our test it spelled every line correctly at 1728 x 2304 for $0.12 in 32.2 seconds. Model id in FLORA: t2i-ideogram-4-5-t2i.

   Best for: Posters, packaging and type-led graphics where the lettering is the design

   Pros

   • Letter-perfect with a larger, more designed title in our test.
   • The highest native size of the three in our run, 1728 x 2304.
   • Ideogram's own app adds Magic Fill, Extend and Remix, plus team plans (as of October 2026).

   Cons

   • It cost about twice as much as the other two in our run ($0.12).
   • On Ideogram's own free plan, generations are public.

4. Nano Banana 2.1

   Google's image model, available in the Gemini app, the Gemini API and FLORA, with optional web search and thinking levels. In our test it spelled every line correctly for $0.052 in 28.3 seconds. Model id in FLORA: t2i-elote-gateway.

   Best for: Fast, low-cost text images and edits at volume

   Pros

   • The fastest and cheapest of the three in our run.
   • Letter-perfect on all five lines.

   Cons

   • Wrapped two lines onto extra rows, which could push a price onto the wrong row on a busier menu.
   • Images made in the Gemini app carry a visible watermark plus SynthID.

5. Recraft V4

   Recraft's image model, available in the Recraft app and in FLORA. Recraft's app focuses on design work: vector and SVG output, mockups and brand styles.

   Best for: Logos, icons and type set inside vector illustration

   Pros

   • Recraft's app exports editable vector and SVG files.
   • Shared custom styles for brand consistency, plus an API and a hosted MCP server.

   Cons

   • Not part of our menu test, so we can't report its spelling on the same prompt.
   • On Recraft's free plan, Recraft owns the images and they can't be used commercially.

What happened when we tested text rendering in FLORA?

On October 16, 2026, we sent one prompt to three models in a single flora_create_generations call, all at 3:4: "Photo of a chalkboard menu hanging on a white tiled wall in a small bakery cafe, soft morning daylight. The board reads, in neat hand-lettered chalk, exactly:" followed by five lines: MORNING MENU / Oat flat white 4.50 / Cardamom bun 3.75 / Rye toast, cultured butter 6.00 / Open 7am to 2pm.

Settings: t2i-gpt-image-2-5-flare at quality high and 1K; t2i-ideogram-4-5-t2i at quality high with magic prompt off; t2i-elote-gateway (Nano Banana 2.1) at 1K. We read all three with one flora_list_generations call. Costs: $0.055, $0.12 and $0.052, for $0.227 in total. Times: 35.3, 32.2 and 28.3 seconds.

Every word and price came back right in all three. The differences were layout. GPT Image 2.5 Flare matched the line breaks exactly and aligned the prices in a column. Ideogram 4.5 did the same, with a larger title. Nano Banana 2.1 split "MORNING MENU" and "Rye toast, cultured butter" across two rows each.

What went wrong: nothing in the spelling, but the Nano Banana 2.1 wrap would push a price onto the wrong row on a busier menu. The fix is to state the layout too, such as "each item and its price on one line", or to keep the GPT Image 2.5 result. On longer text blocks, check every word anyway: one wrong letter is enough to sink a print run.

Models

GPT Image 2.5 Flare, Ideogram 4.5, Nano Banana 2.1

Tools called

flora_create_generations, flora_list_generations

Cost

$0.227 for 3 images

Spelling errors

0 of 5 lines in all three

How do I write a prompt that gets the text right?

Give the model the exact words and where they go.

• Say "reads exactly:" and then give the text.
• Put each line of text on its own line in the prompt.
• State the layout: alignment, which line is biggest, what sits on one line.
• Keep it short. Five lines worked cleanly; a paragraph is harder for any model.
• Name the lettering style, such as hand-lettered chalk or a simple serif.

What if one word comes back wrong?

Edit, don't regenerate. Ask for an image-to-image edit with the result as the reference and a note such as "change Cardamon to Cardamom, keep everything else". Each GPT Image 2.5 edit costs about $0.064, and the rest of the image stays put.

Should text go in the image or be added later?

For mockups, concepts and social posts, in-image text is fast and now reliable on short copy. For final packaging or legal copy, set the type in a design tool on top of a clean image, which you can ask FLORA for with "no text" in the prompt.

Which AI image model should you pick for readable text?

On short text, the top models are all accurate now; layout, size and cost separate them. Running them side by side in FLORA is the quickest way to see which one fits the job.

• Not sure which model fits, or need several looks: FLORA, with all of them on one canvas.
• Exact line breaks and layout: GPT Image 2.5 Flare.
• Type-led posters and packaging at a larger native size: Ideogram 4.5.
• Fast, cheap text images at volume: Nano Banana 2.1.
• Text inside a logo or vector illustration: Recraft V4.

Frequently asked questions