Describe the look of your restaurant, paste the exact dish names, and get menu layouts to react to in minutes. Run the same brief through models that render readable text, proofread every word, and take the result to print or to screen.
A menu is the one document every guest reads, and a misspelled dish name costs you credibility before the food arrives. Image models used to scramble small text, which made them useless for this job. The newer ones can set readable type, so you can draft a whole layout, section headers and dishes included, in the time a design round used to take. Prices and allergens are still yours to get right.
A menu has a strange job. It has to look like your restaurant, stay readable in dim light, and carry dozens of exact words, where one swapped letter in a dish name is visible to every table. Hiring a designer for each seasonal change is slow, and fighting a generic template can eat an afternoon. Text-to-image models now sit between the two: you describe the look, hand over the exact wording, and get several layouts to react to instead of a blank page.
Picasso IA runs 502 models behind one interface, 212 of them text-to-image, so you can send the same menu brief to several engines and keep the one that spelled your dishes correctly. The full catalog is open if you want to browse. New accounts get free credits, which is enough to find out whether a look works before you pay for anything. If your menu also needs pictures of the dishes themselves, that is a separate job covered in AI food photography for menus, and a menu board you want to hang in the window shares a lot with AI poster design.
No single model is best at the whole menu, because a menu is really three jobs: a good-looking page with text on it, editable vector pieces, and the words themselves. Match the model to the job and the rest gets easier.
| Job | Model to try | What it gives you | Watch for |
|---|---|---|---|
| Full menu page as one image | gpt-image-2.5-flare | Follows detailed prompts, renders on-image text, takes reference images | The text is baked into a raster file, so you cannot edit it later |
| Typography-led layout | ideogram-v4-quality | Natural-language or structured JSON prompts, large output sizes | Magic Prompt rewrites your input, so re-read every dish |
| Art-directed page | recraft-v4.1 | Readable on-image text, 14 aspect ratios, considered composition | Long menus work better split into panels |
| Vector borders and ornaments | recraft-v4.1-svg | True SVG output with editable paths | Check any lettering in a vector editor before trusting it |
| Dish descriptions | claude-sonnet-5 | Short draft copy from the ingredients you list | It can invent ingredients you never use |
Two of these have head-to-head breakdowns: Picasso IA vs Ideogram and Picasso IA vs Recraft. If the vector row is the one you care about, AI vector graphics and SVG icons goes deeper on what SVG output is good for.
This is the workflow that ends with a menu you have actually proofread, not a folder of nearly right pages. It assumes you have your dish list and an afternoon.
Spelling is where menu design with AI succeeds or fails, and a few habits move the odds. Generate one section or one panel at a time, because a model that handles six lines cleanly may fall apart at forty. Put every dish name in quotation marks inside the prompt and say where it sits: the title at the top, then the section Starters with exactly these three lines. Short names survive better than long ones, and names with accents, apostrophes or unusual letters deserve extra attention, since those are the first characters to go wrong.
Keep the layout instruction separate from the words. One sentence describes the style, such as cream paper, a single serif typeface, generous margins, a thin border. A second block lists the exact text. If a model drops a letter, regenerate before you start patching, because a new run is usually cheaper than arguing with a bad one. Pages with fewer words in larger type are also easier to read in a dim dining room, which is a design reason and a spelling reason at once.
Tip: treat the generated page like a proof from a printer. Read it against your own text one dish at a time, out loud, starting from the bottom. Reading backward breaks the habit of seeing what you expect to see, which is exactly how a swapped letter in a dish name survives three rounds of review.
Names in more than one language need the same care, plus one more step. Ask a native speaker to read the result, because a model can render a word perfectly and still pick the wrong word.
The first mistake is asking for the entire forty-dish menu in one generation. The more words on the page, the smaller the type and the more errors you should expect. Split the menu into panels or pages and generate each one, so every dish gets a fair chance of coming out right.
The second is letting a model name the dishes. Models invent plausible, delicious-sounding things, and a guest who orders one will ask the kitchen for it. Your dish names, your ingredient lists and your spelling go in; the model only decides how they look. The same goes for house descriptions: a language model will happily mention a sauce you do not make, so read every line against your own recipes.
The third is choosing the mood and forgetting legibility. Pale type over a busy background looks lovely on a laptop and fails in a candlelit room or on a phone screen. Ask for strong contrast and plenty of empty space around the text, and test the page at the size guests will actually see it. And if the menu carries photos, they must stay truthful about the plate that arrives, which is why dish pictures belong to a real food-photography workflow rather than to decoration.
An honest page says where the tool stops working. For menus there are five places:
None of this is a reason to skip the tool. It is the reason the last mile of a menu, the numbers and the allergens, stays with the person who owns the kitchen.
Often, yes, with limits. Models such as gpt-image-2.5-flare and recraft-v4.1 are described as rendering readable on-image text, and short, common words usually come out clean. Long lists, accents and unusual names fail more often. Put each name in quotes, generate one section at a time, and read every line against your own text before anything leaves the building. Treat correct spelling as something you verify, not something you assume.
It depends on which part of the menu you mean. gpt-image-2.5-flare follows detailed prompts and accepts reference images, ideogram-v4-quality takes plain or structured JSON prompts and is worth including in a spelling test, and recraft-v4.1 is built around art-directed composition. Run the same brief through two or three of the 212 text-to-image models and keep whichever one spelled your dishes right.
It can draft them. Paste your dish list and ingredients into a large language model such as claude-sonnet-5 and ask for one short line per dish using only what you list. The risk is invention: a model may mention a sauce, a cut or an origin that is not true for your kitchen. Read every description against your real recipes, and cut what you cannot stand behind.
You can, but plan the file. A generated image is raster, so it can look soft at large sizes, and it carries no bleed or margins. For print, rebuild the page in a layout tool with live text and use the generated image as the look, or generate vector pieces with recraft-v4.1-svg for borders and ornaments. Ask your printer what file format and resolution they need before you export.
No. Type the prices yourself, either in the prompt or in the layout tool afterward, and check every number in the final file. A model does not know your costs or your pricing, and a wrong figure on a printed menu is expensive in a way a wrong letter is not. If the prices change often, keep them in an editable layer so you never regenerate the page for a single number.
Allergen and dietary labels are claims about your kitchen, so they come from you and from your staff, not from a model. Rules about what must be listed differ by country and by region, so check the requirements that apply where you operate, and have a qualified person review the wording. AI can help you lay the information out clearly. It cannot tell you what is in the dish.
Yes, as input. gpt-image-2.5-flare accepts reference images, so you can supply your logo or a photo of your brand colors alongside the prompt. Then inspect the result closely, because a model may redraw a logo slightly wrong. For a mark that must be exact, add the real file in your layout tool after generating the page, and use only logos and colors you own.
Translate the text yourself or with a language model, then generate the page again with the new wording in quotes. Do not ask the image model to translate inside the picture, because you lose control of the exact words. Have a native speaker proofread the result, especially dish names and accents, since correct spelling and the right word are two different checks.
New accounts get free credits, which is enough to test a layout across a couple of models and see whether the spelling holds up. Beyond that, generation costs credits, and the plans are listed on the pricing page, which is the only place worth trusting for current numbers. A sensible trial is one section of your real menu, not a made-up one.
Credits pay for each generation, and the cost depends on the model and the settings you choose, so a figure written here would be out of date quickly. Image generation sits at the lower end of the catalog compared with video or 3D. Check the pricing page for the current plans, and expect to run a few drafts before a layout is good enough to proofread.
Your menu text already exists and only needs a layout. Open the Picasso IA toolkit and see how a first draft reads against your own wording.
Choose your language
