GPT Image 2.5

GPT Image 2.5 Generator

OpenAI's image model, in a browser tab. The one to use when the picture has words in it: packaging, app screens, posters and price tags.

GPT Image 2.5 is OpenAI's image model. Its strength is text inside the picture: packaging copy, app screenshots, posters and price tags come back legible more often than on other models. On MakeViral it runs at 1K, 2K or 4K, costs 2 credits at 1K and 2K, and takes 20 to 60 seconds.

What is GPT Image 2.5?

OpenAI's image generation model, one of three options in the AI photo generator. Nothing to install, no API key. Open the photo tool, pick the model, write a prompt, press generate.

CapabilityGPT Image 2.5
ModesText to image, and image to image
Resolutions1K, 2K, 4K
Aspect ratios9:16, 16:9, 1:1, 4:3, 3:4, 3:2, 2:3
Prompt ceiling4,000 characters
Reference images1 on the edit path
Typical generation20 to 60 seconds
Credits2 at 1K, 2 at 2K, 4 at 4K
Images per run1 to 4

Note the pricing row, the most useful fact here: 1K and 2K cost the same 2 credits. Unless you want a smaller file, set 2K and take the extra pixels. 4K doubles the price to 4 credits, so save it for a banner or a print piece.

It is the slowest of the three, at 20 to 60 seconds an image. That is the trade for text quality: worth it on a mockup, wasted on a background.

Why does readable text in an image matter?

Because a large share of commercial images are mostly words on an object.

Packaging. A tube, a bag, a jar or a box with your product name and size on it. If the name is unreadable, the image cannot be used, however good the light is.

App screenshots and device mockups. A phone in a hand showing a header, a number and a few rows.

Posters and flyers. A headline, a date, a location: three blocks of type at three sizes.

Price tags, shelf talkers and menu boards. A few short numbers: the easiest text to get right, and the most obviously broken when it goes wrong.

Book and product covers. A title, an author, a subtitle, in defined positions.

Other models put something text-shaped in a picture. This one more often puts the actual words there. It is still not typesetting, so read every word at full size before you use it.

When the words sit on top of the image rather than inside it, such as a hook line over a slide, generate a clean background on Z-Image for 1 credit and add the text in the AI slideshow generator.

How do you prompt for text that comes out right?

Five habits, in order of how much they help.

  1. Write the exact words, in capitals. Not "a label with the product name". The literal string you want.
  2. Keep it short. A few words per block. Long sentences degrade into scribble faster.
  3. Name the position. "Across the upper third", "on three stacked lines". Without it, everything piles into the middle.
  4. Name the type style, not your font. "Thin uppercase sans serif", "condensed bold", "handwritten chalk". You cannot upload a typeface, so description is your only steering.
  5. Give one curved element at most. Text around a circle is the hardest case. One per image, and keep the rest straight.
Do writeDo not write
the label reading GENTLE FOAM CLEANSERa label with the product name on it
on three stacked lines, largest firstwith some text under it
in a thin uppercase sans serifin our brand font
150ml in small type at the bottomwith the size somewhere
one curved line around the sticker edgetext curved around everything

The prompt ceiling is 4,000 characters with a live counter, so there is room to spell out every block of type and describe the light.

8 GPT Image 2.5 prompts you can paste today

Written for this model. Generate at 2K: it costs the same 2 credits as 1K.

1. Packaging with exact copy "A white squeeze tube of face cleanser on a pale grey plinth, the label reading GENTLE FOAM CLEANSER in thin uppercase sans serif with 150ml in small type below it, soft even studio light, a subtle shadow to the right, 9:16." Why: the exact string in capitals plus a named type style gives it something to copy.

2. A coffee bag with three lines "A kraft coffee bag with a matte black label reading ETHIOPIA GUJI, beneath it WASHED, and beneath that FILTER ROAST on three stacked lines of clean sans serif, on a light oak counter, soft window light from the left, 4:5." Why: stacking named lines beats one long string, which smears together.

3. An app screen in a hand "A phone held upright in one hand showing an app screen: a dark header reading Today, a large white number 4 with the word streaks under it, and three list rows with short labels, crisp edges, blurred cafe behind, 9:16." Why: a screen described as named elements gives a mockup. "An app screenshot" gives mush.

4. An event poster "A printed poster on a concrete wall, headline LATE SHIFT in large condensed uppercase, below it FRIDAY 11PM in medium type, and UNIT 7 in small type at the bottom, high contrast black on warm cream paper, 2:3." Why: three sizes with three exact strings give hierarchy, not decorative scribble.

5. A retail price tag "A supermarket shelf price tag on a white rail under a row of cereal boxes, the tag reading 2 FOR 5 in bold with SAVE 1.40 in a red strip below it, a small barcode block at the bottom, flat store light, 16:9." Why: retail signage is a few short numbers, the case text rendering handles best.

6. A chalkboard menu "A cafe chalkboard on an easel, handwritten chalk lettering reading FLAT WHITE 3.80, CORTADO 3.20 and COLD BREW 4.00 on three lines, a small drawn arrow beside the last item, warm morning light from a window on the left, 4:5." Why: item and price on one named line keeps the two from drifting apart.

7. A book cover "A hardback book upright on a linen surface, the jacket showing THE QUIET HOUR in large serif across the upper third and a small line reading M. RAHIM at the bottom, deep navy cover, soft raking light from the right, 3:4." Why: naming where each block sits stops everything stacking in the center.

8. A sticker sheet "A sheet of die-cut vinyl stickers on a white desk, one large circular sticker reading MADE SLOWLY around its edge, two small rectangular ones reading BATCH 04, backing paper visible, soft overhead light, a slight sheen, 1:1." Why: one curved element, the rest straight, is the safe ratio for text on shapes.

What can one reference image do?

The edit path here takes one image. That is the main structural difference from Nano Banana 2, which takes up to 14.

One reference is enough for:

  • Relighting a product shot you already own
  • Changing the surface or background around an existing object
  • Restyling a photo, for example turning a flat packshot into a lifestyle frame
  • Re-cropping into a different aspect ratio
  • Changing a color or a finish while keeping the shape

One reference is not enough for:

  • Compositing a product and a person from two separate photos
  • Holding one persona's face consistent across a series
  • Matching a style board of several frames

The practical split: if your job starts from one photo, this model is fine and the text quality is a bonus. If it starts from several, go to Nano Banana 2 and come back when a frame needs legible words.

Whatever you generate can become the next reference, since every image has Use as reference alongside Download, Remix and Delete. That gives you a chain even with one slot.

GPT Image 2.5, Nano Banana 2 or Z-Image?

The column that decides it on this page is the first one.

ModelWords inside the imageTime per imageReference imagesCredits at 2K
GPT Image 2.5Strongest of the three, still worth checking20 to 60 s12
Nano Banana 2Approximate, expect misspellings10 to 30 sUp to 143
Z-ImageAvoid text entirely5 to 15 sNoneNot available, 1K only

The verdict: use GPT Image 2.5 whenever the picture contains words a viewer is meant to read. Packaging, screens, posters, tags, covers, signage. It is also the cheapest of the three at 2K, where Nano Banana 2 charges 3 credits.

Switch to Nano Banana 2 when the image needs more than one of your own photos, or when a person is the subject. Switch to Z-Image for scenery, because 1 credit in 5 to 15 seconds is the right price for a background nobody inspects.

None of the three is a typesetter, and all three can misspell a label. The difference is how many attempts a clean one takes.

What does it cost, and what will it not do?

A credit is about $0.10 on the $49.99 plan for 500 credits, and about $0.067 on the $199.99 plan for 3,000 credits.

RunCreditsOn the $49.99 planOn the $199.99 plan
One image at 1K or 2K2about $0.20about $0.13
Four images at 2K8about $0.80about $0.53
One image at 4K4about $0.40about $0.27
Three attempts at one label6about $0.60about $0.40

Budget for the third attempt: text-heavy images fail more often than scenery.

The limits, stated plainly:

  • No background removal on an uploaded photo. No cutout tool, no transparent export.
  • No brand font control. No typeface upload. You describe a style and the model interprets it.
  • No exact color matching. No hex input, so brand color drifts between runs.
  • No guarantee a logo or a label renders correctly, even here. Read every word at full size and regenerate when it is wrong.

MakeViral also does not upload, schedule or post, and there is no API. Plans are on the pricing page. For where these images go next, see the AI product photo generator, the AI influencer generator, the Shopify product photo guide and the TikTok slideshow strategy guide.

Why it works

What the gpt image 2.5 generator does for you

Text that stays readable

Packaging copy, app screens, posters and price tags, with the exact words in the prompt.

2K at the 1K price

Both cost 2 credits here, so generate at 2K and take the extra pixels.

Clean product mockups

Tubes, bags, boxes, covers and devices on plinths, counters and walls.

4,000-character prompts

Room to spell out every block of type, its position and its style.

One reference image

Relight, restyle, re-crop or recolor a photo you already own.

How it works

Three steps, about a minute

No editor, no timeline, no export settings to get wrong.

  1. Open the photo tool on this model

    Pick the aspect chip, then set 2K. It costs the same 2 credits as 1K here.

  2. Write the exact words

    Put the literal strings in capitals, say where each block sits, name the style.

  3. Read every word, then download

    Check the copy at full size. Regenerate when a letter is wrong.

Who it is for

Works in these niches

  • Packaging mockups
  • App and SaaS marketing
  • Posters and flyers
  • Retail and price tags
  • Book and cover art
  • Ecommerce listings
  • Menu and signage
  • Agencies
FAQ

Questions people ask

What is GPT Image 2.5?
OpenAI's image generation model, running in the browser here as one of three options. It makes images from a text prompt or edits one image you upload, at 1K, 2K or 4K, across seven aspect ratios.
Is it really better at text inside images?
It is the strongest of the three here for words on packaging, screens, posters and tags. It is still not typesetting, so it still misspells. Write the exact words in capitals, keep each block short, and check the output every time.
What does one image cost?
2 credits at 1K, 2 at 2K and 4 at 4K. A credit is about $0.10 on the $49.99 plan for 500 credits and about $0.067 on the $199.99 plan for 3,000 credits, so a four-image run at 2K is about 80 cents.
Should I generate at 1K or 2K?
2K, unless you specifically want a smaller file. Both cost 2 credits on this model, so 1K gives you fewer pixels for the same price. Move to 4K only for banners and print, where the price doubles to 4 credits.
How many reference images can I upload?
One. That is enough to relight, restyle, recolor or re-crop a photo you already own. For a composite that needs a product photo and a person photo together, use Nano Banana 2, which takes 14.
Can I use my brand font?
No. There is no typeface upload and no font control anywhere in the tool. You describe the style instead, such as thin uppercase sans serif, and the model interprets it. For exact type, add the copy in a design tool afterward.
Why is it slower than the other models?
It typically takes 20 to 60 seconds, against 10 to 30 for Nano Banana 2 and 5 to 15 for Z-Image. That is the trade for text quality: time well spent on a mockup, wasted on a background nobody inspects.
What if the label comes out misspelled?
Regenerate. Shorten the string, put it in capitals, and name its position more precisely. Two or three attempts per text-heavy frame is normal, about 60 cents on the $49.99 plan. Cancel anytime, with a 14-day refund window.

Generate the mockup with the words on it

1K and 2K both cost 2 credits, so generate at 2K. That is about 20 cents an image on the $49.99 plan for 500 credits.

Cancel anytime. 14-day refund window on unused credits.