GPT Image 2 Prompting Guide: Tips & Examples [2026]

Why Prompting GPT Image 2 Is Different

GPT Image 2 plans before it paints. Its reasoning layer reads your prompt, resolves layout and text placement, and then renders — which means it rewards specificity in ways older diffusion models didn't. Vague prompts waste that capability; precise, structured prompts unlock near-perfect text and complex, multi-element compositions. This guide is the pattern library. For cost, see the pricing breakdown; for the feature overview, the API guide.

Pattern 1 — Quote the Text Exactly

GPT Image 2's headline strength is legible text. Put the exact words in quotes and specify the typographic intent:

A minimalist café sign reading "MORNING RITUAL" in a clean geometric sans-serif, warm cream background, thin espresso-brown underline, centered.

Naming the font style, weight, and placement gets you readable, correctly-spelled text — the thing every other model historically failed. This holds across 10+ languages; state the language if it's non-English.

Pattern 2 — Describe Layout Like a Brief

The reasoning layer can hold multiple constrained elements at once. Write it like a design brief, not a vibe:

A SaaS pricing card, dark theme, gradient border. Three tiers side by side: "Basic — $9/mo", "Pro — $29/mo", "Enterprise — Custom". Each tier has a green checkmark next to three feature lines. Header reads "Choose your plan". Balanced spacing, rule-of-thirds.

Spell out relationships ("next to", "below", "three side by side"). GPT Image 2 pre-computes these; older models would smear them.

Pattern 3 — Set the Scene, Then the Camera

For photographic output, order matters: subject → environment → lighting → lens/camera → mood.

A ceramic pour-over coffee maker on a walnut counter, soft morning light from a window at left, shallow depth of field, 50mm look, gentle steam rising, warm and calm.

Pattern 4 — Edit With Instructions, Not New Prompts

GPT Image 2 does high-fidelity, instruction-based editing: send the original image plus a natural-language change and it preserves everything you didn't mention.

Replace the background with a clean white studio, add a soft shadow beneath the product, keep the label text unchanged.

Note the cost: reference/edit inputs bill at high input fidelity you can't disable, so batch related edits and avoid needless round-trips.

Pattern 5 — Guide With Reference Images

Pass reference images to lock a style, product, or character, and tell the model what to take from each: "match the color palette of image 1, the product shape of image 2." Three to five well-chosen references beat sixteen mixed ones — references compete for influence.

Pattern 6 — Draft Cheap, Finish Precise

Iterate composition on a low-quality/low-res setting (a fraction of a cent per image), lock the winning prompt, then render the final at high quality. This keeps expensive tiers off your iteration loop — the same draft-then-finish discipline that controls spend in the cost-reduction guide.

Copy-Ready Starter

curl -X POST https://api.linkmodel.ai/api/v1/image-generation \
  -H "Authorization: Bearer $LINKMODEL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-image-2",
    "prompt": "Event poster. Headline \"NIGHT MARKET\" in bold condensed sans, subhead \"Fri 8PM · Riverside\". Deep indigo-to-magenta gradient, small lantern icons along the bottom, centered, high contrast, legible text."
  }'

Common Mistakes

Being vague about text. If you don't quote it, expect approximations. Quote it.
Stacking too many references. Fewer, cleaner references win.
Iterating on the high tier. Draft cheap; finish expensive.
Fighting the grade in post. Name the lighting and mood up front — the model plans for it.

Next Steps

Compare GPT Image 2's approach against Google's grounded model in Nano Banana Pro vs GPT Image 2, or browse the whole field in best AI image generation APIs.

Test these prompts free with a $1 credit, or try them visually in the Playground first.