Best AI Image Generation API in 2026: GPT-5.4 Image vs Nano Banana, Tested

We sent the same eight prompts to four image models — OpenAI's GPT-5.4 Image 2 and Google's Nano Banana 1, 2 and Pro — through a single API, and generated 32 images. No cherry-picking: every output is shown as it came back. The headline surprise: GPT-5.4 Image 2 took ~200 seconds per image, 8–18× slower than the Nano Banana line, at the highest price. Here's what each model actually produced.

TL;DR — the verdict

• Best overall: Nano Banana Pro (Gemini 3 Pro Image) — cleanest text, strongest prompt adherence, ~24s, $0.137/image.
• Best value: Nano Banana 1 (Gemini 2.5 Flash Image) — genuinely usable output at $0.039 and ~11s, the fastest of the four.
• Strong but slow: GPT-5.4 Image 2 — competitive quality, but ~200s per image and $0.225 make it impractical for batch work.
• Awkward middle: Nano Banana 2 — fine, but Pro is only $0.07 more and clearly better, while NB1 is far cheaper.

How we tested (real images, one API)

On June 12, 2026 we generated every image in this article through a single OpenAI-compatible endpoint, switching models with one parameter. Eight prompts, each targeting a real production need and a known failure mode: English sign text, dense multi-line poster text, white-background e-commerce, a portrait with visible hands, a multi-subject scene with left/right spatial control, an infographic with labels, a flat vector illustration, and CJK (Chinese) text rendering. Each model got one shot per prompt — no re-rolls, no prompt tuning per model — so what you see is first-attempt behavior. All four models are listed with live per-image prices on the DataLLM Lab pricing page.

Cost & speed: the numbers that decide it

The speed column is the story. Nano Banana models return in 11–24 seconds; GPT-5.4 Image 2 averaged just over three minutes per image. For a one-off hero image that may not matter — for a batch of 100 product shots it's the difference between minutes and hours. Prices are per generated image at the resolutions returned; check the pricing page for the current rates.

Test 1 — English sign text

Prompt: "A neon storefront sign that clearly reads 'OPEN 24 HOURS', mounted on a brick wall at night, photorealistic."

All four spelled the text correctly. Nano Banana Pro produced the most atmospheric scene (wet street, depth, accurate neon glow); GPT-5.4 Image 2 nailed the lettering in a tighter square crop; Nano Banana 1 was clean but slightly rougher on the "24". Text rendering, once a universal weakness, is essentially solved at the top of this lineup.

Test 2 — Multi-line poster text

Prompt: "A vertical marketing poster with three lines of bold text reading exactly 'SUMMER SALE', 'Up to 50% OFF', 'This Weekend Only'."

All four spelled every line correctly — "SUMMER SALE", "Up to 50% OFF" and "This Weekend Only" all intact, which would have been unthinkable two years ago. The split is stylistic: GPT-5.4 Image 2 produced the boldest, most ad-ready layout; Nano Banana Pro went clean navy-on-white; Nano Banana 2 was tidy; Nano Banana 1 was a touch flatter but perfectly usable. Dense multi-line typography is no longer a differentiator.

Test 3 — E-commerce product shot

Prompt: "Professional e-commerce product photo of a single white running sneaker, centered on a pure white seamless background, soft studio lighting."

Clean white-background product shots from everyone. Nano Banana Pro gave the crispest cut-out with a subtle contact shadow that reads "studio." GPT-5.4 Image 2 produced the most photorealistic shoe but added a faint grey gradient instead of pure white — you'd need to mask it. Nano Banana 1 and 2 delivered flatter but genuinely catalog-usable images. For bulk e-commerce, NB1's $0.039 makes it the obvious workhorse.

Test 4 — Portrait & hands

Prompt: "Photorealistic portrait of a barista holding a cappuccino with both hands clearly visible, warm cafe lighting, natural skin texture."

Hands — the classic AI tell — came out clean on all four. No extra fingers, no melted thumbs gripping the cup. Nano Banana Pro and GPT-5.4 Image 2 were the most photorealistic (natural skin texture, believable depth of field), with NB Pro even adding latte art. Nano Banana 1 and 2 were a little more "stock photo" but still convincing. The hands problem is, for practical purposes, over.

Test 5 — Multi-subject & spatial control

Prompt: "An orange tabby cat on the LEFT and a golden retriever on the RIGHT, both wearing matching red scarves, on a wooden park bench."

This was meant to be the trap — left/right placement plus a per-subject attribute (matching red scarves). All four passed: orange cat on the left, golden retriever on the right, both wearing red scarves. Nano Banana Pro and GPT-5.4 Image 2 went further and honored the "park bench" detail with the most coherent composition; Nano Banana 1 nailed the subjects but skipped the bench. Spatial + attribute binding, once a reliable failure, is now broadly handled.

Test 6 — Infographic with labels

Prompt: "A clean flowchart with three boxes labeled 'Collect', 'Analyze', 'Report', connected by arrows, flat design."

A clean sweep — every model produced a correct three-box flowchart with "Collect → Analyze → Report" and proper arrows. Nano Banana 1 and 2 used soft blue fills; Nano Banana Pro went outline-only with uppercase labels; GPT-5.4 Image 2 produced the most balanced spacing. None hallucinated extra boxes or misspelled a label. Simple diagrams with embedded text are now safe to generate rather than hand-build.

Test 7 — Flat vector illustration

Prompt: "A flat isometric vector illustration of a developer at a desk with a laptop and two monitors, blue and orange palette."

Here the models diverge on style interpretation. Nano Banana 2 and Pro stayed closest to the brief's "flat isometric vector" — clean shapes, the requested blue-and-orange palette, startup-illustration feel. GPT-5.4 Image 2 drifted toward a richer, semi-painterly render — lovely, but less "flat vector" than asked. Nano Banana 1 was on-style but busier. If you need assets that match an existing flat illustration system, the Google models followed the instruction more literally.

Test 8 — CJK (Chinese) text rendering

Prompt: "A Chinese tea shop storefront at dusk with a wooden sign that reads '春茶上市', warm lighting, photorealistic."

The most revealing test. GPT-5.4 Image 2 rendered the cleanest, most legible "春茶上市" — large, correct, crisp characters on a red banner. Nano Banana 2 also got all four characters right; Nano Banana Pro produced an atmospheric scene with mostly-correct (slightly soft) characters; Nano Banana 1 garbled one of the characters — the budget model's clearest weakness in this whole suite. If your product touches Chinese, Japanese or Korean text, this is the one place GPT-5.4 Image 2's slowness might be worth tolerating, or step up to Nano Banana Pro.

Which one should you use?

Call any of them from one endpoint

Every image in this article came from the same API — only the model string changed. On DataLLM Lab that's one OpenAI-compatible endpoint for all four (and the rest of the catalog):

# Same request — switch model string to switch image engine
curl https://api.datallmlab.com/v1/chat/completions \
  -H "Authorization: Bearer $DATALLMLAB_API_KEY" \
  -d '{
    "model": "google/gemini-3-pro-image-preview",  # Nano Banana Pro
    "modalities": ["image", "text"],
    "messages": [{"role": "user", "content": "A neon sign that reads OPEN 24 HOURS..."}]
  }'

Model IDs and live per-image prices for every image model are on the pricing page.

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