OpenAI launched GPT Image 2 on 2026-04-21. It has been live on Oakgen since 2026-04-24. Google's Nano Banana Pro (NBP) has held the photoreal crown since late 2025. Two obvious questions came out of launch week: which model should you reach for first, and which one should you stop using?
We ran both through the same 20 prompts across 5 dimensions. Short answer up front, then the methodology, the scored matrix, the side-by-side examples, pricing, and a three-branch decision tree at the bottom.
The verdict in one paragraph
GPT Image 2 is the best AI image model for anything involving text, structural layouts, or consistent character grids. It wins decisively on typography (including non-Latin scripts), layout fidelity, and 8-image character coherence. Nano Banana Pro is still the best AI image model for photorealistic skin and portrait subjects — pores, subsurface scattering, skin color variation, and micro-expression nuance. They are effectively tied on generation speed (both ~3s end-to-end) and roughly tied on prompt adherence for non-structural prompts. If you are choosing one for marketing and design work, pick GPT Image 2. If you are choosing one for portraits and fashion photography, pick NBP. If you are on Oakgen, you have both — pick per prompt.
GPT Image 2 leads the LMArena text-to-image leaderboard at 1512 Elo — a 242-point lead. NBP sits second. LMArena Elo in a model's first week is noisy; take the directional signal but not the exact gap. What you see in these 20 prompts is closer to what you'll experience in production.
Methodology
We tested both models on 20 prompts spanning five dimensions, with two prompts per dimension scored below and the other ten used as consistency checks. Each prompt ran three times per model; we scored the best-of-three output to match how a working designer uses these tools.
Dimensions (equal weight in the scored matrix):
- Text rendering — small-point body copy, multilingual signage, layouts with dense typography
- Layout obedience — structural prompts (magazine covers, dashboards, infographics, posters)
- Photoreal skin — portraits, close-ups, skin texture under varied lighting
- Character consistency — same character across a grid of expressions or poses
- Edit fidelity — iterative edits preserving composition (remove object, change color, replace element)
Settings: both models at default quality, no seed pinning, no prompt post-processing. Every generation deducted from a live Oakgen account.
What we did not test: NSFW policies, aspect-ratio extremes (ultra-wide, ultra-tall), and community-fine-tuned derivatives. Those are real axes but they are not how 90% of users interact with either model.
Scored matrix: 10 prompts × 5 dimensions
Scores are out of 10. "Prompt" column is an abbreviated description — full prompts and outputs in the GPT Image 2 Prompt Library.
| Feature | Prompt | Dimension | GPT Image 2 | Nano Banana Pro | Winner |
|---|---|---|---|---|---|
| Magazine cover w/ body copy | Text rendering | 9.5 | 7.2 | GPT Image 2 | |
| Japanese typographic poster | Text rendering | 9.7 | 6.4 | GPT Image 2 | |
| SaaS dashboard mockup | Layout obedience | 9.4 | 7.5 | GPT Image 2 | |
| Infographic w/ 5 labeled arrows | Layout obedience | 9.2 | 7.8 | GPT Image 2 | |
| Editorial portrait, window light | Photoreal skin | 7.8 | 9.4 | Nano Banana Pro | |
| Close-up headshot, studio | Photoreal skin | 7.5 | 9.5 | Nano Banana Pro | |
| 3x3 expressions grid | Character consistency | 9.3 | 7.6 | GPT Image 2 | |
| Same character, 4 poses | Character consistency | 9.0 | 7.9 | GPT Image 2 | |
| Remove watermark, keep comp | Edit fidelity | 8.6 | 8.9 | Nano Banana Pro | |
| Replace background, hold subject | Edit fidelity | 8.4 | 8.8 | Nano Banana Pro |
Totals (summed over 10 prompts): GPT Image 2 = 88.4, Nano Banana Pro = 81.0. GPT Image 2 wins 6 of 10. The win margins are decisive where they go its way (+2 to +3 on text and layout). NBP's wins are narrow (+0.3 to +0.6 on photoreal and edits) but consistent.
Side-by-side: five pairs that define the models
The real information is not in the totals — it is in what the failure modes look like. Five pairs below (with placeholder images for the side-by-sides we are backfilling this week).
1. Magazine cover with 10pt body copy
Prompt summary: Editorial magazine cover, title + byline + four body-text pull-quotes in 10pt serif, centered portrait.
GPT Image 2 renders the 10pt body text as real sentences. NBP renders it as plausible-looking but unreadable pseudo-text that scales like English but does not parse. This gap is the single most visible difference between the two models.
2. Tokyo ramen shop sign, Japanese + English
Prompt summary: Photorealistic storefront with kanji sign, yen prices, mixed English/Japanese signage.
GPT Image 2 renders legible kanji ("一龍軒") and correct yen formatting. NBP renders decorative glyphs that approximate Japanese but fail a native reader's inspection.
3. Editorial portrait, window light
Prompt summary: A woman in her thirties, soft north light, medium-format framing, Kodak Portra 400 palette.
NBP wins. The skin texture — pores at natural scale, subsurface scattering in the highlights, varied tonal transitions — is a real step ahead of GPT Image 2's somewhat plastic portrait output. Both models nail composition; NBP nails the skin.
4. 3×3 expression grid of the same character
Prompt summary: Fox in round glasses, nine expressions in a labeled grid, consistent design.
GPT Image 2 keeps the fox identifiably the same fox across all nine tiles. NBP produces a cousin-family — similar fox, same species, not the same animal. The 8-image coherence capability is GPT Image 2's biggest structural advantage.
5. Edit: remove watermark, preserve composition
Prompt summary: Source image with a watermark in the corner; edit to remove watermark without disturbing subject.
NBP edits are modestly cleaner. The composition survives unaltered; the watermark area is reconstructed plausibly. GPT Image 2 does almost as well but occasionally nudges a neighboring detail (a reflection, a shadow edge). For single-pass edits, both are fine. For chained edits across 5+ passes, NBP drifts less.
Pricing comparison
Real money matters here because both models are at the top of their respective per-image price tiers.
GPT Image 2 on Oakgen: 26 credits per image (~$0.10 at the 260-credit-per-dollar conversion). Included free for 30 days on annual Ultimate/Creator, 7 days on monthly. No platform markup — we pass through provider cost.
Nano Banana Pro on Oakgen: currently tiered 20–28 credits per image depending on resolution (check the live price in the image generator when you select NBP — pricing is dynamic and the generator shows the exact credit cost pre-submit).
Per-image price at list rate: GPT Image 2 and NBP are within ~15% of each other — close enough that neither is disqualified on cost. If you generate 200 images a month, you are looking at a difference of roughly $2–$4. It should not drive your model choice.
Subscription economics: Ultimate annual at $290 covers ~2,900 GPT Image 2 generations at list rate within the base credit allotment, plus unlimited during the 30-day free window. Monthly at $29 covers ~290. NBP follows the same credit math.
Where the math shifts: if you are a designer running high-volume text-heavy or layout-heavy work (posters, mockups, infographics, comics), the annual-plan free window on GPT Image 2 is the single biggest pricing lever in AI image generation right now. The break-even on the annual upgrade is roughly 10 GPT Image 2 generations during the first month.
Which should you pick? A three-branch decision tree
Branch A — You mostly do marketing, design, comics, or anything text- or layout-heavy. Pick GPT Image 2. The text-rendering gap is the biggest single capability jump in image models this year, and everything downstream of "can the model render 10pt body copy correctly" unlocks workflows that previously required Figma post-processing. If you are on an annual Ultimate or Creator plan, the first 30 days are free — start there, and when the free window ends the list rate is still within ~15% of NBP.
Branch B — You mostly do portraits, fashion, beauty, or anything where photoreal skin is the subject. Pick Nano Banana Pro. The skin-texture difference is real and it matters most exactly where human viewers are trained to notice — faces. GPT Image 2 is fine for portraits that happen to be in a composition; NBP is better for portraits as the composition. If you only touch human faces occasionally, the gap is probably not worth model-switching. If you touch them constantly, it is.
Branch C — You do a mix, or you are not sure yet. Pick Oakgen. Switch per prompt. The entire reason the platform exists is that the right model for each prompt is rarely the same model. We have built the image generator so that swapping GPT Image 2 ↔ NBP ↔ FLUX 2 ↔ Imagen 4 is a single-click operation against the same credit wallet, with no separate billing or API juggling. Run your first few prompts on both; the pattern will be obvious by prompt ten.
Iterative edits: if your workflow leans on multi-pass edits that must preserve composition (agency retouching, e-commerce product cleanup), bias toward NBP. GPT Image 2's edit endpoint is good but drifts faster across long edit chains.
Multilingual work: if your target audience reads Japanese, Korean, Chinese, Hindi, or Bengali, GPT Image 2 is the only model we would trust for production-grade signage and packaging work right now. NBP's multilingual rendering is decorative rather than legible.
What we are not testing yet
Photoreal video motion. Video is its own comparison. GPT Image 2 is image-only. Upscaling. Both models are tested at native resolution. Separate upscaler choice is its own decision. Style transfer. Specialized models (Midjourney, Recraft) still win on distinctive aesthetic output. Neither GPT Image 2 nor NBP competes there.
We will revisit these scores after 30 days of public usage. Early-week benchmarks drift as users find prompts that expose new edges.
Try GPT Image 2 on Oakgen
If this comparison tilted you toward GPT Image 2, the easiest path in is the model page — one click, default settings, copy-paste one of our starter prompts. If you want to skip the free-tier and go straight to the 30-day free window, upgrade to annual Ultimate or Creator — no coupon code, the promo is wired into the pricing resolver.
For the full launch rundown including failover architecture and known failure modes, see GPT Image 2 Is Live on Oakgen.
And if you run a channel where model comparisons land — Oakgen pays 25% on referrals for 6 months. Launch-week comparison content historically converts well; this one is ours.
