GPT Image 2 and Nano Banana 2 do not rank against each other. They divide the work. The GPT Image 2 vs Nano Banana 2 debate usually gets staged as a title fight, and that framing quietly costs creators hours, because the two models are strongest in places that barely overlap. One was built to put readable, precisely placed text and structured layouts inside a picture. The other was built to push resolution, canvas shape, and reference material further than most image pipelines have ever needed.<\/p>\n
The practical version of the question sounds different. Name the deliverable sitting in front of you, and the answer usually resolves in about ten seconds. A pricing table that has to be legible at thumbnail size goes one way. An ultra-wide banner delivered at 4K goes the other. This comparison is organized around those jobs rather than around a scoreboard, and both models are available inside Picsart, so testing the verdict costs you a prompt rather than a subscription decision.<\/p>\n
The short version of every specification that changes a decision. Full detail on each model lives on the GPT Image 2 model page<\/a> and the Nano Banana 2 model page<\/a>. Read down the two columns to see where they genuinely diverge, then use the job table below to pick one.<\/p>\n Type is where the two models separate fastest, and the separation runs in two different directions at once. GPT Image 2 is documented for reliable text rendering, with crisp lettering, consistent layout and strong contrast, which is exactly the combination a menu board, a pricing card or a labeled diagram needs. OpenAI’s own prompting guidance is unusually practical here: put literal copy in quotation marks or capitals, specify the typography you want, and spell awkward words letter by letter so the model has no room to improvise. Push the quality setting to medium or high for anything with small type, because that is where legibility is won or lost.<\/p>\n Nano Banana 2 answers with reach instead of precision. Multilingual text rendering across 15 languages means a campaign that ships in several markets can stay inside one model rather than being rebuilt per locale, and the character shapes hold up rather than dissolving into decorative squiggles. That is a different kind of win, and it matters enormously to teams shipping regionally.<\/p>\n Both sides come with an honest caveat, and skipping it would do you no favors. OpenAI states plainly that text placement in GPT Image 2 is improved but still imperfect, and that the model can struggle to place elements precisely in layout-sensitive compositions. Plan on iteration for anything where a headline has to sit in an exact spot, and treat the first generation as a rough cut rather than a final file.<\/p>\n Here is the constraint most people discover too late. GPT Image 2 caps the ratio between its long and short edge at 3:1, and that single number quietly disqualifies a whole category of work. An 8:1 header strip or a 1:4 vertical tower simply cannot come out of the model, no matter how the prompt is written. Nano Banana 2 treats those shapes as normal, offering 1:1, 3:2, 2:3, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9 and 21:9 alongside genuine extremes at 4:1, 1:4, 8:1 and 1:8.<\/p>\n Resolution splits along similar lines. Nano Banana 2 runs at 512px, 1K, 2K and 4K as ordinary settings you select and forget about. GPT Image 2 is more particular: both edges must be multiples of 16, the longest edge stops below 3840px, and total pixels have to land between roughly 655,000 and 8.3 million. Anything above 2560×1440 is explicitly flagged as experimental and more variable, which is a polite way of saying results get less predictable at the top of the range.<\/p>\n Inside Picsart the practical picture is cleaner than the raw specification. GPT Image 2 delivers native 2K up to 2048×2048 with 4K in beta, and aspect ratios spanning 3:1 to 1:3, including the 16:9 that most social and web work actually needs. Nano Banana 2 covers the full 512px to 4K span. Choose by output format first, then worry about everything else, because format is the one axis where a wrong choice cannot be prompted around.<\/p>\n Nano Banana 2 handles crowds and catalogues. It accepts up to 10 object references plus up to 4 character references, 14 in total, which is enough to stage a scene with a small ensemble and a full product range without splitting the job into passes. That headroom is what makes campaign work, lookbooks and repeatable brand scenes feasible in a single prompt.<\/p>\n GPT Image 2 comes at consistency from the opposite end, prioritizing depth over breadth. Its documented strength is facial and identity preservation through edits and multi-step workflows, so a face survives a lighting change, a wardrobe swap or a background replacement. Multiple inputs get referenced by index, describing what each image contributes and how the elements interact. Every image you feed it is processed at high fidelity automatically, with no dial to turn that down, so edits built on reference images cost more to run than plain generations do.<\/p>\n Two limitations deserve to be said out loud rather than buried. OpenAI notes that GPT Image 2 can struggle to hold recurring characters or brand elements consistent across separate generations, so a series may drift. And mask-based editing is prompt-guided rather than strictly geometric, meaning the model may not follow the exact shape of the mask you supply. Both are workable. Neither should surprise you mid-project.<\/p>\n Nano Banana 2 can consult Google Search, Image Search included, before it draws. That turns a guess into a lookup for anything where accuracy is the deliverable rather than a nice bonus: a landmark that has to look like itself, an object that has to match its real proportions, a scene that has to reflect current reality rather than a plausible invention. Very few image models offer anything comparable.<\/p>\n One boundary is worth knowing: image grounding does not extend to searching for real people.<\/p>\n Nano Banana 2 also thinks before it answers, at two levels, minimal by default and high on request. At the higher setting it produces up to two interim thought images on its way to the final result, and those interim frames are not charged, though the reasoning behind them is billed. GPT Image 2 counters with strong built-in world knowledge and reasoning, which handles context inference well but cannot check anything against live sources.<\/p>\n The two models cross over rather than one simply undercutting the other. GPT Image 2’s low setting is roughly seven times cheaper than the cheapest Nano Banana 2 tier, which makes it the natural draft engine. At the top end the advantage reverses, because a 4K image from Nano Banana 2 costs less than a single high-quality square from GPT Image 2. The crossover sits around medium quality.<\/p>\n One quirk is worth planning around. GPT Image 2 charges less for portrait and landscape than for squares at the same quality, so a non-square default saves money across a project. The expensive mistake on either model is generating exploratory work at high quality.<\/p>\n\n
\n \n<\/th>\n GPT Image 2<\/th>\n Nano Banana 2<\/th>\n<\/tr>\n<\/thead>\n \n Made by<\/td>\n OpenAI<\/td>\n Google<\/td>\n<\/tr>\n \n Max resolution<\/td>\n Native 2K, up to 2048×2048, 4K in beta<\/td>\n 4K, plus 512px, 1K and 2K<\/td>\n<\/tr>\n \n Aspect ratios<\/td>\n 3:1 to 1:3, including 16:9<\/td>\n 1:1 through 21:9, plus 4:1, 1:4, 8:1 and 1:8<\/td>\n<\/tr>\n \n Reference images<\/td>\n Multiple, referenced by index, every input processed at high fidelity<\/td>\n Up to 10 objects plus up to 4 characters, 14 total<\/td>\n<\/tr>\n \n Text inside the image<\/td>\n Crisp lettering, strong contrast, structured layouts<\/td>\n Multilingual rendering across 15 languages<\/td>\n<\/tr>\n \n Search grounding<\/td>\n No<\/td>\n Yes, Google Web and Image Search<\/td>\n<\/tr>\n \n Transparent background<\/td>\n No<\/td>\n No<\/td>\n<\/tr>\n \n Output formats<\/td>\n PNG, JPEG, WebP, with 0 to 100 compression<\/td>\n PNG and JPEG<\/td>\n<\/tr>\n \n Reasoning step<\/td>\n Built-in world knowledge and reasoning<\/td>\n Thinking, at minimal or high<\/td>\n<\/tr>\n \n Per-image cost<\/td>\n $0.006 to $0.211<\/td>\n $0.045 to $0.151<\/td>\n<\/tr>\n \n Reach for it when<\/td>\n Type, layout and structure carry the image<\/td>\n Resolution, canvas shape or reference volume carry the image<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/figure>\n Text inside the image is the sharpest split<\/span><\/h2>\n
Resolution and shape rule out more jobs than quality settings do<\/span><\/h2>\n
Reference images: a cast on one side, a likeness on the other<\/span><\/h2>\n
Grounding turns a guess into a lookup<\/span><\/h2>\n
The price crossover, and where it flips<\/span><\/h2>\n