GPT Image 2 vs Nano Banana 2: Text and People Compared

GPT Image 2 and Nano Banana 2 are both current VdoBloom image models that generate and edit, and both carry the platform’s STRICT content label. The decision comes down to how they are priced and what they accept: GPT Image 2 costs 3, 5 or 8 credits depending on whether you render at 1K, 2K or 4K and takes prompts up to 20,000 characters, while Nano Banana 2 is a flat 15 credits at any resolution up to 4K, accepts up to 14 reference images, and covers 15 aspect ratios including extreme 1:8 to 8:1 strips.

Updated October 2026.

The two models side by side

Fact	GPT Image 2	Nano Banana 2
Credits per image	3 at 1K, 5 at 2K, 8 at 4K	Flat 15 at 1K, 2K or 4K
Resolutions	1K, 2K, 4K	1K, 2K, 4K
Aspect ratios	11, including auto	15, including auto and 1:8 to 8:1 extremes
Reference images per job	Up to 10 (edit)	Up to 14
Prompt length	Up to 20,000 characters	Up to 5,000 characters on edit
Generate and edit in one model?	Two separate entries in the picker	Yes, one model
Content label	STRICT	STRICT
User rating on VdoBloom	4.6	4.3
Output formats	Standard image output	jpg or png

What GPT Image 2 is

GPT Image 2 is OpenAI’s image model, launched on VdoBloom in April 2026, and its defining trait on the platform is that the price follows the resolution you actually choose. A round of quick concept drafts at 1K costs 3 credits an image; the final export at 4K costs 8. There is no flat surcharge beyond that step, so sketching cheaply and finishing expensively works the way you would hope.

Two rules catch people out. Leaving the aspect ratio on auto forces 1K output no matter which resolution you selected, and a square 1:1 image cannot be rendered at 4K at all — it caps at 2K. Neither is a fault, but both surprise anyone who sets 4K and auto and wonders why the result is small.

The other number that matters is prompt length: 20,000 characters. That is enough room to specify exact object counts, spatial layout, and text that needs to appear in the frame and have the model track all of it. Editing runs as a separate entry in the picker and accepts up to 10 reference images, with the same 3, 5, 8 credit ladder.

What Nano Banana 2 is

Nano Banana 2’s headline is the aspect ratio list. Most generators stop at 21:9; this one goes on to 4:1 and 8:1 horizontal strips, with 1:4 and 1:8 vertical counterparts. That puts web leaderboards, skyscraper ad units, panoramic headers and side-rail graphics inside a single generation instead of an outpainting exercise. Fifteen options in total, including an auto setting that lets the model pick framing from your prompt and inputs, and each renders at 1K, 2K or 4K for the same flat price.

The second headline is fourteen — the number of reference images one job accepts. That turns it into a compositing tool as much as a generator: character from one image, wardrobe from another, product from a third, environment from a fourth, merged into one coherent frame. It handles text-to-image and image-to-image in the same interface, so the line between generating and editing largely disappears. It is rated 4.3 by VdoBloom users and carries the STRICT content label, so restricted material is refused in any mode.

Text in images: what the facts support

Neither model is VdoBloom’s named pick for rendering readable text, and it is worth saying that plainly rather than guessing. The platform’s own routing guidance points at the Qwen and Seedream families for typography — Seedream 4.5 for lettering baked into a generated image at 7 credits, and Qwen Image 2.0 Pro for typography-heavy layout work at 12 credits. If legible text is the whole point of the image, those are the tiers to compare, and best AI image model for text in images sets out the full routing.

What GPT Image 2 and Nano Banana 2 bring to text is different: capacity and resolution. GPT Image 2 takes prompts up to 20,000 characters, which is what lets you specify exact wording, its position, and its styling and expect it to survive. Nano Banana 2’s contribution is the flat price at 4K — 15 credits whether you render at 1K or 4K — and 4K is where small type stops disintegrating. Both are also STRICT on content, so expect refusals on restricted material in either.

People and portraits

For portraits and people, the levers the two models actually differ on are reference count, filter behaviour and cost, not a documented quality gap.

Reference images. Nano Banana 2 takes up to 14 per job against GPT Image 2’s 10, so a composite that needs several faces or a whole outfit plus environment has more room on the former.
Identity preservation. GPT Image 2 handles long, multi-part editing instructions well — asking it to replace a background, keep the subject’s pose exactly, and add a logo top-right tends to come back as three distinct, correctly applied changes rather than one blended guess. That matters when a portrait has to change in one specific way.
Filtering. Both carry the STRICT label. Portraits, headshots and lifestyle imagery are fine; restricted content is refused in either model, in any mode.
Cost per attempt. Re-rolling a portrait at 1K costs 3 credits on GPT Image 2 and 15 on Nano Banana 2. If your workflow is twenty variations to find one, that difference compounds.

If the person in the image is real and not you, you need their permission before you upload. That applies to both models equally.

The cost maths

At 1K and 2K, GPT Image 2 is cheaper per image by a wide margin — 3 and 5 credits against a flat 15. At 4K the gap narrows to 8 against 15, but does not close. What changes the calculation is how many images you make at each size. A workflow of twenty 1K explorations and one 4K final costs 68 credits on GPT Image 2 and 315 on Nano Banana 2. If your work is entirely 4K, and you need the extreme aspect ratios or the wider reference count, the flat 15 is a simpler budget to predict even though it is higher.

Credits come from a subscription or from one-time packs that never expire, so an unused balance carries forward rather than resetting monthly. Paid output is watermark-free with commercial rights, the cost of each run is shown before you generate, and new accounts start with 10 free credits and no card. The pricing page has the full plan list.

Which one to pick

Iterating cheaply, then exporting large: GPT Image 2. The 3, 5, 8 ladder rewards drafting at 1K.
Extreme banner or skyscraper formats: Nano Banana 2. GPT Image 2 has nothing between 21:9 and nothing at all.
Compositing from many sources: Nano Banana 2, for the 14-image ceiling.
Long, precise, multi-part instructions: GPT Image 2, for the 20,000-character ceiling.
One model for both generating and editing: Nano Banana 2, which does both in one place.
Square output at maximum size: neither at 4K — GPT Image 2 caps 1:1 at 2K.

Honest limitations

GPT Image 2: auto aspect forces 1K; 1:1 cannot reach 4K; generation and editing live as two separate entries so you switch between them; and 4K runs at 8 credits are not the cheap option at volume.
Nano Banana 2: 15 credits flat is expensive for quick 1K exploration; the edit prompt ceiling is 5,000 characters against GPT Image 2’s 20,000; and the STRICT label will refuse restricted material outright.
Both: neither is the platform’s typography pick, and both are strict on content limits.

Frequently asked questions

Is GPT Image 2 or Nano Banana 2 cheaper?

GPT Image 2, at every resolution: 3 credits at 1K, 5 at 2K and 8 at 4K, against Nano Banana 2’s flat 15. The gap is widest at 1K and narrowest at 4K.

Which one is better at rendering text in an image?

Neither is VdoBloom’s recommended model for readable typography — the platform’s routing points to Seedream 4.5 and Qwen Image 2.0 Pro for that. What both of these offer is GPT Image 2’s 20,000-character prompt ceiling for specifying exact wording, and Nano Banana 2’s flat 4K price where small type holds up better.

Can I generate and edit in the same model?

On Nano Banana 2, yes — text-to-image and image-to-image share one interface and one flat price. GPT Image 2 has separate generation and editing entries in the picker, though both use the same 3, 5, 8 credit ladder.

How many reference images can I attach?

Up to 14 on Nano Banana 2 and up to 10 on GPT Image 2’s editing model.

Why can’t I get a 4K square image?

On GPT Image 2, a 1:1 output cannot be rendered at 4K and caps at 2K. It is a documented constraint rather than an error — pick 2K, or choose a non-square ratio if 4K matters more than the square crop.

GPT Image 2 and Nano Banana 2 each have a full model page with specs, use cases and credit costs. For step-by-step walkthroughs, see how to use GPT Image 2 and how to use Nano Banana 2.