The honest answer to “GPT Image 2.6 or Nano Banana Pro” is that the question has no answer until you fix three settings — and once you fix them, the answer flips depending on which ones you chose.
Pick Nano Banana Pro if you want a price you can predict. One flat rate per image: $0.134 at 1K or 2K, $0.24 at 4K. One dial. Dense text and complex layouts cost the same as a plain background.
Pick GPT Image 2.6 if you want a cheaper floor and will pin your parameters. At the high rung it costs $0.053 per 1024x1024 image — about 2.5x less than Google. At max it costs $0.211, about 1.57x more. It is also the only one of the two that outputs SVG or animated GIF.
The crossing point is 4,480 output tokens. Below that OpenAI is cheaper per image; above it Google is.
Why there is no single answer
Nano Banana Pro (gemini-3-pro-image) sells images. GPT Image 2.6 sells tokens that happen to come out as an image. That difference is the whole comparison.
Google bills image output at $120 per million tokens, and a 1K or 2K render is a fixed 1,120 tokens — so the per-image price is fixed too, and Google prints it on the pricing page as “$0.134 per 1K/2K image”. A 4K render is 2,000 tokens, printed as “$0.24 per 4K image”. Input is $2 per million, about $0.0011 an image. That is the entire price list.
OpenAI bills image output at $30 per million tokens with no per-image figure published at all — its pricing page now says only “for image generation cost estimates, use the calculator in the image generation guide”. The number of tokens a request consumes is set by three parameters, and all three ship on defaults OpenAI chooses: quality (five rungs, defaults to auto), complexity (three rungs, defaults to high) and size.
So Google’s price is a fact and OpenAI’s is a function. Comparing the two without evaluating the function is the error almost every head-to-head makes.
The price table that settles it
Measured on a 1024x1024 image, at the published rates of $30 per million output tokens for GPT Image and $120 per million for Nano Banana Pro:
| Model and setting | Output tokens | Cost per image | 10,000 images/month |
|---|---|---|---|
GPT Image 2.6 low | 196 | $0.006 | $59 |
GPT Image 2.6 medium | 439 | $0.013 | $132 |
GPT Image 2.6 high | 1,756 | $0.053 | $527 |
GPT Image 2.6 xhigh | 3,122 | $0.094 | $937 |
| crossover | 4,480 | $0.134 | $1,344 |
| Nano Banana Pro 1K or 2K | 1,120 | $0.134 | $1,344 |
GPT Image 2.6 max | 7,024 | $0.211 | $2,107 |
| Nano Banana Pro 4K | 2,000 | $0.24 | $2,400 |
Prices verified 5 November 2026 against OpenAI’s API pricing page and Google’s Gemini API pricing page. GPT Image’s 2.6 and 2.5 rows carry identical rates, so every figure above applies to both generations.
Read the crossover row carefully, because it is the only line that answers the question as asked. Nano Banana Pro’s $0.134 is what 4,480 GPT Image output tokens cost. Four of GPT Image’s five rungs sit below that; one sits above. Google is not the expensive option and OpenAI is not the cheap one — OpenAI is the option whose price you set, and the default is auto, which means you have not set it.
Note also that Google charges the same $0.134 for 1K and 2K. Asking for 2K output is free. Teams that render at 1K out of habit are leaving a resolution upgrade on the table at no cost, which is not true anywhere on OpenAI’s ladder, where every size increase consumes more tokens.
The batch discount that does not exist
The most repeated error about Nano Banana Pro’s price — including in our own September analysis of the GPT Image 2.5 launch — is a batch rate of roughly $0.067, half of standard. It is not on Google’s rate card.
What is true: gemini-3-pro-image lists Batch API as a supported feature, and Google’s Batch API docs say that if you “need to generate a lot of images, you can use the Batch API to get higher rate limits in exchange for a turnaround of up to 24 hours”. What is absent: any batch rate for the model. Its pricing block has a Standard tab and no batch column.
The contrast with its own siblings is what makes this deliberate rather than an omission. Both cheaper Google image models publish explicit batch rows at exactly half of standard:
| Google image model | Standard | Batch |
|---|---|---|
gemini-3-pro-image (Nano Banana Pro) | $0.134 / 1K-2K image | not published |
gemini-3.1-flash-lite-image | $0.0336 / 1K image | $0.0168 |
gemini-2.5-flash-image | $0.039 / image | $0.0195 |
So on Nano Banana Pro, batch buys throughput and patience, not money. Budget $0.134 either way.
This also reframes where Google’s cheap option actually lives: it is not a discounted Nano Banana Pro, it is a different and weaker model. And at the bottom of the market OpenAI wins outright — GPT Image at low costs $0.006, roughly 2.8x less than gemini-3.1-flash-lite-image even at its halved batch rate.
One more line that matters for API buyers: Google’s pricing page marks gemini-3-pro-image as “Not available” on the free tier. Nano Banana Pro is free in the Gemini consumer app — which is the version that carries our Midjourney comparison — but there is no free rung on the API. Neither vendor gives you one here.
Where the money hides on OpenAI’s side
Two of the three dials are the ones to watch, and both were added recently.
complexity defaults to high. OpenAI’s guide states that if you omit it, “both models use high” — the top of three levels — and that it governs how much the model plans before rendering, recommended for “dense text, complex layouts, and relationships between objects”. OpenAI also states that “adjusting complexity has no separate fee”. That is accurate about the rate card and tells you nothing about the bill: there is no published token count for any level, and planning emits tokens. OpenAI’s own caution that “higher complexity can increase latency” points the same way, because latency on a token-metered model usually tracks token count.
This is the asymmetry that catches teams migrating from 2.5. Nothing on the price list moved, so nothing looks like it changed — but a request that inherited auto quality under 2.5 now also inherits high complexity, and the invoice can move without a single number on either rate card moving.
SVG and GIF have no computable price. SVG output bills two models: the image model for a reference image, plus “the authoring model’s rate for the tokens it uses to produce the SVG”. That authoring model is never named and has no pricing row. Animated GIF accepts frame_count from 2 to 16 and defaults to 9, with no per-frame rate published — so a buyer cannot tell whether a default GIF costs 1x or 9x a still.
Both formats are genuine capability advantages; Nano Banana Pro has no answer to either. They are also unbudgetable from the documentation, which makes them prototype features rather than things to put behind a user-facing meter. Google’s top end, by contrast, is a named tier at a named price.
The axis neither pricing page shows: how much warning you get
For a production pipeline, a model disappearing costs more than eight cents an image. The two vendors are not comparable here, and our model retirements calendar tracks both.
OpenAI publishes a policy and keeps dates firm: at least six months’ notice for generally available models, and shutdown dates that do not move once announced. It is also the vendor actively retiring image models right now — gpt-image-1.5, gpt-image-1-mini and chatgpt-image-latest all shut down on 1 December 2026, and gpt-image-1 went on 23 October. The named replacement in every case is a 2.5 Sunburst or Flare model, which 2.6 now sits alongside rather than replaces; both 2.5 models remain listed at unchanged rates with no deprecation entry.
Google publishes no minimum notice period at all. Its deprecations page states that listed shutdown dates “indicate the earliest possible dates on which a model might be retired” and that Google “will communicate the exact shutdown date to users with advance notice” — with no notice period quantified anywhere. The practical trap is id selection: gemini-3-pro-image-preview shut down on 25 June 2026, so pin the GA id gemini-3-pro-image. The cheap sibling gemini-2.5-flash-image carries an earliest date of 15 March 2027, replacement gemini-3.1-flash-lite-image.
Net: OpenAI churns its image lineup more visibly but tells you when; Google moves less often but reserves the right to decide the date later.
Decision table
| If this is your situation | Pick | Why |
|---|---|---|
| High-volume thumbnails, filler, concept passes | GPT Image 2.6 at low or medium | $0.006-$0.013 is 10-22x below Google’s floor for this model |
| Hero imagery where quality is the point | GPT Image 2.6 at high | $0.053 against $0.134 for the nearest comparable output |
| Dense text, posters, complex layouts | Nano Banana Pro | Reasoning is inside the flat price; OpenAI’s complexity dial is unquantified |
| Finance needs a fixed cost per image | Nano Banana Pro | $0.134 is a published number, not a function of three parameters |
| 4K output | Nano Banana Pro | $0.24 at a named tier; OpenAI publishes no per-image figure at its top sizes |
| Vector or animated output | GPT Image 2.6 | Only option of the two — and meter it before shipping |
| You will not pin parameters | Nano Banana Pro | auto quality plus default high complexity is the worst-case bill by design |
| Consumer app, not an API | Neither — see the image generator rankings | This comparison prices the APIs; the app products compare differently |
What to do before you commit
- Pin
qualityandcomplexityexplicitly in the same change if you adopt 2.6. Inheriting both defaults means running the most expensive planning setting underneath a rendering setting OpenAI picks. - Test downward. Most teams assume they need
high. At $0.006, being wrong aboutlowcosts nothing; at $0.211, being wrong aboutmaxacross 10,000 images costs $2,107. - Price Google at $0.134 flat and request 2K, since 1K costs the same.
- Settle quality on your own prompts, because no independent benchmark has rated 2.6 yet.
- Run the arithmetic on your real volume with our API cost calculator.
The verdict: at matched quality GPT Image 2.6 is the cheaper model and Nano Banana Pro is the more predictable one. If your pipeline has an engineer who will pin parameters and watch token counts, OpenAI’s ladder is worth the attention — three of its five rungs undercut Google. If the per-image cost needs to be a constant someone in finance can plan against, Google sells that and OpenAI does not.
Read the full GPT Image / DALL-E review · Read the full Nano Banana Pro review · Compare against Midjourney
What changed
- 5 November 2026: GPT Image 2.6 shipped as
gpt-image-2.6-sunburstandgpt-image-2.6-flareat the 2.5 token rates, adding acomplexitydial that defaults tohigh, plus SVG and animated GIF output with no computable price — see the launch analysis. - 5 November 2026: Corrected on first publication — Nano Banana Pro has no published batch rate, so the roughly $0.067 half-price figure in wide circulation does not exist; batch on
gemini-3-pro-imagebuys rate limits, not a discount.
GPT Image 2.6 vs Nano Banana Pro — frequently asked questions
Which is cheaper, GPT Image 2.6 or Nano Banana Pro?
Neither, until you fix the quality parameter — and that is the whole answer. Nano Banana Pro bills one flat price per image: $0.134 for a 1K or 2K render, $0.24 at 4K. GPT Image bills by output tokens at $30 per million, so its cost per 1024x1024 image runs from about $0.006 at the low rung to about $0.211 at max. Three of GPT Image's five rungs undercut Google, one roughly matches it and one is 1.57x more expensive. The exact crossing point is 4,480 output tokens: below that GPT Image is cheaper per image than Nano Banana Pro, above it Google is. That sits between xhigh (3,122 tokens) and max (7,024), so a comparison that does not state which rung it priced has not compared anything.
Does Nano Banana Pro have a batch discount?
Not a published one, and this is the most commonly misreported fact about its price. Google's model page lists Batch API as supported for gemini-3-pro-image, and the Batch API docs say you can use it to "get higher rate limits in exchange for a turnaround of up to 24 hours" — so batch is available. But the pricing page publishes no batch rate for gemini-3-pro-image at all; the model's block has a Standard tab and nothing else. Its two cheaper siblings do publish batch rows, at exactly half of standard: gemini-3.1-flash-lite-image at $0.0168 against $0.0336, and gemini-2.5-flash-image at $0.0195 against $0.039. So on Nano Banana Pro, batch buys throughput, not a discount, and the roughly $0.067 half-price figure in circulation for it is not on Google's rate card. Price it at $0.134 per 1K/2K image whichever mode you run.
Which produces better images?
There is no independent answer for 2.6 yet, which is itself worth knowing before you commit a pipeline. As of 5 November 2026 Artificial Analysis has no leaderboard row for GPT-Image-2.6 — it shipped on 4 November — while Google's image models have been rated for months. Any Elo figure quoted for "GPT Image" today belongs to an earlier generation, usually gpt-image-2, and carrying it forward to 2.6 assumes exactly the thing the number was supposed to prove. This is the second GPT Image release in a row to launch with no third-party score. The practical consequence is that the quality half of this comparison has to be settled on your own prompts, which means the price half is the part you can actually decide in advance.
Which one should I use for text inside images and complex layouts?
Both vendors aim at it, and they charge for it differently. Nano Banana Pro is marketed by Google as "a professional design engine with a reasoning core for studio-quality 4K visuals, complex layouts, and precise text rendering" — the reasoning happens inside the flat $0.134, so dense text costs the same as an empty background. GPT Image 2.6 added a complexity parameter for the same job, documented for "dense text, complex layouts, and relationships between objects", and it defaults to high, the most expensive of its three levels. OpenAI states that "adjusting complexity has no separate fee", which is true of the rate card and silent on the invoice: planning emits tokens and tokens are the billing unit, with no published token count per level. For layout-heavy work Google's price is the predictable one; OpenAI's is the one you have to meter.
Can either model output SVG or animated GIF?
Only OpenAI, and neither format has a computable price. GPT Image 2.6 added SVG output, which bills the image model for a reference image plus "the authoring model's rate for the tokens it uses to produce the SVG" — an authoring model OpenAI never names and gives no rate-card row. It also added animated GIF, where frame_count accepts 2 to 16 and defaults to 9, with no per-frame rate published anywhere. Nano Banana Pro offers neither; its top end is a named 4K tier at $0.24. So if you need vector or animation output, OpenAI is the only option of the two and you cannot budget it from the documentation — meter it on a test key first. If you need a number you can put in a spreadsheet, that is Google's side of this comparison.
Which is the safer model to build a production pipeline on?
Google gives you the steadier price and OpenAI gives you the steadier calendar, and which matters more depends on your horizon. OpenAI publishes at least six months' notice for generally available models and treats shutdown dates as firm once announced — but it is actively retiring image models, with gpt-image-1.5, gpt-image-1-mini and chatgpt-image-latest all shutting down on 1 December 2026, and gpt-image-1 already gone on 23 October. Google publishes no minimum notice period for the Gemini API at all, and states that listed shutdown dates are "the earliest possible dates on which a model might be retired", with the exact date communicated later. One practical trap on Google's side: pin the GA id gemini-3-pro-image, not gemini-3-pro-image-preview, which shut down on 25 June 2026. Our model retirements calendar tracks both vendors' dates.