GPT Image 2.5 vs Nano Banana 2: Which Should You Use?

TL;DR — OpenAI released GPT Image 2.5 on 8 September 2026, but Google's Nano Banana 2 still holds the edge on extreme image sizes and published benchmark scores. We compared both documentation sets and found three clear differences across size caps, pricing models, and watermarks. One model has thousands of blind human tests behind it, while the other doesn't have any yet.

Verdict: Pick Nano Banana 2 today for wider banners, predictable billing, and tested quality, or wait for GPT Image 2.5 if you need custom pixel dimensions.

Both vendors claim 4K output and smart edits from reference pictures. Their marketing pages look almost identical at first glance.

So how do you pick between them for a real project this week? The differences that matter aren't in the slogans. They're in the size limits, the reference image caps, and what each vendor will tell you about cost before you run a prompt.

GPT Image 2.5 vs Nano Banana 2 at a glance

Every row comes from the vendors' own documentation and the Artificial Analysis leaderboards on 9 September 2026. Sources: https://developers.openai.com/api/docs/guides/image-generation and https://ai.google.dev/gemini-api/docs/image-generation

Nano Banana 2 makes bigger and wider pictures

Both makers say their tools generate 4K images. But when you look at the raw numbers, they don't mean the same thing.

Google gives you distinct shapes for different screen layouts. In Google's specs, Nano Banana 2 generates 4K images up to 5504x3072 at 16:9 and 12288x1536 at 8:1 without any stitching.

Here's why those two numbers matter for everyday work:

• A 5504x3072 image gives you full 4K widescreen coverage with extra room to crop or print.
• A 12288x1536 image lets you build website hero banners, panoramas, and wrap-around print ads without stitching pieces together.

The Nano Banana 2 Generator handles a wide ladder of named aspect ratios. You can pick standard options like 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, and 16:9. You can also pick extreme ratios like 4:1, 8:1, 1:4, and 1:8.

OpenAI sets a hard cap that stops well short of those dimensions. No edge on GPT Image 2.5 can pass 3840 pixels, and its shape can't stretch beyond a 3:1 ratio.

So if you need an ultra-wide panoramic header or an ultra-tall poster, Nano Banana 2 is the only option of the two that'll build it.

GPT Image 2.5 takes any size you like, within four rules

OpenAI takes a different path on canvas dimensions. Instead of locking you to named ratios, it lets you type custom pixel numbers.

According to OpenAI's documentation, GPT Image 2.5 limits custom image sizes to four rules that every request must meet:

• Both width and height numbers must be multiples of 16.
• The aspect ratio must stay between 1:3 and 3:1.
• No single edge can exceed 3840 pixels.
• The total pixel area must sit between 655,360 and 8,294,400 pixels.

OpenAI also marks sizes above 2560x1440 as experimental. Its standard presets are 1024x1024, 1536x1024, and 1024x1536.

This setup gives you finer control for unusual grids. If your front end needs an exact box like 1440x960, OpenAI lets you specify those numbers directly. Google's model won't let you type custom dimensions, so you'll have to pick from its ratio list and scale the output yourself.

Reference pictures: 14 against an unpublished number

A reference image is a picture you give the model to copy the look, subject, or style from. Both models can edit pictures and follow visual cues, but only one documents its actual capacity.

Google publishes clear limits for multi-image prompts:

• You can mix up to 14 reference images in a single request to guide style and subject consistency.
• You can include video inputs along with your text prompts.
• Audio inputs aren't supported for image tasks.
• The model includes a thinking mode that generates unpaid interim thought images to plan composition.

OpenAI claims GPT Image 2.5 is "better at preserving the subjects in your reference photos." But in the documentation we read, OpenAI doesn't publish any maximum reference count for either gpt-image-2.5-flare or gpt-image-2.5-sunburst.

We didn't find a published limit, which isn't the same as there being none. But when you're building a production system that mixes multiple brand photos, Google tells you the ceiling and OpenAI leaves you guessing.

Only one of them tells you what an image costs

Comparing costs between these two tools comes down to how much information the vendors share. Both bill image jobs on token rates rather than flat fees.

Here's what each vendor lists on its base rate card:

• Nano Banana 2 charges $0.50 per million tokens for text or image input.
• Nano Banana 2 charges $3.00 per million tokens for text and thinking output, and $60.00 per million tokens for image output.
• GPT Image 2.5 charges $5.00 per million tokens for text input, $8.00 per million for image input ($2.00 cached), and $30.00 per million for image output.

The critical difference is token visibility. In its documentation, Google lists the exact token cost of every image size so you know what you'll spend:

• 747 tokens for a 512px (0.5K) image.
• 1,120 tokens for a 1K image.
• 1,680 tokens for a 2K image.
• 2,520 tokens for a 4K image.

OpenAI shares no token counts for its 2.5 models. In fact, OpenAI notes that the GPT Image 2 calculator does not estimate GPT Image 2.5 token consumption anywhere in its docs.

So you can forecast a Google bill before spending a dollar, because you know the exact token count for every resolution. With OpenAI, you get a rate card per million tokens, but you won't know your total token spend until after the job finishes.

Both now carry the same invisible watermark

Watermarking on AI images used to split vendors into rival camps. As of September 2026, both OpenAI and Google use the same core protection system.

Google has always embedded SynthID into every image Nano Banana 2 creates. SynthID is an invisible watermark carried inside the image data itself, meaning you can't see it on the canvas, but detection software can read it reliably.

On 8 September 2026, OpenAI adopted SynthID for GPT Image 2.5 output across ChatGPT, Codex, and its API. OpenAI noted that SynthID "embeds an invisible watermarking layer that complements C2PA metadata-based approaches."

Here's what this shared standard means for teams using these models:

• Every image from both generators carries Google DeepMind's invisible watermark.
• Stripping EXIF tags or standard file metadata won't remove the SynthID mark.
• Commercial teams should plan their compliance knowing both models mark their outputs identically.

OpenAI still attaches standard C2PA provenance metadata on top of SynthID, while Google relies on its native SynthID integration.

What the independent scores say, and what they don't

An Elo score comes from blind comparisons. People see two images made from the same prompt, pick the one they prefer, and never learn which model made either. Higher scores mean users picked that model more often.

On the Artificial Analysis text-to-image leaderboard, Nano Banana 2 ranks fourth with an Elo score of 1121 across 15,772 blind human comparisons. The top spots stand as follows:

• First place: OpenAI's older GPT Image 2 (high) at 1178 Elo (14,585 samples).
• Second place: Microsoft's MAI-Image-2.6 at 1149 Elo.
• Third place: Reve 2.1 at 1127 Elo.
• Fourth place: Google's Nano Banana 2 at 1121 Elo.

On the Artificial Analysis image-editing board, Nano Banana 2 ranks fifth with an Elo of 1104 across 11,089 evaluations. MAI-Image-2.6 leads that board at 1122, followed by GPT Image 2 (high) at 1117.

Here's the honest takeaway: neither gpt-image-2.5-flare nor gpt-image-2.5-sunburst appears on either leaderboard as of 9 September 2026.

Nano Banana 2 has thousands of verified blind human tests behind its score. GPT Image 2.5 doesn't have any yet.

That doesn't mean Nano Banana 2 is better than GPT Image 2.5, but it means Google's model has independent receipts while OpenAI's new release doesn't. If you want a proven model from OpenAI today, you can look at the GPT Image 2 Generator.

What each one still gets wrong

Neither tool is flawless. Both vendors document clear limitations that can trip up your projects if you don't plan around them.

According to OpenAI's documentation, GPT Image 2.5 has four main weak spots:

• "Complex prompts may take up to 2 minutes to process."
• It "can still struggle with precise text placement and clarity."
• It "may occasionally struggle to maintain visual consistency for recurring characters or brand elements across multiple generations."
• It "may have difficulty placing elements precisely in structured or layout-sensitive compositions."

According to Google's documentation, Nano Banana 2 has its own limits:

• Best performance is limited to a named language list, including English, Arabic, German, Spanish, French, Hindi, Indonesian, Italian, Japanese, Korean, Portuguese, Russian, Ukrainian, Vietnamese, and Chinese.
• Image generation doesn't support audio inputs.
• "The model won't always follow the exact number of image outputs" you ask for.
• For text inside a picture, Google advises generating the text first and then asking for an image containing it.
• Grounding with Google Search "does not support using real-world images of people from web search at this time".

Reviewing these lists side by side shows that both tools still find structured layouts, exact text placement, and character consistency challenging.

Run Nano Banana 2 on Seadanse today

If you want to test these capabilities without setting up API keys, you can run Nano Banana 2 on Seadanse right in your browser. Both sides of this comparison run in the same picker, so you can test your prompt on one model, switch to the other, and settle the choice for yourself.

Seadanse lets you test multiple generation models side by side:

• Run Nano Banana 2 for high-resolution wide banners and multi-reference compositions.
• Test other top-tier options like the GPT Image 2 Generator and the Seedream 5 Pro Generator.
• Try lighter alternatives such as Seedream 5 Lite and Grok Imagine Image 2.

You can run GPT Image 2.5 here as well, choosing between GPT Image 2.5 Flare for fast everyday tasks and GPT Image 2.5 Sunburst for precise editing at longer generation times. Both take a prompt or up to three reference images at 1K, 2K, and 4K. The GPT Image 2.5 page has the details.

There's also a clear price difference between the two models on Seadanse. GPT Image 2.5 Flare stays at the same price at every size from 1K to 4K. Nano Banana 2 costs more at each step up the ladder.

At 1K they are close, but at 2K Flare costs a little over half, and at 4K a Flare image costs less than half what a Nano Banana 2 image costs.

If you need 14 reference pictures or an ultra-wide shape, Nano Banana 2 earns its price tag, but Flare gives you high resolution for less.

You can create an account on Seadanse today with no credit card required. New sign-ups receive free starter credits to test prompts immediately.

If a generation fails or gets interrupted, the system refunds your credits automatically. You can also buy standalone credit packs with no recurring subscription by checking the Seadanse pricing page.

The image model picker in the Seadanse generator. GPT Image 2.5 is two entries here, Flare and Sunburst — 2026-09-09.

GPT Image 2.5 vs Nano Banana 2 FAQ

Which model makes higher-resolution images?

Nano Banana 2 generates larger single images. At 4K resolution, it can output standard widescreen pictures at 5504x3072 pixels and ultra-wide 8:1 banners up to 12288x1536 pixels. GPT Image 2.5 caps every generation at a maximum edge length of 3840 pixels and an overall ceiling of 8,294,400 pixels.

Which model is better for image editing?

Nano Banana 2 gives you clear operational boundaries for edits, allowing up to 14 reference images and supporting video inputs. It also holds a fifth-place Elo score of 1104 on the Artificial Analysis editing board. GPT Image 2.5 claims improved subject preservation from reference pictures, but OpenAI doesn't publish its reference image cap or independent editing scores.

Is either model free to use?

Neither vendor offers a permanent free API tier. Google charges token rates starting at $0.50 per million input tokens, with fixed token brackets per image resolution. OpenAI charges $5.00 per million text input tokens and $30.00 per million image output tokens. But you can test Nano Banana 2 using free starter credits on Seadanse when you register a new account.

Which model should I pick for a product photo?

You can run Nano Banana 2 on Seadanse if you need to combine up to 14 reference shots of your product from different angles, or if you need an ultra-wide web banner. Pick GPT Image 2.5 if you need an exact, uncommon pixel dimension that fits a strict front-end layout grid.

The verdict

Both models bring strong tools to creative work, but they solve different problems.

Nano Banana 2 gives you extreme aspect ratios up to 8:1, resolutions up to 12288x1536 pixels, a solid 14-image reference cap, and transparent token counts for every resolution. It also has thousands of blind human tests confirming its fourth-place position on independent leaderboards.

GPT Image 2.5 gives you flexible custom pixel sizing within its four rules, alongside native C2PA metadata and SynthID protection. But its size caps are smaller, its token consumption remains unpublished, and independent benchmark trackers haven't ranked it yet.

Here's how we recommend picking between them:

• Choose Nano Banana 2 if you need wide banners, multi-image references, or predictable project costs.
• Choose GPT Image 2.5 if your design layout requires specific custom pixel dimensions within a 3:1 aspect ratio.
• Choose GPT Image 2 if you want OpenAI's highest-ranked model on public leaderboards today.

We compared both vendors' official developer documentation and the Artificial Analysis leaderboards on 9 September 2026.

Related articles

• What Is GPT Image 2.5? — everything OpenAI shipped on 8 September.
• GPT Image 2.5 vs GPT Image 2 — the upgrade question, and the batch discount you lose.
• Is GPT Image 2.5 Free? — what each ChatGPT plan actually gets.