GPT Image 2.5 vs Nano Banana Pro vs FLUX.2 - Which to Use

Short answer: Nano Banana Pro produced the best single images in my tests and renders accented text reliably, GPT Image 2.5 is the cheapest of the three and also got every letter right, and FLUX.2 is strong at photoreal scenes built from several reference images but slips on diacritics. For prompt-only photo edits there is also FLUX.1 Kontext. On Jantaris an image costs $0.0432 per image with GPT Image 2.5, from $0.0747 per image with Nano Banana Pro and from $0.0688 per image with FLUX.2 [pro]. Below: specs side by side and what happened when I ran the same prompts through them.

The models

"From" in a price means higher resolutions cost more; per-resolution rates are on each model page. GPT Image 2.5 is flat on Jantaris: the price doesn't change with quality, size or the number of reference images.

My test: identical prompts, one attempt each

I run my own image-model comparisons on promptowy.com, my Polish blog. On 27 September 2026 I sent the same five prompts to eight API models: a Warsaw market scene, a bakery sign reading "PIEKARNIA ŻÓŁĆ" (a Polish word with four accented letters, a good stress test for diacritics), a flat editorial illustration with no text, a portrait with hands holding a mug, and dumplings shot from above. 16:9, one attempt per prompt, no cherry-picking. The same day I ran both GPT Image 2.5 variants through the same prompts. Full boards are in the image generator ranking and the GPT Image 2.5 test (both in Polish).

Caveats: the scores are mine, one attempt per prompt, and timings were measured through a different API than Jantaris, so yours may differ. On Jantaris, gpt-image-2.5 is the Flare variant and gpt-image-2.5-sunburst is Sunburst.

GPT Image 2.5 and Sunburst

GPT Image 2.5 is OpenAI's September 2026 image generation, offered in the API as two variants. Flare is the faster one. Sunburst, per OpenAI, is meant for long chains of edits where a product or character must stay consistent, and it takes longer.

Strengths:

• Accented text. The Polish sign was correct in both variants at three quality levels, from medium to max. On the market scene it added price cards on its own, also spelled correctly.
• Simple sizes. Three presets (square, landscape, portrait) or auto, at one flat price.
• Up to 16 references. Pass them in image_urls and the request becomes an edit.
• Pricing on Jantaris. Flat per image, even with quality: "max".

The surprise: Sunburst and Flare produced near-identical frames in four of five prompts, down to the same man in the same sweater. Sunburst won on details such as hands.

The weaker side is editing while leaving the rest of the frame alone. In a five-changes-in-one-prompt test on a living-room photo (part of my FLUX hub), Sunburst repainted areas it was told not to touch more than Nano Banana Pro did: structural similarity of the untouched regions was SSIM 0.870 versus 0.942 (1 = identical).

Nano Banana Pro and Nano Banana 2

Nano Banana Pro is Google's Gemini 3 Pro Image. It takes up to 14 reference images, offers 10 aspect ratios and three resolutions: 1K, 2K and 4K. Nano Banana 2 (Gemini 3.1 Flash Image) is the faster sibling: up to 4 images per request, 14 aspect ratios including 8:1 and 1:8, resolutions from 0.5K.

In my ranking Nano Banana Pro had the highest average (9.0) and was at or near the top on every prompt. Nano Banana 2 scored 8.6, was the fastest model in this comparison (12.2 s) and was the only one to get 10/10 on the sign, because it also added a correct "OTWARTE" (open) plate on the door. In a separate August typography test (a sign, a poster and a neon with the Polish pangram "Zażółć gęślą jaźń"), Nano Banana 2 made no letter errors at all.

Things to watch:

• It fills in. Nano Banana added price cards to the market scene and, in the typography test, festival dates and an address nobody asked for. Read every line before a client sees it.
• 4K is not always 4K of detail. In a sharpness test Nano Banana Pro returned a 5504x3072 file, but the real detail matched roughly a 4.2 MP image. GPT Image 2.5 at 3840x2160 was similar, about 4.1 MP.
• Regional dishes. Nano Banana 2 shaped Polish dumplings like gyoza. Pro got them right.
• One image per request on Pro. Several variants means several requests. Nano Banana 2 gives up to 4 at once.

FLUX.2 and FLUX.1 Kontext

FLUX.2 from Black Forest Labs launched in November 2025. Jantaris has three tiers plus the older Kontext line:

• [pro] - the production tier, photoreal, up to 8 references, 1K or 2K, prompts up to 5,000 characters,
• [max] - the highest-quality tier, 1 to 4 MP output, with a seed field,
• [flex] - the tier the vendor positions for control and typography, up to 8 references, 1K or 2K,
• FLUX.1 Kontext [pro] and [max] - the May 2025 line for prompt-only editing without masks, keeping a character consistent across edits; up to 4 references, about 1 MP, with seed.

FLUX.2's strength is composing a scene from several inputs: the room from one photo, furniture from others, the blanket texture from another. In my ranking FLUX.2 [pro] scored 8.5 on the illustration, the portrait and the dumplings, so its photos are solid, and it was quick (13 s).

The weak spot is diacritics. In the ranking it wrote "ZÓŁC" instead of "ŻÓŁĆ". In the August typography test it made at least four errors across three images, including "PIEKARNIÁ" with a foreign accent and the pangram below.

If FLUX.2 is making a graphic with accented text, leave space and set the text in an editor. Plain English text is a different case; this test doesn't cover it.

Editing and references

One rule works for all of them: one change per request, and spell out what must stay the same ("the product, its label and its shadow stay exactly as they are"). Images to edit go in image_urls as public HTTPS URLs.

Which one for what

Same prompt, three models, one script

Value formats differ per model. GPT Image 2.5 takes a pixel size (1536x1024); Nano Banana Pro and FLUX.2 take a ratio (3:2) plus a resolution. GET /v1/models/{id} returns each model's schema.

import os
import requests

API = "https://jantaris.com/v1"
HEADERS = {"Authorization": f"Bearer {os.environ['JANTARIS_API_KEY']}"}
PROMPT = ('Old town bakery storefront at dusk, hand-painted sign reading exactly '
          '"FRESH BREAD DAILY", warm light, loaves on wooden shelves')

jobs = {
    "gpt-image-2.5": {"aspect_ratio": "1536x1024", "quality": "high"},
    "nano-banana-pro": {"aspect_ratio": "3:2", "resolution": "1k"},
    "flux-2-pro": {"aspect_ratio": "3:2", "resolution": "1K"},
}

for model, extra in jobs.items():
    r = requests.post(
        f"{API}/generate",
        params={"wait": 60},
        headers=HEADERS,
        json={"model": model, "input": {"prompt": PROMPT, **extra}},
        timeout=130,
    )
    task = r.json()
    if task.get("status") == "succeeded":
        print(model, task["output"][0]["url"], task["cost"])
    else:
        print(model, task.get("status"), task.get("error") or task.get("poll_url"))

Each model is billed separately and a failed task costs nothing. If an image isn't ready within 60 seconds (Sunburst at high quality can take longer), the response includes a poll_url to check later. Batching and saving files are covered in generate images with a Python API.

In Jantaris Studio you can do the same without code: keep the prompt, switch the model in the list and compare results in your history. How to write prompts that make such comparisons meaningful is in the AI image prompt guide, and to animate the winning image see the image-to-video guide.

FAQ

Which image model handles accented text best?

In my tests Nano Banana Pro, Nano Banana 2 and both GPT Image 2.5 variants rendered the Polish sign with zero errors. FLUX.2 [pro] dropped or misplaced accents.

What is the difference between GPT Image 2.5 and GPT Image 2.5 Sunburst?

Sunburst is slower and, per OpenAI, better at long edit chains. In my test both produced near-identical frames; Sunburst was more precise in details such as hands and took 1.5-2 times longer.

Nano Banana Pro or Nano Banana 2?

Pro for single images where quality matters most (9.0 vs 8.6 in my ranking). Nano Banana 2 for volume: it is faster, returns up to 4 images per request and supports extreme ratios such as 8:1.

Which of these is cheapest per image?

At the time of writing, GPT Image 2.5 ($0.0432 per image). Live rates for every model are on the pricing page. You pay from a prepaid USD balance that never expires, with no subscription.

Can I edit my own photos with these models?

Yes. All of them accept images in image_urls: GPT Image 2.5 up to 16, Nano Banana Pro up to 14, FLUX.2 up to 8, FLUX.1 Kontext up to 4. Get consent before editing photos of real people.

Is any of them free?

Jantaris has no free plan or starter credits; you pay from the first image and the minimum top-up is $10. Google and OpenAI offer limited free image generation inside the Gemini and ChatGPT apps, with limits they set.