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Nano Banana 2.1 vs Nano Banana vs Nano Banana Pro: Same Prompt, Three Generations

Fox — Sun, 11 Oct 2026 06:15:00 +0000

Google released Nano Banana 2.1 on October 6, 2026 as the update to Nano Banana 2 (Gemini 3.1 Flash Image), and deprecated Nano Banana 2 the same day. Three names that differ by a suffix are easy to second-guess, so we put all three through the same set of prompts to see where the new release actually changed.

The method is the same as in our GPT Image 2.5 comparison: four prompts, each run once through all three models at 1K (the original only offers 1K), keeping whatever came out. No re-rolls, no best-of-four. Each section shows the results left to right: Nano Banana, Nano Banana 2.1, Nano Banana Pro.

What the three generations are

Model	1K	2K	4K	Positioning
Nano Banana	3 credits	—	—	The original. 1K only, the cheapest draft machine.
Nano Banana 2.1	4 credits	5	6	October 2026 release, Flash-level speed, up to 4K.
Nano Banana Pro	8 credits	8	10	Google's flagship, strongest on quality and reference-image edits.

A new account gets 10 credits, enough for two 2.1 images. The $1.49 trial pack is 50 credits, which is 16 Nano Banana images, 12 Nano Banana 2.1 or 6 Nano Banana Pro.

Test 1: a documentary portrait

Candid photograph of a man in his sixties repairing a wooden fishing boat on a pebble beach, early morning side light, wool sweater, weathered hands holding a plane, boat hull in flaking blue paint, calm grey sea behind, shallow depth of field, 50mm lens, natural color.

All three followed the brief: the sweater, the plane, the pebbles, the grey sea, the blue boat. The gap is in light and texture.

Nano Banana has the flattest light, more overcast noon than early morning side light, and the hull is mostly solid blue with little of the flaking the prompt asked for. The figure and the action are fine; the frame just lacks a layer of photographic depth.
Nano Banana 2.1 looks most like a reportage frame: the man bent over the plane, shavings at his feet, convincingly weathered paint, the sea and headland softly out of focus. It added a black beanie nobody asked for, which does no harm.
Nano Banana Pro nails the morning light, warm and raking across the figure and the hull, with the finest paint-flake texture of the three, sharp pebbles and even gulls. If one of these is going into a layout, it is this one.

Pro is still the best photograph here, but 2.1 gets within touching distance for half the credits, and the original is clearly a step behind.

Test 2: a poster with two lines of text

A minimalist concert poster. Large headline text "NIGHT TIDES" at the top in a bold serif, below it smaller text "Live at the Harbor · Saturday 8 PM". Illustration of a lighthouse on a cliff under a navy sky with a thin crescent moon, limited palette of navy, cream and coral, flat vector style, clean margins.

Every line of text is correct in all three, including the middle dot and "8 PM", and even the original got it right. Text rendering is no longer the dividing line in this family either; the differences are back to design.

Nano Banana is the most literal: a thin border, headline, subheading, a lighthouse with red beams. Correct palette, tidy layout, flat.
Nano Banana 2.1 added a beam of light sweeping across the frame and a coral cliff, set the subheading in coral, and gave the whole thing tension. The most poster-like of the three.
Nano Banana Pro put the title in a cream band, uppercased the subheading on its own initiative, and drew a neat lighthouse and waves. A safe, finished piece.

2.1 wins this one. If you want strict minimalism, the original is actually the most obedient.

Test 3: a product shot you can count

Product photo on a matte sage-green background: a ceramic pour-over coffee dripper in cream glaze sitting on a glass carafe, exactly three coffee beans to the left of the carafe, a folded white paper filter leaning against the right side, soft top light with one gentle shadow falling to the lower left, no steam, no text, centered composition.

This prompt is an instruction-following test with conditions you can tick off: exactly three beans, a folded filter leaning on the right, a shadow to the lower left, no text.

Nano Banana placed four beans, and the filter is a pleated wave-style dripper standing upright rather than a folded filter leaning against anything. Everything else checks out, but it missed two of the countable instructions.
Nano Banana 2.1 got three beans, a folded filter leaning on the right of the carafe, soft top light and a centered composition. The only quibble is that the dripper reads more like a handled cup than a ribbed dripper.
Nano Banana Pro got three beans, the leaning filter and a clearly defined shadow to the lower left, and its dripper looks most like the real product. The most complete of the three.

2.1 and Pro both pass the bean count; the original misses by one.

Test 4: a 21:9 panorama

Panoramic view of rice terraces at dawn after rain, mist drifting through the valley, mirror-like flooded paddies reflecting a pale orange sky, a single farmer with a conical hat walking along a ridge in the middle distance, a few wooden stilt houses on the far left, soft diffused light, fine detail in the water reflections and terrace edges.

Google's release notes for 2.1 single out wide and panoramic aspect ratios, so this group went in. The strip is stacked top to bottom here because of the width, same order as before. All three models returned a proper 21:9 frame with every element present: terraces, reflections, mist, stilt houses on the left, a figure on a ridge.

Nano Banana draws fine terrace lines, but the mist is thin and the farmer is a small dark dot with no visible hat. The whole frame is a little plain.
Nano Banana 2.1 has the most convincing drifting mist, a farmer with a conical hat and carrying pole right on the ridge, and detail in both the reflections and the terrace edges. It captures "dawn after rain" best.
Nano Banana Pro has the most mirror-like sky reflection and the clearest houses, and added a water buffalo next to the farmer. Not requested, though many people would count it as a bonus.

On the panorama, 2.1 and Pro each have their strengths; the original looks thin by comparison.

Which one to use

Most everyday generation: Nano Banana 2.1. It won two of the four tests and sat right behind Pro in the other two, costs 4 credits at 1K, and offers 2K and 4K. It is now the default pick in the Google column.
Photographic finals that will be scrutinized, and editing a photo you already have: Nano Banana Pro. Its light and texture still lead in the side-lit portrait, and reference-image editing remains its strongest suit, which this piece did not test.
The cheapest possible 1K draft: the original Nano Banana. One credit less than 2.1; in this round it had the flattest light and missed two of the countable instructions.

All twelve images above are single, unedited runs. Your results will differ run to run, which is the honest caveat with any comparison like this. The quickest way to see for yourself is to open the generator with one of these prompts filled in and switch the model from the dropdown:

New visitors can generate one GPT Image 2.5 Flare image before signing up; Nano Banana 2.1 needs an account, and sign-in is free and comes with 10 credits, enough for two tries.

GPT Image 2.5 Flare vs Sunburst vs Nano Banana Pro: Same Prompt, Three Results

Fox — Sat, 03 Oct 2026 10:10:56 +0000

OpenAI shipped GPT Image 2.5 in two versions, Flare and Sunburst, and both are now in the CVY.AI image generator next to Google's Nano Banana Pro. The obvious question is which one to pick. Rather than quote spec sheets, we ran the same three prompts through all three models, once each, at 1K, and kept whatever came out. No re-rolls, no best-of-four.

The three prompts cover the jobs people actually bring to an image generator: a realistic portrait, a poster with several lines of text, and a product photo. Each section shows the three results side by side, left to right: Flare, Sunburst, Nano Banana Pro.

What the two 2.5 versions are

On CVY.AI the two versions differ mostly in weight and price:

Model	1K	2K	4K	Positioning
GPT Image 2.5 Flare	2 credits	3	5	The fast one. Default model on the site.
GPT Image 2.5 Sunburst	3 credits	4	6	The heavier one, for production imagery and careful edits.
Nano Banana Pro	8 credits	8	10	Google's flagship, strongest at reference-image edits.

A new account gets 10 credits, so the first few Flare images are free. The $1.49 trial pack is 50 credits, which is 25 Flare images, 16 Sunburst or 6 Nano Banana Pro.

Test 1: an editorial portrait

Editorial portrait of a woman in her 30s with short silver hair, wearing a dark green wool coat, standing on a rainy Lisbon street at dusk. Natural skin texture, soft window light from a café on the left, shallow depth of field, 85mm lens look. No text.

All three followed the brief: silver hair, green wool coat, wet cobblestones, a yellow Lisbon tram, warm café light from the left. The differences are in taste rather than correctness.

Flare gives the cleanest, most flattering face and the strongest eye contact. The background is a little simpler than the other two, which in a portrait is usually what you want.
Sunburst builds the richest scene: rain on the café glass, pedestrians with umbrellas, the castle on the hill, reflections on the tram tracks. Skin texture is the most photographic of the three, with visible pores and a slightly uneven complexion that reads as a real person.
Nano Banana Pro is a convincing street photo with a busy café behind her, but the face is harsher and less lit, and the whole frame sits a notch below the other two in polish.

For a portrait you would hand to someone, Flare or Sunburst. Flare costs a quarter of Nano Banana Pro here and arguably produced the nicer image.

Test 2: a poster with three lines of text

Minimalist concert poster for a jazz trio. Large headline text "BLUE HOUR SESSIONS", subheading "Live at The Attic · Friday 14 Nov · 8 PM", a small line at the bottom "Tickets at the door". Deep navy background, one warm amber spotlight, a silhouette of an upright bass. Swiss grid typography, clean sans-serif.

Text rendering used to be the dividing line between models. Not any more: all three spelled every line correctly, including the middle dots and "8 PM". What separates them now is design judgement.

Flare is the most faithful to "minimalist": headline, one rule, one subheading, a bass in a spotlight, the small line tucked bottom-left. It looks like a poster someone would print.
Sunburst added things the prompt did not ask for: a piano and a drum kit, a tiny "JAZZ TRIO / BASS PIANO DRUMS" block top-right, letter-spaced caps for the bottom line. The result is more designed and arguably better, but it is a looser reading of the brief.
Nano Banana Pro rendered the poster as a flat vector on a black mount, with a stylised bass. Typography is correct and tight, the image is simply plainer.

If your prompt says what it wants precisely, Flare follows it. If you want the model to bring ideas, Sunburst does.

Test 3: a product photo

Product photo of a matte black ceramic pour-over coffee dripper on a light oak table, next to a glass carafe half full of coffee, morning sunlight from a window casting soft long shadows, a few scattered coffee beans. Clean commercial look, slight steam rising from the carafe. No text, no logo.

This is where the three models look most alike. Matte ceramic, glass with condensation, oak grain and beans are all handled well everywhere, and the morning window light is consistent with the shadows in each image.

Flare put a paper filter with wet grounds in the dripper, a detail nobody asked for that makes the scene read as "just brewed". The steam rises from the carafe as requested.
Sunburst is the cleanest catalogue shot: empty dripper, carafe front and centre, a linen cloth and a bowl of beans as props. This is the one you would put on a product page.
Nano Banana Pro is good but feels more like a snapshot: the table edge in the foreground, the props a little small in the frame, slightly flatter light.

Which one to use

Drafts, posters, anything with text, and most everyday images: Flare. It followed the brief most literally in all three tests and costs 2 credits at 1K.
Hero images, product shots, anything you will zoom into: Sunburst. It consistently produced the richest, most finished frame. One credit more per image.
Editing a photo you already have: Nano Banana Pro. None of these tests used a reference image, which is where Nano Banana Pro earns its price. For generation from text alone, the 2.5 models matched or beat it at a quarter to a third of the cost.

All nine images above are single, unedited runs. Your results will differ run to run, which is the honest caveat with any comparison like this. The quickest way to see for yourself is to open the generator with one of these prompts filled in and switch the model from the dropdown:

New visitors can generate one Flare image before signing up; after that, sign-in is free and comes with 10 credits.

The hard part of national ID OCR isn't the OCR

Fox — Fri, 19 Jun 2026 03:32:18 +0000

You wire up OCR for your KYC flow, point it at a national ID card, and get back a clean { name, idNumber, dateOfBirth }. Ship it. Then you onboard your second country — and it falls apart. Fields you mapped don't exist. The name comes back as garbled Latin. The date of birth says the year 2567.

Here's the thing nobody tells you when you start: the hard part of national ID OCR isn't the OCR. It's that every country's ID is a different document. A model that reads text off a card is table stakes. Turning 30 countries' cards into data your system can actually use is where the work is.

Let me show you the three axes of variation that will bite you, then how to architect so they don't.

Axis 1: the fields are different

There is no universal "national ID" schema, because the cards themselves don't agree on what to print.

A Thai ID card prints the holder's religion.
A German ID card prints height and eye color.
A Chinese ID card prints ethnicity and the issuing authority.

None of these are edge cases — they're core fields on those documents. So the instinct to define one IdCard type with a fixed set of columns is wrong from day one. Either you drop information that some countries consider essential, or you end up with a sparse table full of nulls and country-specific special-casing.

And it's not just which fields exist — it's what they're called and how they're split. The same "name" concept might come back as a single full-name string on one card and as separate given/family fields on another, sometimes in two scripts at once. Your data model has to treat "the field set depends on the country" as a first-class fact, not an afterthought.

Axis 2: the script is different

If your users are global, a lot of their names are not in the Latin alphabet — Chinese, Thai, Arabic, and more.

The naive move is to transliterate everything to Latin "so it's consistent." Don't. Transliteration is lossy and ambiguous: multiple native spellings collapse to the same Latin form, diacritics get dropped, and you can no longer match the name back against the source document or a government database. For KYC specifically, mangling the name defeats the purpose.

The correct approach is to keep the native-script value as printed, and carry a Latin form alongside it when the card itself prints one (many do — they show both). That way you have a local handle for matching against local records and a Latin handle for systems that need ASCII, without throwing away the original.

Axis 3: the format is different — dates and ID numbers

Two formats will surprise you specifically:

Dates aren't all Gregorian. A Thai card prints dates in the Buddhist calendar (BE), which runs 543 years ahead of the Gregorian year and, on top of that, uses Thai numerals rather than Arabic digits. So two things break a naive parser: the digits won't int()-parse at all, and even once you read the number, treating it as a Gregorian year leaves every age check and expiry comparison off by 543 years. The fix is to convert the numerals, subtract 543, and normalize to a single representation (ISO 8601 is the sane choice) — while keeping the original string around for display. (You'll see exactly this — the raw Thai date next to the normalized one — in the response further down.)

ID numbers have structure. A Thai national ID is 13 digits; a Chinese one is 18. Many encode a checksum digit, region codes, or a date. That structure is genuinely useful — you can validate the checksum to catch a misread early — but note the responsibility split: an OCR step gives you the number as printed; validating it against each country's rule is logic you own, per country. Don't assume the extraction layer does it for you.

Two ways to deal with this

Once you accept that ID OCR is per-country, you have two implementation paths:

Build the per-country layer yourself. Run a generic OCR/vision model, then write, per country, the field mapping, the script handling, the date-calendar conversion, and the number parsing — plus tests with real sample cards for each. This is doable, but the cost is linear in the number of countries you support, and it never really ends: layouts get redesigned, new document versions ship, a new market means a new adapter.
Use an API that already encodes the per-country knowledge. You hand it an image and tell it which country/document you're reading; it gives back the fields that document actually has, in the right scripts, with dates normalized. You've outsourced the heterogeneity instead of maintaining it.

If you're supporting more than two or three countries, the second path is usually the honest economic call. PicToText's ID Card OCR API is one option built around exactly this per-country model, so the rest of this post shows what that looks like in practice.

What it looks like in practice

You send one image and a country-specific documentType — the documentType is how you select which national format to read. With cURL:

curl -X POST "https://pictotext.io/api/v1/ocr" \
  -H "Authorization: Bearer sk_live_YOUR_API_KEY" \
  -F "image=@id_card.jpg" \
  -F "documentType=th_id_card"

Or the same call in Python:

import requests

with open("id_card.jpg", "rb") as image:
    resp = requests.post(
        "https://pictotext.io/api/v1/ocr",
        headers={"Authorization": "Bearer sk_live_YOUR_API_KEY"},
        files={"image": image},
        data={"documentType": "th_id_card"},  # e.g. th_id_card, de_id_card, cn_id_card
        timeout=30,
    )
resp.raise_for_status()
data = resp.json()
print(data["nameEn"], data["nameTh"], data["birthEn"])

Now look at the two things that make this a per-country response. A Thai card comes back with native + Latin names, a normalized date next to the original, and religion:

{
  "idNumber": "1234567890123",
  "nameTh": "สมชาย ใจดี",
  "nameEn": "SOMCHAI JAIDEE",
  "birthTh": "๑ มกราคม ๒๕๓๓",
  "birthEn": "1990-01-01",
  "expiryDateEN": "2030-01-01",
  "religion": "พุทธ",
  "address": "123/456 หมู่ 1 ถ.สุขุมวิท เขตคลองเตย กรุงเทพฯ 10110"
}

A German card, same endpoint, returns a different field set entirely — height, eyeColor, placeOfBirth:

{
  "firstName": "ERIKA",
  "lastName": "MÜLLER",
  "documentNumber": "L01X00T28",
  "dateOfBirth": "1990-01-01",
  "nationality": "DEUTSCH",
  "placeOfBirth": "BERLIN",
  "eyeColor": "BLAU",
  "height": "170",
  "address": "PLATZ DER REPUBLIK 1, 11011 BERLIN"
}

Notice what axes 1–3 look like when they're handled: native script preserved (nameTh), Latin alongside (nameEn), the Buddhist-calendar birthdate normalized (birthEn), and each country's own fields returned instead of being flattened away. The exact keys per country are in the field reference — e.g. the Thai ID card page.

Takeaway

If you remember one thing: treat "every country is different" as a first-class design constraint, not something you patch after OCR. Your data model should expect a per-country field set, your name handling should preserve native script, and your dates should be normalized but reversible. Whether you build that layer yourself or use an API that already has it, the teams that get global identity verification right are the ones that designed for the heterogeneity up front.

If you'd rather not maintain a per-country adapter for every market, the docs are open on GitHub and there's a quickstart to try it on your own sample cards.

The problem: too many image models, which one do I use?

Fox — Mon, 01 Jun 2026 09:19:46 +0000

New image-generation models keep landing — Nano Banana, Nano Banana Pro, GPT Image 2, ByteDance's Seedream — and each claims to be the best. But when you actually need one good image, the real questions are:

Same request, different model — how much does the output actually differ?
Re-tuning the prompt for every model is exhausting.
Comparing them means hopping between platforms and signing up over and over.

So I ran a small, reproducible test: fix one prompt, feed it to several models, look at the differences, and boil it down to a selection cheat sheet. To avoid the multi-platform shuffle I did the comparison on cvy.ai, where you switch models from a dropdown on the same prompt — no re-registering, no rewriting.

Step 1: make the prompt reproducible with a template

A fair comparison needs a stable, reusable prompt — otherwise the differences you see are just you writing it differently each time. I break prompts into fixed slots:

[subject] + [style/medium] + [composition/lens] + [light/mood] + [details] + [aspect ratio]

A portrait example:

Subject: a young woman in a casual wool coat, half body, glancing toward camera
Style: cinematic realism, warm film tone
Composition: 85mm telephoto, shallow depth of field
Light: golden-hour, a glowing storefront neon sign reading "CAFE" in the blurred background, rim light on hair
Details: natural skin, windswept hair strands, no over-smoothing
Aspect ratio: 3:4 vertical

Note the neon sign reading "CAFE" — it's deliberate. Text inside an image is one of the clearest ways models differ, so keeping a short word in the scene makes the text-rendering comparison below much more telling.

Flatten that into one continuous prompt and every model gets identical input — that's what makes the comparison mean something.

💡 Tip: instead of staring at an empty prompt box, keep a few reusable templates (portrait / product / scene / social cover) and just swap the subject. cvy.ai ships a set of editable templates I use as a starting point — faster than writing from scratch.

Step 2: same prompt, four models side by side

GPT Image 2 — the winner.

Prompt Adherence: Excellent. The "windswept hair" is dynamic and natural. The pose of the subject glancing over her shoulder adds superb narrative depth.
Text Rendering: The spelling of "CAFE" is perfect. The neon glow and bokeh effect integrate flawlessly with the optical physics of an 85mm lens.
Lighting & Vibe: Perfectly captures the "golden-hour" backlight. The rim light on the hair is spot-on, and the warm film tone is rich and cinematic.
Details & Textures: The skin retains authentic texture without feeling over-smoothed. The wool coat texture is slightly soft but generally solid.
Review: The absolute winner of this test. It completely nails the "cinematic realism" requirement with a perfect balance of atmosphere and accuracy.

Seedream 4.5 — biggest visual impact.

Prompt Adherence: Follows the composition well, though the "windswept hair" feels a bit forced and slightly clumpy rather than naturally blown by the wind.
Text Rendering: "CAFE" is perfectly legible, featuring a very strong and bright neon glow.
Lighting & Vibe: Takes a highly aggressive approach to the "golden-hour" and "rim light" prompts with intense backlighting. This creates massive visual impact, though it sacrifices a bit of the soft film vibe requested.
Details & Textures: The wool coat texture is well-rendered. However, while freckles are present, there is still a faint hint of "AI smoothing" on the skin, making it feel slightly less than 100% natural.
Review: The strongest visual impact. While slightly over-rendered, it is incredibly polished and ready for direct commercial use.

Nano Banana Pro — texture king, lopsided.

Details & Textures: The undisputed king of textures. The coarse, pillowy grain of the wool coat, the natural facial imperfections, and the ultra-realistic skin pores demonstrate terrifying microscopic rendering capabilities.
Text Rendering: "CAFE" is clear, but the neon tube structure looks somewhat stiff and doesn't blend seamlessly into the background lighting.
Lighting & Vibe (Major Deduction): It completely missed the "golden-hour" and "warm film tone" instructions. The lighting is incredibly flat (resembling an overcast afternoon), and the requested rim light on the hair is almost non-existent.
Prompt Adherence: The hair is messy, but it lacks the dynamic motion implied by "windswept."
Review: A hyper-realistic but lopsided specialist. It is visually flawless if you only care about micro-textures, but it completely failed to follow the core lighting and atmospheric instructions.

Nano Banana — baseline.

Prompt Adherence (Major Deduction): The hair is perfectly neat and tucked away, entirely ignoring the "windswept hair" prompt.
Text Rendering: Suffers from AI hallucination; an extra glowing accent mark appeared above the 'E', spelling "CAFÈ" instead of "CAFE".
Lighting & Vibe: The lighting is dull with only a very faint hint of a sunset glow. It lacks cinematic tension, and the background blur feels rigid and artificial.
Details & Textures: The coat feels more like flat felt than coarse wool, and the skin details are the flattest among the four candidates.
Review: Baseline performance. It missed multiple instructions and falls significantly behind the other models in this test.

Step 3: the cheat sheet

Same prompt across the board, distilled (★ = relative strength from this test):

Model	Prompt Adherence	Text Rendering	Lighting & Vibe	Details & Textures	Best for
Nano Banana	⭐⭐	⭐⭐	⭐⭐	⭐⭐⭐	Quick flat drafts
Nano Banana Pro	⭐⭐⭐	⭐⭐⭐	⭐⭐	⭐⭐⭐⭐⭐	Hyper-realistic textures
GPT Image 2	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	Cinematic storytelling
Seedream 4.5	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐	High-impact commercial

The takeaway: no single model wins on every axis. The skill is matching the model to the job — cinematic storytelling from one, raw texture from another, high-impact commercial polish from a third. That's exactly why I didn't want to juggle separate platforms: compare once, and you know which job goes where.

A reusable generation workflow

What the experiment settled into as my default:

Start from a template — pick the closest prompt template, swap the subject.
Pick the model by task — use the cheat sheet; don't default to the same one every time.
Add a reference image when you need direction — when text alone won't land it, upload a reference so the result follows an existing look.
Iterate in small steps — keep prompts, styles, model choices, and good samples together so each render informs the next.

I run the whole flow on cvy.ai — templates, multiple models, and text-to-image / image-to-image in one workspace — which suits a "compare fast, iterate often" habit. The method itself is platform-agnostic, though; any multi-model tool works.

Wrap-up

Don't pick a model by vibes — run one identical prompt across all of them.
Keep prompts structured and templated so comparison is fair and reuse is cheap.
Remember: match the model to the task, don't worship one.

If "which image model should I use?" keeps tripping you up, spend ten minutes running your own same-prompt comparison — the conclusion beats any review. The portrait template and cheat sheet above are yours to copy.