Imagen 4 vs Nano Banana Pro: Differences and What to Use Now
October 27, 2026
Imagen 4 vs Nano Banana Pro isn't a live choice any more: Google shut down every Imagen 4 model by August 17, 2026, on both the Gemini API and Vertex AI. Google's own migration map sends Imagen 4 Ultra users to Nano Banana Pro (gemini-3-pro-image) and Imagen 4 Standard and Fast users to Nano Banana 2.1. So the useful question is what you gain, and what you give up, when you make that move.
Here's the short version:
- Imagen 4 was a dedicated text-to-image model. One prompt in, up to four images out, at up to 2K, billed per image.
- Nano Banana Pro is a Gemini model that also draws. It edits in conversation, takes up to 14 reference images, outputs up to 4K, can ground images in Google Search, and bills by tokens.
- Neither Google page compares them head to head. Google never published an Imagen 4 vs Nano Banana Pro benchmark, so anyone who crowns a winner is guessing.
Quick comparison
| Imagen 4 (Standard, Ultra, Fast) | Nano Banana Pro | |
|---|---|---|
| Status | Shut down by August 17, 2026 | Generally available since May 28, 2026 |
| What it is | A text-to-image model with its own endpoint | Gemini 3 Pro Image, a Gemini model built on Gemini 3 Pro |
| Model IDs | imagen-4.0-generate-001, imagen-4.0-ultra-generate-001, imagen-4.0-fast-generate-001 | gemini-3-pro-image |
| How you use it | One prompt, one batch of images | Prompt, then refine in follow-up turns |
| Image input | None. Editing wasn't supported on Vertex AI | Up to 14 reference images |
| Max resolution | 2048x2048 (Standard, Ultra), 1024x1024 (Fast) | 1K, 2K or 4K |
| Aspect ratios | 1:1, 3:4, 4:3, 9:16, 16:9 | 14 ratios, from 1:8 to 8:1 |
| Images per request | Up to 4 | Limited by the output token budget |
| Prompt languages | English GA, seven more in preview | Multilingual, including translating text inside images |
| Google Search grounding | No | Yes |
| Billing model | Per generated image | Per token, with image output converted to a per-image equivalent |
| Watermark | SynthID | SynthID, plus Content Credentials (C2PA) |
The rest of this guide explains each row and where it comes from.
Imagen 4 vs Nano Banana Pro: what each model is
These are two different designs, not two versions of one product.
Imagen 4: Google's last dedicated text-to-image model
Google brought Imagen 4 to developers on June 24, 2025. The Imagen 4 launch post called it Google's "best text-to-image model yet" and promised "significantly improved text rendering" over earlier Imagen models. It came in three variants:
| Variant | Model ID | Positioning |
|---|---|---|
| Imagen 4 | imagen-4.0-generate-001 | The general-purpose flagship |
| Imagen 4 Ultra | imagen-4.0-ultra-generate-001 | Tuned to follow the prompt more closely |
| Imagen 4 Fast | imagen-4.0-fast-generate-001 | Faster, capped at 1024x1024 |
Imagen 4 did one job. You wrote a prompt, it returned images. Google Cloud's Imagen 4 model page is explicit about what it didn't do: no mask editing, no inserting or removing objects, no outpainting, no upscaling and no negative prompts on any of the three versions.
Nano Banana Pro: a Gemini model that draws
Nano Banana Pro is the nickname for Gemini 3 Pro Image. Google DeepMind announced it on November 20, 2025, and the Nano Banana Pro launch post describes it as built on Gemini 3 Pro. It reached general availability on the Gemini API on May 28, 2026, according to the Gemini API changelog.
The difference is structural. Imagen was an image endpoint. Nano Banana Pro is a multimodal model that reads text and images and returns images, so it can take a photo, a sketch and a brief, reason about them, and keep editing across turns. Google's image generation guide calls it "the premium choice for the most complex visual tasks" and says to use it "for professional asset production."
Is Imagen 4 better than Nano Banana Pro?
There's no official answer, because Google never tested one against the other.
The Gemini 3 Pro Image model page publishes human-preference (Elo) charts, but the comparison set is GPT-Image 1, Gemini 2.5 Flash Image, Seedream v4 and Flux models. Imagen 4 isn't on it. The Gemini 3 Pro Image model card doesn't list Imagen 4 either.
What Google did publish is a decision. The Firebase AI Logic Imagen migration guide maps each Imagen 4 model to its replacement:
| If you used | Google says to move to |
|---|---|
imagen-4.0-ultra-generate-001 | gemini-3-pro-image (Nano Banana Pro) |
imagen-4.0-generate-001 | gemini-nano-banana-2.1, thinking level HIGH |
imagen-4.0-fast-generate-001 | gemini-nano-banana-2.1, thinking level MINIMAL |
So Nano Banana Pro is the official successor to Imagen 4 Ultra specifically. Standard and Fast users go to Nano Banana 2.1 instead. How the Gemini image models stack up against OpenAI's is covered in our GPT Image 2 vs Nano Banana breakdown.
Why people still ask "is Imagen 4 better": it was simpler. A flat per-image price, up to four images per call, no thinking step. Quality isn't the only axis.
How they differ in practice
Text-to-image vs conversational editing
With Imagen 4, every change meant a new prompt and a fresh set of images. You couldn't say "same scene, but make the sky darker."
With Nano Banana Pro, you generate an image, then ask for changes in plain language over several turns. The launch post lists localized edits, camera angle and focus changes, color grading, and lighting changes such as turning day into night.
There's a cost. Google's image generation guide says Gemini 3 image models think before they draw, that thinking "is enabled by default and cannot be disabled in the API," and that thinking tokens are billed by default. The model may also render interim "thought images" to test a composition, and Google says those aren't charged.
Multi-image input and consistency
This is the biggest practical gap, since Imagen 4 took text only. Nano Banana Pro accepts up to 14 images per prompt, according to the Gemini Enterprise Agent Platform Gemini 3 Pro Image page (formerly Vertex AI). Google DeepMind says it can keep the resemblance of up to five characters consistent and hold the fidelity of up to fourteen objects in one workflow. That makes these jobs possible without a separate editor:
- Putting one product into several lifestyle scenes
- Keeping the same characters across a storyboard or comic
- Combining a logo, a product shot and a background into one ad
DeepMind adds a fair caveat: the model "excels at character consistency, but it may not always get it right."
Text rendering
Both models were sold on text. Imagen 4 promised "significantly improved text rendering" over earlier Imagen. Nano Banana Pro's launch post goes further, claiming legible text "from short taglines to long paragraphs" in several languages, plus translating text inside an existing image.
Read the limitations before you trust that for print work. Google's own Gemini 3 Pro Image model card lists text rendering as an area for improvement: small text is "often blurry" at 1K, and long paragraphs and page-length text are weak. The DeepMind page says the model "can still struggle with small faces, accurate spelling, and fine details."
The practical rule is the same for both: keep in-image text short, render at 2K or 4K if the text is small, and proofread every word.
Resolution and aspect ratios
| Imagen 4 | Nano Banana Pro | |
|---|---|---|
| Largest output | 2048x2048 on Standard and Ultra | 4K |
| Fast or default size | 1024x1024 on Fast | 1K by default |
| Aspect ratios | 5 | 14, including 21:9, 1:8 and 8:1 |
| Images per call | Up to 4 | Bounded by the 32,768-token output limit |
Banners, tall infographics and 4K hero images are now possible. If you relied on four variants per call, expect a new loop: generate, review, ask for a revision.
Grounding in real-world information
Imagen 4 only knew what it learned in training. Nano Banana Pro supports grounding with Google Search, and Google's launch post names weather and sports as examples of real-time information it can visualize. For a chart of this week's figures, that's a real advantage.
Where you can use Nano Banana Pro today
Imagen 4 is gone from every developer surface. The preview models were retired on February 17, 2026, and the Gemini API deprecations page lists August 17, 2026 as the shutdown date for all three GA models. The migration guide applies that date across the Gemini Developer API and the Agent Platform. The Imagen page in the Gemini API docs now simply says "Imagen models are shut down." Our Imagen 3 API guide walks through the code changes for old Imagen calls.
| Surface | Who it's for | Notes |
|---|---|---|
| Gemini API and Google AI Studio | Developers | Model ID gemini-3-pro-image. Google lists no shutdown date |
| Gemini Enterprise Agent Platform (formerly Vertex AI) | Google Cloud customers | GA, global endpoint only, retirement scheduled for May 28, 2027 or later |
| Gemini app | Everyone, with paid-plan access to Pro | Paid Google AI plans can regenerate an image with "Redo with Pro" |
In the Gemini app, Google's image generation page lists Nano Banana 2 as the free model and names Google AI Pro, Plus and Ultra as the plans that get "Redo with Pro." At launch, Google also brought Nano Banana Pro to Google Slides, Vids, Google Ads and Flow. For free options, see our best free AI image generators roundup.
Pricing model and watermarking
How billing changed
Imagen 4 was billed per generated image, a flat price per variant. Nano Banana Pro uses token billing. The Gemini API pricing page lists separate rates for text and image input, text output (which includes thinking), and image output. Image output is priced per token and then converted into a per-image equivalent, and a 4K image uses more tokens than a 1K or 2K one. Also from that page:
- No free tier. The pricing page lists Nano Banana Pro as not available on the free tier of the Gemini API.
- Batch is cheaper. Batch and Flex rates are half the standard rate, in exchange for slower turnaround.
- Thinking adds cost. Thinking tokens count as text output.
Check that page for current rates rather than trusting a tutorial's numbers.
SynthID and visible watermarks
Provenance didn't get weaker. The Imagen 4 launch post says every Imagen 4 image carries an invisible SynthID watermark. Google's image generation guide says the same about Nano Banana: "All generated images include a SynthID watermark." On the Agent Platform, Nano Banana Pro also supports Content Credentials (C2PA).
The Gemini app adds a visible watermark on top. Google's launch post said free and Google AI Pro users get it, while Ultra subscribers and Google AI Studio output don't. You can also upload an image to the Gemini app and ask whether Google AI made it.
Which one to use now
You can't pick Imagen 4 any more. Here's how to choose within Google's current lineup, based on what each model is documented to do.
| If your job is... | Use | Why |
|---|---|---|
| Replacing an Imagen 4 Ultra pipeline | Nano Banana Pro | Google's official mapping |
| Replacing Imagen 4 Standard or Fast | Nano Banana 2.1 | Google's official mapping, with thinking set to HIGH or MINIMAL |
| Product shots built from your own photos | Nano Banana Pro | Up to 14 reference images |
| Posters, diagrams and infographics with text | Nano Banana Pro at 2K or 4K | Text rendering is a stated strength, but small text blurs at 1K |
| Charts or visuals that need current facts | Nano Banana Pro | Google Search grounding |
| High-volume, low-cost drafts | A lighter Nano Banana model | Pro has no free API tier and bills thinking tokens |
Test with your own prompts before switching a production pipeline. Google's mapping and Elo charts are a starting point, not a verdict.
Keep the workflow, not just the model
Model swaps like this one are why image workflows break. The prompts and edit steps that worked on Imagen 4 have to be rebuilt for a model that thinks and edits in turns.
If you'd rather keep that rebuilt workflow than rediscover it next time, Taku is an AI-native desktop workspace where you can save an image workflow that works, remix it for your own files, and run it again instead of re-prompting from scratch. Taku is in Beta, and the Mac app is available now.
FAQ
Is Imagen 4 better than Nano Banana Pro?
Google never published a head-to-head comparison, so there's no official answer. Imagen 4 was simpler: text in, up to four images out, billed per image. Nano Banana Pro does more: conversational editing, up to 14 reference images, 4K output and Search grounding. And since Imagen 4 is shut down, Nano Banana Pro is the one you can actually use.
Is Imagen 4 still available?
No. Google's migration guide says all Imagen models shut down as of August 17, 2026, on both the Gemini Developer API and the Agent Platform (formerly Vertex AI).
What replaced Imagen 4?
Google maps Imagen 4 Ultra to Nano Banana Pro (gemini-3-pro-image), and Imagen 4 Standard and Fast to Nano Banana 2.1 (gemini-nano-banana-2.1) with thinking set to HIGH and MINIMAL respectively.
Imagen 4 vs Nano Banana: what's the core difference?
Imagen was a dedicated text-to-image model with its own endpoint. Nano Banana is the name for Gemini's native image generation, so the same model that reads your prompt and reference images also draws and edits the result.
Can I use Nano Banana Pro for free?
Not through the Gemini API, where Google's pricing page lists it as unavailable on the free tier. In the Gemini app, free users get Nano Banana 2, and paid Google AI plans can regenerate images with Nano Banana Pro.
Key takeaways
- Imagen 4 was shut down by August 17, 2026, across the Gemini API and the Agent Platform.
- Nano Banana Pro is Google's named successor to Imagen 4 Ultra. Standard and Fast map to Nano Banana 2.1.
- Nano Banana Pro adds conversational editing, up to 14 reference images, 4K output, more aspect ratios and Search grounding.
- You give up flat per-image billing and four images per call, and thinking can't be turned off.
- Both models watermark every image with SynthID.