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For new image generation through Google’s Gemini API, use Nano Banana 2.1—not Imagen. Google’s current Gemini API documentation says Imagen is shut down and no longer available through that API. Nano Banana 2.1 (API model code gemini-nano-banana-2.1) is the current high-efficiency option for generating images and editing them through conversation. Google positions Nano Banana Pro for more complex professional assets and Nano Banana 2 Lite for the fastest, lowest-cost high-volume work.
That answer has an important boundary: the shutdown statement is specifically about the Gemini API. It does not establish whether every old Imagen integration, third-party service or legacy endpoint has disappeared. If you are choosing a model for a new Google workflow, however, Nano Banana is the supported direction.
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What the comparison means in October 2026
“Nano Banana” is Google’s branding for Gemini’s native image-generation capabilities. It is not a separate consumer app or a single fixed model. Google’s current model family includes Nano Banana 2.1, Nano Banana 2 Lite, Nano Banana 2 and Nano Banana Pro, each aimed at a different workload.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallImagen is the older Google image-generation line. Google AI for Developers’ image-generation guide now states: “Imagen is shut down and no longer available through the Gemini API. Use Nano Banana for image generation and editing.” That is the practical decision for developers building on the Gemini API: select a Nano Banana model instead of trying to start a new Imagen integration.
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The evidence does not support a fresh, independent Nano Banana-versus-Imagen quality ranking. Google’s published October 2026 evaluation table compares current Nano Banana models and does not include an Imagen score. Treat Google’s descriptions and benchmark numbers as vendor-published information, not as a neutral head-to-head test.
What Nano Banana 2.1 is
Nano Banana 2.1 is identified in Google’s model documentation by gemini-nano-banana-2.1 and is listed as a stable model. Google describes it as an update to Nano Banana 2 and as a high-efficiency model for image generation and conversational editing. The documented input types are text, images, video and PDFs; the outputs are images and text.
| Specification | Nano Banana 2.1 |
|---|---|
| API model code | gemini-nano-banana-2.1 |
| Status | Stable, according to Google’s model page |
| Latest model-page update | October 2026 |
| Input modalities | Text, images, video and PDFs |
| Output modalities | Images and text |
| Input-token limit | 131,072 tokens |
| Output-token limit | 32,768 tokens |
| Generation features listed by Google | Image generation, search grounding, thinking and Batch API support |
| Output resolutions described in the selection guide | 1K, 2K and 4K |
The large context limits matter when an image task depends on several references, a long creative brief or source documents. They do not guarantee that every prompt will produce a good result; composition, legible text and identity consistency still depend on the request and the model’s behavior.
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How Nano Banana 2.1 differs from the other current Google choices
Google’s selection guide frames the models by workflow rather than by a single “best” score. Nano Banana 2.1 is the general-purpose high-efficiency workhorse. Nano Banana 2 Lite prioritizes speed and scale. Nano Banana Pro is the premium choice for complex instructions and professional asset production. Nano Banana 2 is the previous-generation high-efficiency workhorse, and Google recommends 2.1 for new projects.
| Model | Google’s stated role | Best fit | Important limitation or qualification |
|---|---|---|---|
| Nano Banana 2.1 | High-efficiency generation and conversational editing | Most new projects that need a balance of quality, speed, editing and cost efficiency | Google’s positioning, not an independent comparative test |
| Nano Banana 2 Lite | Fastest and cheapest image model | High-volume or latency-sensitive generation | Not optimized for multiple reference inputs or sequential multi-turn editing |
| Nano Banana 2 | Previous-generation high-efficiency workhorse | Existing applications that already depend on it | Google recommends Nano Banana 2.1 for new projects |
| Nano Banana Pro | Premium model for complex instructions and professional asset production | Detailed commercial, editorial or production assets where instruction-following is the priority | Higher-tier positioning; channel-specific availability and pricing must be checked |
| Imagen through the Gemini API | Shut down and unavailable | Not a new Gemini API option | The statement applies to the Gemini API; it does not prove that every historical or third-party route is closed |
Capabilities that make 2.1 the practical default
Conversational, multi-turn editing
Nano Banana 2.1 is designed for an iterative workflow: generate an image, then request targeted changes while preserving the parts that are already correct. This is useful for changing a background, adjusting a product color, replacing an object or refining layout without restating the entire brief each time. “Conversational editing” describes the model’s product behavior; it is not a promise that every edit will preserve every pixel or identity detail.
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Reference-image workflows
Google’s guide says Nano Banana 2.1 supports up to 14 reference images overall, including up to 10 object references and up to four character references. That gives a project room for product shots, style references, characters and scene elements in one request. Organize references clearly and label their intended roles in the prompt; otherwise the model may treat a style image as an object reference or blend incompatible instructions.
Text rendering
Google lists improved text rendering as one of the model’s advantages. This is relevant to posters, packaging mockups, diagrams, thumbnails and social graphics. Generated lettering should still be checked manually for spelling, punctuation, small-print accuracy and brand-critical details before publication.
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The model supports Google Web and Image Search grounding, and Google lists search grounding among its capabilities. Grounding can help a prompt refer to current information or visual context instead of relying only on the model’s internal knowledge. Google specifically notes that grounding does not support using real-world images of people from web search at this time. Do not assume that a request for a current public figure’s photograph will be fulfilled through grounded image search.
Thinking and batch processing
Google’s model page lists thinking and Batch API support for Nano Banana 2.1. Thinking is a selectable model capability in Google’s evaluation table, while Batch API support is relevant to asynchronous or large-volume processing. Exact quotas, latency, pricing and request syntax depend on the access channel and should be checked in the current API documentation.
SynthID watermarking
Google says all generated images include a SynthID watermark. Account for that provenance signal when images move through a publishing, advertising or asset-management pipeline. A watermark does not replace your own review for copyright, likeness, trademark or factual accuracy.
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Which model should you choose?
Choose Nano Banana 2.1 when you need a balanced default
- You are starting a new Gemini API image-generation project.
- You need both first-pass generation and multi-turn revisions.
- Your prompt uses several reference images or requires consistent characters and objects.
- You need 1K, 2K or 4K output options and improved rendered text.
- You want Google Search or Image Search grounding where the request is eligible.
For most new applications, this is the safest starting point because Google identifies 2.1 as the current high-efficiency workhorse and recommends it over the previous-generation Nano Banana 2.
Choose Nano Banana 2 Lite for speed and scale
Lite is the better fit when the dominant constraint is throughput or cost rather than complex editing. Examples include producing many rough concepts, generating variations for internal review or powering a latency-sensitive interface. It is not optimized for multiple reference inputs or sequential multi-turn editing, so it is a poor match for a long, reference-heavy art direction process.
Choose Nano Banana Pro for demanding professional assets
Google positions Pro for complex instructions and professional asset production. Use that description when the job involves unusually detailed composition, strict art direction or a higher expectation of instruction-following. Pro is not automatically the right answer for every image: if your task is routine and high-volume, 2.1 or Lite may better match the operational requirement.
Do not choose Imagen for a new Gemini API build
Imagen is not a currently available Gemini API target according to Google’s guide. If an old code sample, tutorial or internal design document names an Imagen model, treat it as legacy material and identify the corresponding Nano Banana replacement before implementation.
How to start with Nano Banana 2.1
- Select an access channel. Google DeepMind’s model card lists the Gemini app, Google AI Studio, Gemini API, Google Search AI Mode, Google Ads, Google Flow and Google Stitch as distribution channels. A downstream provider may also expose the model through an API under applicable terms.
- Confirm that the channel offers the model. Availability, quotas, pricing and feature access can differ between the consumer app, AI Studio, direct API and other products. The model card says no special hardware or software is required, but that does not mean every channel has identical limits.
- For an API project, select
gemini-nano-banana-2.1. Replace any obsolete Imagen model identifier in your application configuration. Keep the model name in a configuration variable so a future model change does not require editing business logic. - Describe the output and the editing contract. State the subject, composition, camera or illustration style, aspect ratio, required text, exclusions and the role of each reference image. For an edit, identify what must remain unchanged and what may change.
- Add references deliberately. Stay within the documented limit of 14 total references, with no more than 10 object references and four character references. Label them in the prompt and remove redundant images that could create conflicting instructions.
- Choose grounding only when it solves a real need. Use Web or Image Search grounding for eligible current context, and remember the restriction on real-world images of people from web search.
- Review the returned image and text. Check lettering, object counts, faces, logos, proportions, factual details and the requested resolution. Iterate conversationally when a correction is needed rather than silently accepting the first result.
- Record provenance and channel terms. Preserve the model identifier, date, prompt version and any generated-image metadata. Recheck Google’s terms, pricing and availability before moving a prototype into production.
What Google’s published evaluations do—and do not—show
Google DeepMind’s October 2026 model card reports an “Overall Preference” text-to-image evaluation with these results:
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| Model configuration | Overall Preference score |
|---|---|
| Nano Banana 2.1 with Thinking | 1050 ± 14 |
| Nano Banana 2.1 without Thinking | 1015 ± 13 |
| Nano Banana 2 with Thinking | 990 ± 7 |
| Nano Banana Pro | 935 ± 8 |
The same card reports “Infographic Factuality” values of 0.521 for Nano Banana 2.1 with Thinking, 0.328 without Thinking, 0.179 for Nano Banana 2 with Thinking and 0.265 for Nano Banana Pro.
These figures are Google-published evaluation results, not a universal image-quality score. They describe the displayed current-model comparison and do not include an Imagen result. They also should not be read as a guarantee that Thinking will win every prompt, style or production workflow. Your own acceptance tests should use the prompts, references, resolutions and revision patterns that matter to your application.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Migration and troubleshooting
An Imagen model name returns an unavailable-model error
That is consistent with Google’s documented Gemini API shutdown. Replace the Imagen target with Nano Banana 2.1 for a general-purpose migration, then review prompt formatting and output handling because a model-family change can alter response structure and visual behavior.
The model is missing from your product’s picker
Check the channel first. The model card lists several distribution points, but access and availability can vary. A model being listed by Google does not guarantee that every account, region, plan or downstream provider exposes it at the same time.
Reference images are inconsistent
Reduce the number of references, assign each one a clear role and specify which visual attributes must remain fixed. If the workflow depends on many sequential edits, avoid Lite because Google says it is not optimized for that pattern; use 2.1 or consider Pro for unusually complex art direction.
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Generated lettering is almost right but unusable
Put the exact wording in a clearly marked instruction, specify capitalization and punctuation, request a clean high-resolution layout and inspect the result at full size. Regenerate or edit rather than publishing text that has not been proofread. Improved text rendering is a model capability, not a guarantee of perfect typography.
Grounded output includes the wrong person or image
Remember Google’s stated restriction: grounding does not support using real-world images of people from web search at this time. Remove that dependency, use permitted reference material, or redesign the prompt around non-person-specific visual information.
Availability, hardware and pricing considerations
Google DeepMind’s model card says no required hardware or software is needed to use Nano Banana 2.1. The model is listed across consumer, developer and product channels, including the Gemini app, Google AI Studio and the Gemini API. That does not establish one universal price or quota. Billing, rate limits, feature flags and regional access are channel-specific and can change, so verify the terms for the channel you intend to operate.
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For a production decision, compare the cost of the whole workflow rather than only the image call: retries caused by text errors, reference preparation, human review, storage, batch scheduling and any grounding or downstream service charges. The available documentation does not provide one price that applies to every Nano Banana 2.1 access path.
Bottom-line decision framework
| Your priority | Starting choice | Reason |
|---|---|---|
| New Gemini API image-generation project | Nano Banana 2.1 | Google’s current high-efficiency model; Imagen is unavailable through that API |
| Lowest latency or highest volume | Nano Banana 2 Lite | Google describes it as the fastest and cheapest option |
| Complex professional asset production | Nano Banana Pro | Google positions it for complex instructions and professional work |
| Existing Nano Banana 2 application | Evaluate migration to 2.1 | Google recommends 2.1 for new projects; test your prompts and response handling |
| Legacy Imagen tutorial or integration | Identify a Nano Banana replacement | The Gemini API documentation says Imagen is shut down there |
Unless your project has a strong speed-and-scale reason to use Lite or a complex-asset reason to use Pro, Nano Banana 2.1 is the sensible default. It is the current Google model for the workflow the title describes, while Imagen is a historical comparison rather than a supported Gemini API alternative.
Quick Recap
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