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Use Nano Banana 2.1 for new image-generation work through the Gemini API. Google’s current Gemini API documentation says Imagen is shut down and no longer available through that API, and directs developers to Nano Banana for image generation and editing. The statement is specific to the Gemini API; it does not prove that every legacy integration or third-party service has disappeared.
Nano Banana 2.1, identified by the API model code gemini-nano-banana-2.1, is Google’s stable, high-efficiency model for generating images and making conversational edits. Google positions Nano Banana Pro for more complex professional assets and Nano Banana 2 Lite for the fastest, lowest-cost high-volume work. Those are Google’s product descriptions, not an independent head-to-head test against Imagen.
The short answer: choose Nano Banana 2.1 for new Gemini API projects
If your decision is about what to use today in the Gemini API, Imagen is not an available choice. Google AI for Developers’ current image-generation guide says, in substance, that Imagen has been shut down for the Gemini API and that Nano Banana should be used for generation and editing instead.
Nano Banana 2.1 is the practical default when you need a balance of quality, speed, conversational editing, text in images, reference-image control and multiple output resolutions. It is not automatically the best option for every workload:
#1 Best Overall
- Choose Nano Banana 2 Lite when throughput, response time and low cost matter more than complex reference-image workflows.
- Choose Nano Banana Pro when the asset has demanding instructions or a professional production requirement that justifies a premium model.
- Choose Nano Banana 2.1 for a general-purpose workhorse, especially when you need multi-turn refinement, search grounding or output at 1K, 2K or 4K.
Google’s published model card reports stronger evaluation numbers for Nano Banana 2.1 than the other current Nano Banana variants shown in its table. It does not report an Imagen result, so those figures cannot establish that Nano Banana 2.1 is objectively better than a particular Imagen version.
What Nano Banana and Imagen mean now
Nano Banana is Gemini’s native image capability
Nano Banana is Google’s branding for image generation built into Gemini models. The current stable model page names Nano Banana 2.1 as gemini-nano-banana-2.1 and describes it as an update to Nano Banana 2. Its documented inputs are text, images, video and PDFs; its outputs are images and text. The model page lists an input-token limit of 131,072 and an output-token limit of 32,768, with the latest update shown as October 2026.
Because the model accepts images and supports conversational editing, a workflow can begin with a generated or uploaded image and continue through several correction turns rather than restarting from a single prompt. The model page also lists image generation, search grounding, thinking and batch API support.
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Imagen is no longer an option in the Gemini API
Google’s current Gemini API guide explicitly says that Imagen is shut down and unavailable through that API. This is the decisive difference for developers selecting a Gemini API model. The wording does not identify a specific Imagen release, and it does not establish the status of every historical product, private deployment or third-party provider. If an older application still exposes an Imagen control, verify that provider’s own documentation before assuming it is backed by the current Gemini API.
Current Google image models compared
The following table combines Google’s current selection guidance with the availability statement in its Gemini API documentation.
| Model | Google’s stated role | Best fit | Key considerations |
|---|---|---|---|
Nano Banana 2.1gemini-nano-banana-2.1 |
High-efficiency image generation and conversational editing | New projects needing a broad capability mix | 1K, 2K and 4K output; improved text rendering and multi-turn consistency; supports search grounding, thinking and batch API; up to 14 reference images overall |
| Nano Banana 2 Lite Gemini 3.1 Flash Lite Image |
Fastest and cheapest image model | Speed and scale | Not optimized for multiple reference inputs or sequential multi-turn editing |
| Nano Banana Pro Gemini 3 Pro Image |
Premium model for complex instructions and professional asset production | Demanding, production-oriented assets | Use when instruction complexity and professional output requirements outweigh efficiency priorities |
| Nano Banana 2 Gemini 3.1 Flash Image |
Previous-generation high-efficiency workhorse | Existing integrations that already depend on it | Google recommends Nano Banana 2.1 for new projects |
| Imagen | Former Google image-generation family in this context | Not available through the Gemini API | Google says it is shut down for the Gemini API; no current like-for-like Gemini API comparison is supplied |
Names in this table are Google’s product labels and mappings. They should not be treated as interchangeable API identifiers: the only identifier supplied for Nano Banana 2.1 is gemini-nano-banana-2.1.
Capabilities that make Nano Banana 2.1 the default
Resolution choices
Google’s guide describes Nano Banana 2.1 as supporting 1K, 2K and 4K output resolutions. Select the smallest resolution that satisfies the delivery requirement when latency and processing volume matter; select 4K when a large working file is genuinely needed. The documentation establishes the available resolution tiers, but it does not provide a universal file-size, latency or cost figure for each tier.
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Conversational, multi-turn editing
Nano Banana 2.1 is designed for an iterative conversation: generate an image, request a targeted change, inspect the result, and continue. Google specifically highlights improved multi-turn consistency. That is useful for changes such as replacing a background, adjusting a product color or correcting a label while preserving the rest of the composition. Consistency is a documented product characteristic, not a guarantee that every object, face or pixel will remain unchanged after every turn.
Reference images
The official guide says Nano Banana 2.1 supports up to 14 reference images in total, including up to 10 object references and up to 4 character references. This lets a prompt distinguish between the subject to preserve, the objects to combine and the people or characters whose appearance should guide the result.
Reference limits are ceilings, not a promise that every 14-image arrangement will produce a coherent result. For a reliable workflow, label references in the prompt by role and describe which attributes must remain fixed. Nano Banana 2 Lite is specifically described as not optimized for multiple reference inputs, so moving a reference-heavy workflow to Lite may reduce suitability even when it improves speed or cost.
Text rendering inside images
Google lists improved text rendering for Nano Banana 2.1. This matters for posters, diagrams, packaging concepts, interface mockups and infographics, where illegible lettering can make an otherwise attractive image unusable. Treat generated copy as a visual draft: proofread every word, number and symbol before publication, and regenerate or edit when exact text is a requirement.
Search grounding
Nano Banana 2.1 supports Google Web and Image Search grounding. Grounding can help a prompt use current factual or visual context instead of relying only on the model’s internal knowledge. Google notes an important restriction: grounding does not currently support using real-world images of people found through web search. Do not design a workflow that expects the model to retrieve and reproduce a specific real person from image search.
Input and output modalities
The model page lists text, images, video and PDFs as inputs and images and text as outputs. That combination supports workflows such as turning a PDF brief into a visual concept, using a video frame as a style reference or editing an uploaded image through text instructions. The documented token limits are 131,072 input tokens and 32,768 output tokens; those limits are model specifications, not a recommended prompt size for every request.
How to choose between Nano Banana 2.1, Lite and Pro
Choose Nano Banana 2.1 for a mixed creative workflow
Use 2.1 when a project combines several of the following requirements:
- Repeated edits in one conversation rather than one-shot generation.
- Several reference images, including separate object and character references.
- Readable text in a poster, chart, package or infographic.
- Grounding with Google Web or Image Search.
- A choice between 1K, 2K and 4K output.
- Batch processing alongside interactive generation.
This is the safest starting point when you do not yet know which capability will dominate the workflow. It is also the model Google recommends for new projects instead of the previous-generation Nano Banana 2.
Choose Nano Banana 2 Lite for speed and scale
Lite is the appropriate experiment when the main objective is to produce many images quickly and economically. Google calls it the fastest and cheapest image model. That positioning is useful for thumbnails, variations, routine content operations or applications where users rarely make sequential corrections.
Do not choose Lite solely because a task is visually simple if it depends on multiple references or a long chain of edits. Google explicitly says Lite is not optimized for those workflows. Confirm the current channel’s pricing and quotas before committing to a volume forecast; the supplied model descriptions do not state universal prices.
Choose Nano Banana Pro for complex professional assets
Pro is Google’s premium choice for complex instructions and professional asset production. It is the better candidate when the prompt contains many interacting constraints, the asset is headed into a professional production pipeline, or a quality requirement is more important than efficiency. The available evidence does not provide a price, latency target or independent quality test, so the decision should be validated in the channel where you will deploy it.
A practical selection framework
Answer these questions in order:
- Are you selecting a model for the Gemini API? If yes, remove Imagen from the current shortlist because Google documents it as shut down there.
- Do you need repeated edits, several references or search grounding? Start with Nano Banana 2.1.
- Is high-volume speed and cost the overriding constraint? Evaluate Nano Banana 2 Lite, while accepting its limitations with references and sequential editing.
- Are the instructions unusually complex or the asset professionally critical? Evaluate Nano Banana Pro.
- Are you maintaining an existing Nano Banana 2 integration? Test 2.1 against your prompts and output checks before migrating; Google recommends 2.1 for new projects, but existing applications may have compatibility requirements.
This framework uses Google’s stated positioning. It is not a substitute for testing your own prompts, moderation rules, latency budget and output-review process.
How to start a Nano Banana 2.1 workflow
No special hardware or software is required according to the Google DeepMind model card. Access is distributed through several Google channels, and each channel can have different terms, quotas, pricing and availability.
- Select an access channel. The model card lists the Gemini app, Google AI Studio, Gemini API, Google Search AI Mode, Google Ads, Google Flow and Google Stitch. For an application, use the Gemini API; for prompt exploration, use a channel that exposes the model picker and image output.
- Choose the model. In an API integration, select
gemini-nano-banana-2.1. In a graphical product, select the Nano Banana 2.1 option if the channel exposes model names differently. - Define the deliverable before prompting. Specify subject, composition, aspect ratio, intended audience, required resolution, exact text and any elements that must not change.
- Add references by role. State which images are object references and which are character references. Keep the total within Google’s documented limit of 14, including no more than 10 object references and 4 character references.
- Generate a first version. Start with one complete prompt rather than a series of vague fragments. If factual or current context is needed, request Web or Image Search grounding and remember the restriction on real-world people from web search.
- Iterate conversationally. Make one material change per turn when possible. This makes it easier to identify whether a change affected text, layout, identity or style.
- Inspect the output. Check spelling, numbers, logos, faces, hands, object counts, reference adherence and licensing or privacy requirements before delivery.
- Choose the final resolution. Use 1K, 2K or 4K according to the destination rather than requesting the largest output by default.
Generated images include a SynthID watermark, according to Google’s guide. Plan for that provenance marker in any publication or asset-management process that needs to account for generated content.
What Google’s published evaluations actually show
Google DeepMind’s October 2026 model card reports the following scores for its Overall Preference text-to-image evaluation:
| Model configuration | Overall Preference score | Reported uncertainty |
|---|---|---|
| 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 model card reports these Infographic Factuality values:
Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problems| Model configuration | Infographic Factuality |
|---|---|
| Nano Banana 2.1 with Thinking | 0.521 |
| Nano Banana 2.1 without Thinking | 0.328 |
| Nano Banana 2 with Thinking | 0.179 |
| Nano Banana Pro | 0.265 |
These are Google-published evaluation results from 2026. Overall Preference is an evaluation metric, not a universal image-quality score; Infographic Factuality measures a narrower task. The table compares current Google models and contains no Imagen result. It therefore cannot answer whether Nano Banana 2.1 beats a named Imagen version in an independent, apples-to-apples test.
Thinking mode: what the figures do and do not imply
The model card reports separate Nano Banana 2.1 results with and without Thinking. In the displayed evaluation, the Thinking configuration scores higher for both Overall Preference and Infographic Factuality. That supports testing Thinking when a request has complicated layout, factual or multi-constraint requirements.
The figures do not establish a universal speed, cost or quality trade-off. Google’s published table does not state how much additional latency or usage cost Thinking creates, nor does it guarantee that every prompt benefits equally. Treat it as a configuration to evaluate for demanding tasks, not a mandatory setting for every generation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Migration guidance for Imagen-era applications
If an application was built around Imagen, begin by identifying where the model is selected and what the response parser expects. Nano Banana is a Gemini-native capability, so migration may involve more than changing a model name.
- Confirm the integration’s provider. Google’s shutdown statement applies to the Gemini API. A third-party service may have its own migration notice or may not expose the same model family.
- Inventory inputs and outputs. Record whether the old workflow accepted text only, reference images, masks, files or other controls. Nano Banana 2.1 documents text, images, video and PDFs as inputs and images and text as outputs.
- Rewrite prompts for conversational editing. Preserve fixed requirements explicitly and describe each reference image’s role. Do not assume a prompt written for an Imagen endpoint will produce the same composition or text behavior.
- Update response handling. Test how your client receives image and text outputs, including error handling, content filtering and metadata. The supplied Google documentation does not establish that every Imagen response field maps directly to a Nano Banana response field.
- Re-test resolution and review rules. Validate 1K, 2K and 4K outputs against storage, delivery and moderation requirements. Add human review for text, identity, factual graphics and rights-sensitive material.
- Measure the workflow you actually operate. Compare completion time, retry rate, reference adherence, text accuracy and per-request usage in your chosen channel. Do not substitute the model-card benchmark for deployment testing.
Availability, terms and operational caveats
The model card lists distribution through the Gemini app, Google AI Studio, Gemini API, Google Search AI Mode, Google Ads, Google Flow and Google Stitch. It also says downstream providers may access models through an API subject to applicable terms and that no required hardware or software is needed.
Those statements do not mean that every channel exposes the same model version, resolution, quota, billing or feature set at the same time. Availability and product naming are volatile. Check the terms and model list in the channel you intend to use immediately before deployment, particularly if your application depends on batch processing, search grounding, 4K output or a specific reference-image limit.
Common mistakes and how to avoid them
Assuming Imagen is still the fallback
For Gemini API work, it is not. Remove Imagen from new architecture diagrams and test Nano Banana instead. Keep any legacy reference only to document an old dependency, not as a current selection.
Treating Google’s positioning as an independent review
Descriptions such as high-efficiency, fastest, cheapest and premium come from Google. They are useful for initial routing, but they are not independent measurements. Benchmark the prompts, content types and regions that matter to your own service.
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Using too many references without assigning roles
Even within the documented 14-image ceiling, ambiguous references can produce an incoherent result. Name each image’s purpose and specify which visual properties are immutable.
Publishing generated text without proofreading
Improved text rendering does not make generated lettering infallible. Verify every label, date, price, statistic and legal notice manually.
Expecting search grounding to reproduce any person
Google’s guide excludes the use of real-world images of people from web search for grounding. Use permitted references and follow applicable privacy, copyright and publicity rules instead.
Comparing scores as if they were a universal ranking
The model-card evaluations cover defined tasks and current Google models. They do not include Imagen and do not predict every creative, technical or factual use case.
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Decision checklist
- For a new Gemini API image project, start with Nano Banana 2.1.
- Use the exact model identifier
gemini-nano-banana-2.1where the API requires one. - Move to Nano Banana 2 Lite when speed and scale dominate and reference-heavy editing is not central.
- Evaluate Nano Banana Pro for complex instructions and professional asset production.
- Use 1K, 2K or 4K according to the delivery requirement.
- Keep reference inputs within 14 total, with no more than 10 object and 4 character references.
- Use search grounding only when its current limitations fit the task.
- Proofread all generated text and review people, objects, factual claims and rights-sensitive content.
- Check channel-specific availability, quotas, pricing and terms before launch.
- Do not claim a direct Imagen benchmark advantage; Google’s published table does not include Imagen.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
