Nano Banana 2.1: Price, API, Features and October 29 Migration Guide

Quick answer: Nano Banana 2.1 is Google’s newest Gemini image-generation model, released on October 6, 2026, with materially lower API output pricing than the model it replaces. Developers using the older Nano Banana 2 endpoint should pay particular attention to the October 29 shutdown date and test image quality, thinking level, latency and batch economics before moving production traffic.

This guide focuses on the practical questions developers and creators are searching for right now: what changed, how much the API costs, which resolutions are supported, how thinking affects cost, what the migration deadline means, and how to plan a low-risk switch.

What is Nano Banana 2.1?

Nano Banana 2.1 is the latest generation in Google’s Gemini image model family. Current launch reporting identifies the API model as gemini-nano-banana-2.1. The update is positioned as a production-focused replacement for Nano Banana 2, with lower image-output pricing and continued support for image generation and editing workflows.

The timing matters because the prior gemini-3.1-flash-image model is scheduled to shut down on October 29, 2026. That gives teams a short migration window rather than an open-ended period where both versions can be treated as interchangeable.

Nano Banana 2.1 API pricing

The biggest immediate change is price. Current launch documentation and pricing reports list the following standard output charges:

Batch generation is therefore especially attractive for workloads that do not need immediate responses, such as product-catalog art, campaign variants, synthetic training data, storyboards and overnight asset generation.

Input is currently listed at $1.50 per million tokens, while text and thinking output is listed at $7.50 per million tokens. Those token charges matter because the model can spend reasoning tokens before producing an image, so “price per image” is not always the whole bill.

Google’s live pricing page should always be treated as the source of truth before budgeting a large run: Gemini API pricing.

There is no 512px tier

Nano Banana 2.1 currently centers its image-output pricing around 1K, 2K and 4K. Teams that previously relied on a very small 512px or 0.5K generation tier should not assume the same tier exists here. If your final asset is tiny, it may still be cheaper to generate at 1K and downscale than to keep an older workflow alive purely for resolution reasons.

Thinking levels: minimal, medium and high

The model supports multiple thinking levels, commonly described as minimal, medium and high, with medium used as the default in current launch coverage. Higher reasoning effort can help on difficult compositions, multi-object edits, text-sensitive layouts and instructions with several constraints, but it can also increase token usage and latency.

A sensible production strategy is to route simple jobs to minimal or medium thinking and reserve high thinking for prompts that have already failed simpler settings. That keeps quality high without paying the maximum reasoning cost on every request.

How Nano Banana 2.1 changes real-world costs

Consider a service generating 10,000 1K images. At the listed standard output price, image output alone would be about $336 before input and thinking-token charges. Using batch pricing for the same 10,000 images would put image output around $168. That difference is large enough to change the economics of high-volume creative applications.

For 4K output, 10,000 standard images would be about $756 in image-output charges, while batch would be about $378. In other words, choosing synchronous versus batch processing can matter almost as much as prompt optimization.

Who should migrate first?

Apps already calling Nano Banana 2

These teams have the clearest deadline. They should inventory every place the old model ID appears: production code, serverless functions, notebooks, automation platforms, cached configuration, staging environments and customer-specific overrides.

High-volume image businesses

Catalog-generation tools, ad-creative platforms and content pipelines should benchmark batch mode early because the price difference can be substantial at scale.

Editing-heavy workflows

Teams using reference images, iterative edits or complex prompt constraints should test consistency rather than looking only at generation speed. A cheaper output is not a saving if it requires more retries.

Migration checklist before October 29

1. Locate the old model ID. Search your codebase and environment configuration for gemini-3.1-flash-image.
2. Create a fixed test set. Use the same prompts and reference images across both models so differences are measurable.
3. Compare quality at every resolution you sell. Check 1K, 2K and 4K separately.
4. Measure total cost, not only image output. Include input and thinking tokens plus retry rate.
5. Test all thinking levels. Find the lowest setting that reliably meets your quality bar.
6. Validate safety and moderation behavior. Production policies can be affected by model changes even when prompts stay the same.
7. Run a staged rollout. Shift a percentage of traffic first, monitor errors and quality, then increase.
8. Remove the old endpoint before the deadline. Do not leave shutdown-day migration work until October 29.

Where Nano Banana 2.1 is appearing

Launch coverage indicates the model is rolling into Google AI creation surfaces in addition to the API ecosystem. Availability can vary by product, account type and region, so developers should distinguish between “available in a Google interface” and “available to their exact API project.”

How it compares with the previous Nano Banana 2

What can go wrong during migration?

The most common mistake is changing the model ID and assuming everything else stays identical. Image models can differ in prompt interpretation, composition, text rendering, reference-image fidelity and safety behavior. Automated tests should therefore include visual review, not only HTTP success rates.

Another trap is measuring only the nominal price. If one configuration generates cheaper images but needs 25% more retries, the effective cost can erase the advertised saving. Track accepted outputs per dollar, not just generations per dollar.

Related AVARIXO coverage

If you are evaluating current Gemini changes more broadly, see our guide to Gemini Free Tier changes taking effect October 9. Developers comparing newer AI model launches may also find our Mistral Large 4 guide useful.

Frequently asked questions

When did Nano Banana 2.1 launch?

Current launch coverage places the release on October 6, 2026.

What is the Nano Banana 2.1 model ID?

Current API reporting identifies it as gemini-nano-banana-2.1. Confirm the exact identifier in your Google AI project before deploying.

When does Nano Banana 2 shut down?

The prior Nano Banana 2 model, gemini-3.1-flash-image, is reported to shut down on October 29, 2026.

Is batch generation cheaper?

Yes. Current pricing lists batch image output at roughly half the corresponding standard output price.

Does Nano Banana 2.1 support 512px output?

The current launch pricing centers on 1K, 2K and 4K outputs rather than a 512px tier.

Sources and verification

Pricing and migration details were cross-checked against Google’s live Gemini API pricing page and same-day launch reporting from Creative AI News and Proje Defteri. Because this is a same-day model release, Google may adjust documentation or availability after publication.