Nano Banana 2.1 is priced as a Gemini API image-generation model, not as a separate consumer subscription. Your bill depends mainly on output resolution and whether requests use Standard or Batch processing. Google lists a per-image equivalent for 1K, 2K and 4K output, while input tokens, text or thinking output, and Google Search grounding can add separate charges.
To estimate a workload, separate images by resolution, multiply each count by the matching rate, then add token and grounding costs. The result is a planning estimate rather than a guaranteed invoice because your final total depends on the actual request configuration and usage.
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What Nano Banana 2.1 is—and what you are paying for
Google identifies the model as gemini-nano-banana-2.1. Its model documentation describes it as the latest high-efficiency image-generation and conversational-editing model, with an update dated October 2026. The update emphasizes visual quality, prompt adherence, multi-turn character consistency and text rendering.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteThe model is intended for image generation and conversational editing. Depending on the access route and configuration, the documented capability surface includes text, image, video and PDF inputs; image and text outputs; image generation; search grounding; and Batch API support. Google Cloud documentation also describes generation from video input, image editing and multi-turn image editing. Check the documentation for the specific API or cloud product you are using because capability and billing details can vary by route.
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Google positions Nano Banana 2.1 as the primary high-efficiency workhorse for new image-generation projects. Nano Banana 2 Lite is aimed at speed and lower cost at scale, while Nano Banana Pro is intended for more complex visual tasks. The figures in this article apply to Nano Banana 2.1 pricing listed for the Gemini API; they do not establish prices for those other models or for a consumer Gemini plan.
Current Nano Banana 2.1 image-output rates
Google’s pricing table expresses image output as $30 per 1,000,000 image tokens for Standard processing and $15 per 1,000,000 image tokens for Batch processing. It also provides approximate per-image equivalents based on the output resolution.
| Output | Pixel dimensions | Image tokens per output | Standard image-output equivalent | Batch image-output equivalent |
|---|---|---|---|---|
| 1K | 1,024 × 1,024 | 1,120 | $0.0336 per image | $0.0168 per image |
| 2K | 2,048 × 2,048 | 1,680 | $0.0504 per image | $0.0252 per image |
| 4K | 4,096 × 4,096 | 3,780 | $0.113 per image | $0.0567 per image |
These are image-output equivalents, not an all-inclusive request price. A request can also contain billable input or text output, and a grounded request can incur search-query charges. The listed rates are the figures published by Google AI for Developers as of October 10, 2026; recheck the live pricing page before setting a production budget.
Additional charges that can change the estimate
Input tokens
For Standard processing, Google lists $1.50 per 1,000,000 text, image and video input tokens. Batch input is listed at $0.75 per 1,000,000 tokens. Prompts, reference images, video and other supported inputs can therefore add cost even when the generated image count is unchanged.
Text and thinking output
Google lists Standard text and thinking output at $7.50 per 1,000,000 tokens. The Batch equivalent is $3.75 per 1,000,000 tokens. Include this category when your application asks the model to return textual reasoning, descriptions, structured data or other text alongside an image.
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Search grounding
Google lists 5,000 free Google Web and Image Search grounding requests per month, shared across Gemini 3 and newer models. After that allowance, the listed price is $14 per 1,000 requests. Billing is based on each individual search query performed, not simply on the number of API calls. One submitted request can generate one or more queries, so a workload with 1,000 API calls could have more than 1,000 billable queries.
The formula for estimating API usage
Use separate counts for each output resolution and billing mode. A practical formula is:
Estimated API cost = Σ(image count at each resolution × matching image-output rate)
+ applicable input-token cost
+ applicable text/thinking-output cost
+ applicable grounding chargesFor each component, keep the unit consistent. Divide token totals by 1,000,000 before multiplying by the published per-million-token rate. For grounding, estimate the number of individual queries, subtract the applicable free monthly allowance, and apply the listed rate to the remaining queries.
Step 1: Define the workload
Write down how many images you expect to generate in the billing period. Split that count into 1K, 2K and 4K outputs rather than using one average resolution. A social thumbnail, a draft concept and a print-ready asset may have very different resolution requirements.
Step 2: Select the processing mode
Use the Standard column for interactive or otherwise non-Batch requests. Use the Batch column only when your implementation can submit work through Google’s Batch API and tolerate its operational workflow. Do not apply Batch rates to ordinary synchronous calls merely because the requests are sent in groups by your own code.
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Step 3: Add input and text totals
Estimate or measure the text, image and video tokens sent to the model, then add any text or thinking tokens returned. The image-output line is only one part of the invoice for multimodal or conversational applications.
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If Search grounding is enabled, instrument the application to record the number of queries actually issued. Counting top-level requests alone can understate usage when one request produces multiple searches.
Step 5: Build scenarios
Create low, expected and high cases using different image volumes, resolution mixes, token totals and query counts. This is more useful than multiplying a single optimistic image count because resolution and grounding behavior can shift as a product evolves.
Worked image-output examples
The following examples isolate the image-output component so that the arithmetic is clear. They do not include input tokens, text or thinking output, grounding, taxes or any other applicable line item.
| Workload | Calculation | Image-output estimate |
|---|---|---|
| 100 Standard 1K images | 100 × $0.0336 | $3.36 |
| 100 Batch 1K images | 100 × $0.0168 | $1.68 |
| 100 Standard 2K images | 100 × $0.0504 | $5.04 |
| 100 Standard 4K images | 100 × $0.113 | $11.30 |
| 50 Standard 1K plus 50 Standard 4K images | (50 × $0.0336) + (50 × $0.113) | $7.33 |
The mixed-resolution example demonstrates why an average price can mislead. Two projects with the same image count can have substantially different output costs when one uses mostly 4K assets.
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Choosing 1K, 2K or 4K output
1K for drafts and compact assets
At 1,024 × 1,024 pixels, 1K is the lowest listed output tier. It is the natural baseline for prototypes, thumbnails, chat previews and workflows where a later resize or selection step is expected.
2K for general-purpose deliverables
2K increases both pixel dimensions and the image-token allocation. Choose it when the asset must survive moderate cropping or display on larger screens but does not require the largest documented output.
4K for high-detail deliverables
4K uses 3,780 image tokens per output—the highest allocation in the table—and therefore has the highest per-image equivalent. Reserve it for cases where the additional detail is required by the final use, such as large-format artwork or substantial cropping. Generating every draft at 4K can multiply costs without improving the review process.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Standard versus Batch processing
Batch image-output equivalents are exactly half of the listed Standard equivalents at each resolution. That difference can be significant for large, non-interactive jobs, but suitability matters more than the percentage alone.
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- Choose Standard when a user is waiting for an immediate result, when you need an interactive editing loop, or when the application cannot use the Batch API.
- Consider Batch for scheduled catalogs, overnight generation, backfills and other queues where latency is less important and the integration supports Batch submission and result retrieval.
- Compare the complete estimate rather than only image output. Batch has lower listed input and text/thinking token rates as well, but grounding and other applicable charges still need to be included.
A queue that your code calls “batch” is not automatically Google’s Batch billing mode. Confirm the API endpoint and product configuration before applying the lower rates.
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- Google Pixel 10 is the everyday phone unlike anything else; it has Google Tensor G5, Pixel’s most powerful chip, an incredible camera, and advanced AI - Gemini built in[1]
- Unlocked Android phone gives you the flexibility to change carriers and choose your own data plan[2]; it works with Google Fi, Verizon, T-Mobile, AT&T, and other major carriers
- Unlocked Android phone gives you the flexibility to change carriers and choose your own data plan[2]; it works - Google Fi, Verizon, T-Mobile, AT&T, and other major carriers
- The upgraded triple rear camera system has a new 5x telephoto lens - up to 20x Super Res Zoom for stunning detail from far away; Night Sight takes crisp, clear photos in low-light settings; and Camera Coach helps you snap your best pics[3]
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How to track usage in a real application
- Record the model and mode. Store
gemini-nano-banana-2.1, the Standard or Batch path, and the request date for every job. - Record requested output resolution. Keep 1K, 2K and 4K as separate fields so the invoice estimate can use the correct image-token allocation.
- Count generated outputs. If a user requests several candidates, record each output rather than treating the interaction as one image.
- Capture token usage. Save input text/image/video tokens and text or thinking output tokens returned in the API usage metadata, when available.
- Capture grounding activity. Log individual search-query counts, not only the parent request ID.
- Reconcile with billing. Compare your estimate with Google’s billing data over a complete period and investigate differences caused by resolution mix, token totals or query fan-out.
This record-keeping also lets you answer which feature is driving cost: high-resolution output, reference-media input, verbose text responses or grounded searches.
Common estimation mistakes
Treating the per-image figure as the whole bill
The per-image equivalents cover image output only. Omitting prompt media, video, text output or thinking tokens can make a prototype estimate look artificially low.
Using API-call counts as search-query counts
Grounding charges attach to individual queries. Instrument query volume whenever grounding is enabled, especially for prompts that ask for several types of current information.
Applying Standard prices to a Batch forecast—or vice versa
Keep the processing mode explicit in spreadsheets and dashboards. A 1K image has different listed equivalents under the two modes, and the token rates also differ.
Budgeting with one blended resolution
Separate 1K, 2K and 4K counts. A small share of 4K outputs can materially change the total because the 4K image-token allocation is more than three times the 1K allocation.
Assuming today’s price is permanent
Google can change model availability, pricing or product configuration. Treat the October 2026 figures as a dated planning snapshot and verify the live pricing page before signing off a budget or publishing a cost calculator.
Quick Recap
A practical budgeting checklist
- Confirm that the application is using
gemini-nano-banana-2.1, not another image model. - List expected 1K, 2K and 4K output counts for the billing period.
- Mark each workload as Standard or eligible for Google’s Batch API.
- Estimate text, image and video input tokens.
- Estimate text and thinking output tokens.
- Determine whether Search grounding is enabled and forecast individual query volume.
- Apply the free monthly grounding allowance only once across the applicable Gemini 3 and newer workloads.
- Produce low, expected and high scenarios and label every assumption.
- Recheck Google’s current pricing and the documentation for your access route before deployment.
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.
