Nano Banana 2.1 Pricing: Check Each Resolution
Nano Banana 2.1 pricing lowers the cost of several Google image-generation workloads, but the saving differs by resolution. Google’s official Gemini API table lists $0.0336 for a standard 1K output image and about $0.113 for 4K. Therefore, the widely repeated $0.076 figure should not be used for the current standard 4K rate. Google’s pricing table is the reference checked on October 8.
Event date: October 6, 2026 · Sources checked: October 8, 2026
Nano Banana 2.1 pricing by output size
Standard image-output equivalents are $0.0336 at 1K, $0.0504 at 2K and approximately $0.113 at 4K. Batch equivalents are $0.0168, $0.0252 and $0.0567 respectively. Google prices image output at $30 per million tokens for standard requests and $15 for batch requests.
For comparison, Nano Banana 2’s listed 1K output was approximately $0.067, so the new 1K rate is roughly half. At 4K, comparing $0.113 with approximately $0.151 gives a reduction of about 25%, not 50%. Input tokens, text or thinking output and applicable grounding can add to the bill.
The stable model identifier is gemini-nano-banana-2.1. Google’s model documentation describes improved visual quality and conversational editing across supported resolutions.
How to estimate an image workload
At the listed standard rate, 1,000 successful 1K outputs have an image-output component of $33.60. This simple calculation excludes additional token charges and repeated attempts. Therefore, a production estimate should include how many generated images actually meet the acceptance criteria.
Similarly, keep standard and batch requests separate in a comparison. The cheaper batch price does not make the two delivery modes interchangeable for every workflow. Record the output resolution as well, because a single model name does not establish a single per-image cost.
Nano Banana 2.1 pricing — xpu live analysis
Our view is that cost per usable image is the most helpful comparison for creative teams. A lower output rate can help, but consistent composition and fewer rejected results may matter just as much. Test the same brief at the resolutions you expect to publish.
For example, examine subject accuracy, visual artifacts and whether the image survives the final crop. Next, measure the number of attempts and total billed components. That connects the advertised rate with the actual asset delivered.
Finally, retain the model version and pricing date in the record. Image-generation prices can change, and an old rounded figure can quickly become misleading.
Sources and further reading
Related on xpu live: AI API pricing guide.