Nano Banana 2.1 API setup: authentication, requests, and image outputs

To call Nano Banana 2.1, use the Gemini API model ID gemini-nano-banana-2.1, authenticate with a Gemini API key, send a request to https://generativelanguage.googleapis.com/v1beta/interactions, and decode the base64 image returned in interaction.output_image.data. That is the shortest reliable path from a prompt to an image file.

This guide covers REST and SDK setup, text-to-image requests, image editing, output formats, resolution and token trade-offs, reference-image limits, unsupported parameters, and the failure modes most likely to stop an integration.

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What Nano Banana 2.1 is, and which model ID to use

Google describes Nano Banana as Gemini’s native image-generation capability. For the Gemini API, the canonical model identifier is gemini-nano-banana-2.1. Use that exact string in the request’s model field; a display name or an older model ID will not select this model.

Google Cloud lists Nano Banana 2.1 as generally available with a release date of October 6, 2026. The model page was updated October 6 and the Cloud reference October 12, 2026. Availability, limits, and parameter support can change, so verify the current model page before deploying a new integration: Gemini Nano Banana 2.1 model documentation and the Google Cloud model reference.

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“Nano Banana is the name for Gemini’s native image generation capabilities.”

Nano Banana 2.1 supports image generation, conversational image editing, search grounding, thinking, and Batch API workflows according to Google’s model documentation. Those capabilities do not remove the need to configure the correct endpoint, credentials, input encoding, and output handling.

Prerequisites and an integration-path decision

Have these items ready before writing application code:

• A Google account and access to the Gemini API or the Google Cloud environment you intend to use.
• A Gemini API key for the direct Gemini API examples.
• A runtime that can send HTTPS requests and base64-decode binary data.
• An output directory where your application can write image bytes.
• If you use Google Cloud’s Agent Platform route, a Google Cloud project with billing enabled and the Agent Platform API enabled, as required by the Cloud example.

Choose the interface that matches the rest of your application:

The REST and SDK examples in Google’s image-generation documentation use the Gemini API interaction workflow. Do not assume that credentials or endpoint details from one route automatically apply to the other.

Authentication: create and protect the API key

Store the key outside source code

Put the key in an environment variable on the server or in your deployment secret manager. For local development, a shell session can export it temporarily:

export GEMINI_API_KEY='your-key-value'

Do not commit the value to a repository, embed it in browser JavaScript, or print it in application logs. A browser-visible key can be copied and used by someone else. If a key is exposed, revoke it and create a replacement in the Google API-key console.

REST authentication header

Google’s documented REST request sends the key in the x-goog-api-key header and sets Content-Type: application/json. The endpoint is:

https://generativelanguage.googleapis.com/v1beta/interactions

The header name is not interchangeable with an arbitrary bearer-token header. Keep the key in the header rather than placing it in a URL that could be recorded by a proxy or log.

SDK authentication

With an official Google GenAI SDK, create the client with the configured key before calling interactions.create. The exact package installation command and supported methods vary by language and SDK release, so follow the current language-specific example linked from Google’s image-generation guide rather than copying a generic Gemini text-generation snippet.

Make a first REST request

A minimal text-to-image request contains the model ID and a text input. This cURL example follows Google’s documented interaction endpoint and header pattern:

curl -X POST 
  'https://generativelanguage.googleapis.com/v1beta/interactions' 
  -H 'x-goog-api-key: '$GEMINI_API_KEY 
  -H 'Content-Type: application/json' 
  -d '{
    "model": "gemini-nano-banana-2.1",
    "input": "A detailed watercolor illustration of a red fox in a snowy forest"
  }'

Keep the JSON valid: the model value is a string, the input can be a simple text string, and the request must be sent as JSON. A missing key, malformed JSON, or misspelled model ID fails before image generation starts.

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Request options for aspect ratio and size

The image-generation guide documents a response_format option for image output. Use it to request an aspect ratio and an image size supported by the endpoint. The documented resolution tiers are 1K, 2K, and 4K, and the model overview identifies 1K as the default. Parameter names and nesting can differ between SDK versions, so copy the current example for your language and confirm that the selected model accepts the option.

A conceptual request with those choices looks like this:

{
  "model": "gemini-nano-banana-2.1",
  "input": "Product photo of a ceramic mug on a pale blue table",
  "response_format": {
    "aspect_ratio": "16:9",
    "image_size": "2K"
  }
}

Treat this as a settings illustration rather than a substitute for the endpoint’s current schema. If the service returns an unknown-field error, compare your payload with the live Gemini image-generation example and use the exact property names shown there.

Use the Google GenAI SDK

The SDK path removes much of the HTTP plumbing but does not change the model identifier or the output concept. Configure a client with the API key, call interactions.create, pass the model and input, then inspect the returned interaction. Google’s examples are available for several languages; install the current package and use its language-specific method signatures because SDK releases can change.

Pseudocode for the sequence is:

client = GenAIClient(api_key=ENV['GEMINI_API_KEY'])
interaction = client.interactions.create(
    model='gemini-nano-banana-2.1',
    input='A studio photograph of a blue bicycle'
)
image_bytes = base64_decode(interaction.output_image.data)
write_file('bicycle.png', image_bytes)

The names above describe the documented flow. Do not paste Python-style pseudocode into a production project without checking the current SDK’s constructor, import path, asynchronous variant, and response-object properties.

Read and save the image output

For a simple image response, Google’s examples access interaction.output_image.data. That property contains base64-encoded image data. Base64 is text representing binary bytes; writing the text itself produces a corrupt file. Decode it first, then write the resulting bytes in binary mode.

import base64

encoded = interaction.output_image.data
raw = base64.b64decode(encoded)
with open('output-image.png', 'wb') as handle:
    handle.write(raw)

Use a file extension and MIME handling strategy that matches the actual returned format. If your application receives a data URL rather than bare base64, remove the metadata prefix before decoding. Validate the decoded byte stream and handle an empty value as an unsuccessful generation rather than silently creating a zero-byte file.

When the convenience property is not enough

A simple output may expose one image through output_image, but complex responses can interleave text and images. Google warns that convenience properties do not capture all content in those responses. For an interleaved result, iterate over the interaction’s steps, identify each output part, and process image data in the order returned. Preserve accompanying text if your application needs captions, explanations, or subsequent conversational context.

Generate an image from text

Start with a precise prompt that states the subject, action, setting, visual style, composition, and any text that must appear in the image. For repeatable application behavior, keep prompt construction separate from transport code. Log a request identifier and non-sensitive settings, not the API key or the entire user payload if it contains private material.

For a one-shot generator, the flow is:

1. Validate that the requested model is gemini-nano-banana-2.1.
2. Build the text input and optional response-format settings.
3. Send the authenticated POST request or call interactions.create.
4. Check the HTTP and interaction-level result for errors.
5. Read the image data, decode base64, and save or stream the bytes.
6. Return a safe application URL or storage reference instead of exposing the raw key or response object to an untrusted client.

Edit an existing image

Nano Banana 2.1 supports conversational image editing. An editing input can combine image bytes with text instructions. Google’s documentation shows image data supplied as base64 bytes together with a MIME type, followed by an instruction such as “remove the background” or “change the jacket to green.”

Before sending an edit, confirm that the source file is in a supported format: PNG, JPEG, WebP, HEIC, or HEIF. Encode the binary file as base64, preserve the correct MIME type, and include the text instruction in the same interaction input structure shown in the current guide. A wrong MIME type can prevent the service from interpreting the bytes correctly.

For conversational edits, retain the interaction history or the prior image reference according to the SDK or endpoint pattern you use. Each new instruction should state what to change and what to preserve. For example, “Keep the person, pose, lighting, and background; replace only the red backpack with a black leather backpack.” This reduces accidental changes to identity or composition, but the model’s output is still generated rather than pixel-locked.

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Reference images, formats, and upload limits

Google says the model supports up to 14 reference images in one prompt. The model overview also describes workflows involving up to four characters and ten objects for consistency and fidelity. These are caps, not a guarantee that every image will be reproduced identically. Use the fewest references that clearly establish the desired subject, style, or object.

Do not assume that combining several files allows each file to exceed its route-specific limit. Check size before encoding; base64 increases the request payload compared with the original binary. Reject unsupported extensions early, and normalize MIME types instead of trusting a user-supplied filename.

Resolution, aspect ratio, and token trade-offs

The available output tiers are 1K, 2K, and 4K. Higher resolution can be useful for large layouts or detailed crops, but it increases output-token consumption and may increase transfer and storage costs. Google Cloud publishes these image-output estimates:

These are Google’s published specifications or estimates, not independent measurements. Google Cloud also lists a 131,072-token context window, a 32,768-token maximum output, and 1,120 tokens for each input image. Reference-heavy prompts therefore consume context even before the text instruction and generated image are counted.

Choose the smallest resolution that satisfies the downstream use. Generate at 1K while refining a prompt, move to 2K for ordinary delivery, and reserve 4K for cases where the extra detail has a clear value. If you accept user-selected settings, validate the allowed values and estimate the request’s token and storage impact before submitting it.

Parameters to avoid

Generic Gemini examples often include sampling controls, but Google Cloud lists seed, topK, logprobs, temperature, and topP as unsupported for Nano Banana 2.1. Setting any of these can produce an API error. Remove them from shared generation-option objects rather than passing a broad set of defaults to every model.

Keep model-specific configuration isolated. A text-model helper that automatically adds temperature or top-p is a common source of failures when reused for image requests. Construct an image payload explicitly, and test it against the current model reference after SDK upgrades.

Gemini API versus Google Cloud Agent Platform

The direct Gemini API is the straightforward choice when your application already uses API keys and the documented interactions endpoint. Google Cloud’s Agent Platform route can fit a project that already has Cloud billing, service configuration, storage, and deployment controls. The Cloud example requires a Google Cloud project with billing enabled and the Agent Platform API enabled.

These routes are not interchangeable labels for the same setup. Confirm which console creates the credential, which endpoint your client library calls, where quotas and billing are recorded, and whether your runtime is permitted to access the selected service. The Cloud model reference lists global availability, batch inference, and pay-as-you-go consumption, but operational access can depend on account and project configuration. Verify access in your own console before promising a feature to users.

Batch, grounding, and production workflows

Google’s model information lists Batch API support, search grounding, and thinking. Those capabilities may be useful for asynchronous image jobs, grounded prompts, or more deliberate generation, but they introduce additional request and response handling. Confirm the current operation schema and eligibility before building around them; do not assume that a synchronous image example automatically works as a batch job.

For production, add request timeouts, bounded retries for transient server or network failures, and an idempotency strategy appropriate to your application. Avoid retrying authentication or validation errors unchanged. Record status codes, request timing, selected resolution, and a correlation ID. Keep the generated bytes in controlled storage, enforce content-type and size checks, and delete temporary files when a job completes or fails.

Common errors and fixes

401 or 403 authentication errors

Confirm that the environment variable is populated in the process that sends the request, that the header is exactly x-goog-api-key, and that the key belongs to the service and project you intended. Check restrictions, expiration, and whether the selected route requires Cloud authentication instead of a Gemini API key. Never solve a key error by moving the credential into a public URL.

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404 or model-not-found errors

Use gemini-nano-banana-2.1 exactly, including punctuation and version suffix. A display name, typo, or model identifier copied from another product can select nothing. Recheck the current model page if the model’s availability or naming has changed.

400 invalid-request errors

Validate JSON syntax, the input structure, MIME type, image size, and response-format values. Remove unsupported parameters such as temperature, top-p, seed, top-k, and logprobs. If you copied a generic Gemini configuration object, inspect it for fields that this model rejects.

Image data is missing or the file will not open

Inspect the complete response before assuming generation succeeded. For a basic result, read interaction.output_image.data; for interleaved results, iterate over the interaction steps. Decode base64 once, write binary bytes, and avoid saving JSON or the encoded string under an image extension. Preserve the MIME type when selecting a file extension.

Payload too large

Check each source file against the 7 MB inline/direct-console limit or the 30 MB Cloud Storage limit. Remember that base64 expands data in transit. Reduce image dimensions or compression, use the supported Cloud Storage route when appropriate, and stay within the 14-reference-image maximum.

Security and operational checklist

• Keep the API key in a server-side environment variable or secret manager.
• Use HTTPS and the documented header; never expose the key to browser code.
• Redact credentials, raw authorization headers, and sensitive prompts from logs.
• Validate upload format, MIME type, size, and reference count before sending.
• Allow only supported model-specific parameters.
• Decode returned base64 as binary and scan or validate files before making them public.
• Set timeouts and distinguish retryable transport failures from permanent 4xx errors.
• Monitor usage, quotas, and billing in the console associated with your chosen route.
• Recheck model limits, endpoint examples, and availability when upgrading dependencies or changing regions.

Testing plan before launch

Use a small test matrix rather than one successful prompt. Test a plain text generation, each intended aspect ratio, 1K and the highest resolution you plan to offer, one supported edit input, and a multi-reference request within the 14-image limit. Deliberately test an oversized file, an unsupported MIME type, and an unsupported parameter so your application returns a useful validation message instead of a generic server error.

Verify that a saved file opens, that the extension matches the returned format, and that interleaved responses do not lose text or secondary images. Test missing, revoked, and incorrectly scoped keys without printing them in test output. If you use Cloud Storage or Agent Platform, test project billing and API enablement in the same environment where production jobs will run.

Frequently asked questions

What is the exact Nano Banana 2.1 model ID?

Use gemini-nano-banana-2.1 in the Gemini API request’s model field.

Which REST endpoint should I call?

Google’s documented interaction request uses POST https://generativelanguage.googleapis.com/v1beta/interactions with the API key in x-goog-api-key and JSON as the content type.

How do I turn the response into a PNG or other image file?

Read the base64 value in interaction.output_image.data, decode it to bytes, and write those bytes in binary mode. For complex interleaved responses, inspect the interaction steps instead of relying only on the convenience property.

Can I send an image for editing?

Yes. The documented workflow accepts image data with a MIME type together with a text instruction. Supported formats listed by Google are PNG, JPEG, WebP, HEIC, and HEIF.

How many reference images can one prompt contain?

Google lists a maximum of 14 reference images. The model overview separately describes consistency workflows with up to four characters and ten objects.

What are the image upload limits?

Google lists 7 MB per file for inline data or direct console uploads and 30 MB per file when the image comes from Google Cloud Storage.

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Which output resolutions are available?

The documented tiers are 1K, 2K, and 4K. Google identifies 1K as the default in the model overview. Higher tiers have published image-token estimates of 1,680 for 2K and 3,780 for 4K, compared with 1,120 for 1K.

Can I set temperature, top-p, or a seed?

No. Google Cloud lists seed, topK, logprobs, temperature, and topP as unsupported for this model; including them can cause an API error.

Do I need Google Cloud billing?

Direct Gemini API examples use a Gemini API key. The Google Cloud Agent Platform example requires a Cloud project with billing enabled and the Agent Platform API enabled. Choose the route that matches your account and deployment setup.

Is Nano Banana 2.1 generally available?

Google Cloud lists it as generally available with an October 6, 2026 release date. Availability, limits, and access can change, so confirm the current model and Cloud pages for your account.

Bottom line

A dependable Nano Banana 2.1 integration is a short, explicit pipeline: use gemini-nano-banana-2.1, send authenticated JSON to the interactions endpoint or use the configured GenAI SDK, keep image inputs within the documented format and size limits, and decode interaction.output_image.data before saving it. Start at 1K, add only supported response options, and move to 2K or 4K when the extra detail justifies the additional published token consumption. Keep credentials server-side, handle interleaved outputs deliberately, and verify Google’s current documentation whenever the model, SDK, or deployment route changes.

Frequently Asked Questions

What is the exact Nano Banana 2.1 model ID?

Use gemini-nano-banana-2.1 in the Gemini API request’s model field.

Which REST endpoint and authentication header are required?

Send a JSON POST request to https://generativelanguage.googleapis.com/v1beta/interactions and place the API key in the x-goog-api-key header.

How do I save the returned image?

Read interaction.output_image.data, base64-decode it, and write the resulting bytes in binary mode. Iterate over interaction steps for interleaved text-and-image responses.

Can Nano Banana 2.1 edit existing images?

Yes. Send supported image bytes with the correct MIME type together with a text editing instruction.

How many reference images and what file sizes are supported?

Up to 14 reference images are supported. Google lists 7 MB per file for inline or direct-console uploads and 30 MB per file from Cloud Storage.

Which parameters are unsupported?

Google Cloud lists seed, topK, logprobs, temperature, and topP as unsupported; including them can trigger an API error.

What resolutions can I request?

The documented tiers are 1K, 2K, and 4K. Published image-token estimates are 1,120 for 1K, 1,680 for 2K, and 3,780 for 4K.

The Bottom Line

Use gemini-nano-banana-2.1, authenticate with x-goog-api-key, POST JSON to /v1beta/interactions, and base64-decode interaction.output_image.data. Validate formats, limits, and model-specific parameters, then choose 1K, 2K, or 4K according to the required detail and published token estimates.

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