Nano Banana 2: Complete Specifications, Pricing, API Access & Use Cases (2026)

Nano Banana 2 is a Google image-generation model designed to balance output quality, generation speed and production cost. As per the Gate.AI model-card, it supports a 131K context window, multiple image resolutions and search-grounded generation. This guide explains its documented specifications, pricing, modalities, production uses, limitations and access considerations as of July 2026.

What is Nano Banana 2?

Nano Banana 2 is the informal name associated with Google’s Gemini 3.1 Flash Image Preview model. It belongs to the image-focused branch of the Gemini family and is intended for text-to-image generation, image editing and iterative visual refinement.

Google introduced Gemini 3.1 Flash Image on February 26, 2026, positioning it as a faster image model suited to high-volume workflows. Its Flash designation reflects an emphasis on responsiveness and efficiency rather than solely on maximum visual fidelity.

It should not be confused with the standard Gemini 2.5 Flash model , which primarily handles multimodal understanding and text generation rather than native image output. The distinction matters because models within the same Gemini family may use different endpoints, pricing units and request formats.

The Gate.AI model-card identifies the listed version as google/gemini-3.1-flash-image-preview . Because preview identifiers may change, developers should verify the active model ID before building a production integration.

What Are Nano Banana 2’s Key Specifications and Pricing?

The listed token pricing allows teams to estimate prompt-processing costs. A request using 10,000 input tokens and 2,000 billable output tokens would be calculated as follows:

Input cost: 10,000 ÷ 1,000,000 × $ 0.50 = $ 0.005

Output cost: 2,000 ÷ 1,000,000 × $ 3 = $ 0.006

Estimated total: $ 0.011

This calculation illustrates token-based pricing only. It should not be interpreted as a confirmed per-image price because the Gate.AI listing does not separately explain how image resolution, output tokens or repeated generations affect billing.

For cost forecasting, teams should measure the average number of attempts needed to produce one approved asset. A low-cost generation may become more expensive when a workflow requires repeated revisions, several output formats or 4K rendering for every draft.

What Can Nano Banana 2 Do That Makes It Useful in Production?

Nano Banana 2 supports conversational image creation. A user can generate an initial visual and then request changes to its composition, lighting, background, text placement or object arrangement without rebuilding the entire prompt.

Its resolution options can support a staged production workflow. Teams may use 512px or 1K images during concept development and reserve 2K or 4K output for approved compositions. This approach can reduce unnecessary processing while keeping final assets suitable for larger digital formats.

The model can also work with text and image references. A retailer could provide a product image and request seasonal backgrounds, while a design team could use an existing visual as the basis for layout variations. Generated product details, branding and written text should still be checked before publication.

Search grounding can help when a visual depends on recent public information. However, grounding does not guarantee that every label, map feature, number or visual claim is correct. Human verification remains necessary for factual graphics and commercial assets.

Nano Banana 2 therefore fits teams prioritizing rapid iteration. For centers centered on detailed typography workflow or reference fidelity, models such as Seedream 4.5 may provide a more relevant comparison.

What Are Nano Banana 2’s Supported Modalities?

The core workflow combines text and image inputs with text and image outputs. A user can begin with a written prompt, upload visual references where the platform permits, and continue refining the result through natural-language instructions.

Capabilities documented for Google’s provider-direct platform should not automatically be treated as confirmed Gate.AI features. File formats, image limits, grounding controls and response fields depend on the API route exposed by the platform.

Where Does Nano Banana 2 Fall Short?

The operational main limitation is the model’s preview status. Preview identifiers can be renamed, replaced or withdrawn, which creates migration risk for applications that hard-code the model ID.

The Gate.AI listing also does not independently confirm the complete image-generation request schema. Developers still need to verify accepted image formats, maximum file sizes, resolution controls, response encoding and whether the workflow is synchronous.

Visual consistency can present another limitation. Generating several images of the same person, product or environment does not guarantee identical details across every output. Teams creating campaigns or catalogs should review identity, colour, proportions and brand elements.

Image models may also struggle with dense text, small labels, complex diagrams and precise object counts. For typography-heavy production, compare the model with specialist alternatives rather than choosing solely by token price. Qwen Image 2.0 , for example, is positioned around unified generation and editing with native 2K output.

What Is Nano Banana 2 Best Used For?

Nano Banana 2 is best suited to workloads where speed and iterative control matter:

• Advertising concepts: Generate several campaign directions before selecting a final design.
• E-commerce visuals: Adapt verified product images to new backgrounds, seasons or placements.
• Social graphics: Produce multiple aspect ratios and concepts for fast-moving campaigns.
• Editorial illustrations: Convert written concepts into supporting visual assets.
• Conversational editing: Modify existing outputs through targeted follow-up instructions.
• Grounded visual drafts: Incorporate recent public context while retaining human fact-checking.

Choose Nano Banana 2 when the workflow needs rapid experimentation, multimodal prompting and flexible output resolutions. Consider another model when exact typography, long-term model stability, strict character consistency or a fully documented Gate.AI image endpoint is more important.

How Does Nano Banana 2 Compare to Other Image Models?

Nano Banana 2 is suitable when teams already use Gemini workflows or need rapid image iteration. GPT Image 1.5 may better suit applications built around OpenAI-compatible image tooling, while Qwen Image 2.0 may appeal to teams prioritizing native 2K output and unified editing.

The practical choice depends on prompt adherence, editing quality, resolution, cost per approved asset and API stability—not on a single model label.

How Do I Access Nano Banana 2 Through Gate.AI?

As per the Gate.AI model-card, the listed model ID is:

Plain google/gemini-3.1-flash-image-preview

A model listing confirms catalog and commercial information, but it does not alone verify an executable image request schema. The exact Gate.AI endpoint, media fields and response structure should therefore be checked against current documentation.

Until the model-specific Gate.AI workflow is confirmed, the following examples illustrate Google’s provider-direct Gemini API.

Python

Python import os
import genai from google
from google.genai import types
api_key = os.environ.get("GEMINI_API_KEY")
if not api_key:
    raise RuntimeError("Set the GEMINI_API_KEY environment variable.")
client = genai.Client(api_key=api_key)
response = client.models.generate_content(
    model="gemini-3.1-flash-image",
    contents="Create a minimal 4:3 diagram of an AI image workflow.",
    config=types.GenerateContentConfig(
        response_modalities=["TEXT", "IMAGE"]
    ),
)
for part in response.candidates[0].content.parts:
    if getattr(part, "inline_data", None):
        with open("nano-banana-2-output.png", "wb") as file:
            file.write(part.inline_data.data)

curl

Bash curl \
  -H "x-goog-api-key: ${GEMINI_API_KEY}" \
  -H "Content-Type: application/json" \
  -X POST \
  "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.1-flash-image:generateContent" \
  -d '{
    "contents": [{
      "parts": [{
        "text": "Create a minimal 4:3 diagram of an AI image workflow."
      }]
    }],
    "generationConfig": {
      "responseModalities": ["TEXT", "IMAGE"]
    }
  }'

These documentation-based examples are not execution-confirmed and should not be presented as Gate.AI-compatible code without verifying the current Gate.AI image workflow.

FAQs

Is Nano Banana 2 an official model name?

Nano Banana 2 is an informal name associated with Google’s Gemini 3.1 Flash Image model. Gate.AI lists the relevant preview variant under the Nano Banana 2 name.

What is its context window?

As per the Gate.AI model-card, Nano Banana 2 has a 131K-token context window.

How much does Nano Banana 2 cost?

The Gate.AI listing states $ 0.50 per million input tokens and $ 3 per million output tokens. Per-image or resolution-specific Gate.AI charges are not separately confirmed.

Can it edit existing images?

Yes. Its documented workflows include image input, generation and conversational editing. Exact Gate.AI file-handling rules should be verified before integration.

Can Nano Banana 2 generate 4K images?

Google’s provider documentation describes 4K output support alongside 512px, 1K and 2K resolutions. The corresponding Gate.AI resolution parameter remains to be confirmed.