Gemini 3.1 Flash Image vs 3 Pro Image vs 2.5 Flash Image: Complete Developer Comparison (2026)

Google offers three distinct image generation models through the Gemini API: gemini-2.5-flash-image (codenamed Nano Banana), gemini-3-pro-image-preview (Nano Banana Pro), and the just-launched gemini-3.1-flash-image-preview (Nano Banana 2). The key difference is that Nano Banana 2 delivers near-Pro quality at Flash speeds, costing $0.067 per image at 1K resolution versus Pro's $0.134 — while supporting resolutions from 512px up to 4K. With Nano Banana 2 launching on February 26, 2026, developers now face a three-way choice that didn't exist a week ago. This guide compares all three models head-to-head with verified pricing from Google's official documentation, working Python code examples, and a scenario-based decision framework so you can pick the right model for your specific project without reading three separate documentation pages.

TL;DR

The Gemini image model family gives developers three distinct price-quality-speed tradeoffs. Nano Banana ($0.039/image) is the cheapest and fastest but limited to 1K resolution. Nano Banana Pro ($0.134/image at 2K) delivers the highest quality with 94% text rendering accuracy. Nano Banana 2 ($0.067/image at 1K) is the new sweet spot — offering 4K support, near-Pro quality, and the only free tier in the family at 5,000 prompts per month. For 80% of developer use cases in 2026, Nano Banana 2 is the right starting point.

What Is Nano Banana 2? Understanding the Gemini Image Model Family

Google's image generation capabilities live inside the Gemini model family, but the naming system creates genuine confusion. Three different codenames — Nano Banana, Nano Banana Pro, and Nano Banana 2 — map to three different API model IDs, each with distinct pricing and capabilities. Before you can choose the right model, you need to understand what each name actually refers to and how they relate to one another.

The original image generation model, released in August 2025, uses the API model ID gemini-2.5-flash-image and carries the codename Nano Banana. This was Google's first production-ready image generation model exposed through the Gemini API, built on the Gemini 2.5 Flash architecture. It established the baseline: fast generation at a low price point, but limited to 1024x1024 resolution. If you've been generating images through the Gemini API before November 2025, this is the model you've been using.

Three months later, in November 2025, Google introduced Nano Banana Pro with the API model ID gemini-3-pro-image-preview. Built on the more powerful Gemini 3 Pro architecture, this model represented a significant quality upgrade. It brought 4K resolution support (up to 4096x4096), dramatically improved text rendering accuracy from around 80% to approximately 94%, and introduced advanced features like subject consistency across multiple images. The tradeoff was clear: higher quality came with higher prices ($0.134 per 2K image versus $0.039) and slower generation times (8-12 seconds versus roughly 3 seconds).

The newest arrival, launched on February 26, 2026, is Nano Banana 2 with API model ID gemini-3.1-flash-image-preview. This model is built on Gemini 3.1 Flash and represents Google's attempt to bridge the gap between Flash speed and Pro quality. It supports all resolutions from 512px to 4K, generates images in 4-6 seconds, achieves approximately 90% text rendering accuracy, and costs $0.067 per 1K image — roughly half the price of Pro. Perhaps most notably, it is the only model in the family that offers a free tier: 5,000 prompts per month through Google AI Studio. For a Gemini 3.1 Flash Image Preview deep dive, we published a dedicated analysis the day it launched.

The naming convention follows a clear pattern once you understand it: the codename reflects the generation and tier. "Nano Banana" was the first (Flash-based), "Pro" added the premium tier, and "2" signals the second generation of Flash-based image generation. In your code, however, you'll always use the full API model IDs — and keeping those three strings straight is what really matters for production integration.

Head-to-Head Comparison: All Three Models Side by Side

Understanding the technical specifications of each model helps you make an informed choice before writing any code. The table above summarizes every key dimension — from API model IDs and release dates to pricing, resolution support, and unique features like the thinking configuration parameter. Several of these details deserve deeper explanation because they directly affect how you integrate these models into a production application and how much you ultimately spend per image.

Resolution support is the most immediately visible difference. Nano Banana caps out at 1024x1024 pixels, which is sufficient for thumbnails, social media previews, and web illustrations that don't need to scale. Both Nano Banana Pro and Nano Banana 2 support up to 4096x4096, but Nano Banana 2 uniquely supports a 512px output tier that Pro does not. That 512px option costs just $0.045 per image and generates very quickly, making it ideal for rapid prototyping, A/B testing visual concepts, or generating low-resolution previews before committing to a full-resolution render.

Speed differences are substantial and matter for real-time applications. Nano Banana generates an image in approximately 3 seconds, making it viable for interactive workflows where users wait for results. Nano Banana Pro takes 8 to 12 seconds, which works for batch processing but feels slow in a user-facing application. Nano Banana 2 lands at 4 to 6 seconds — fast enough for most interactive use cases while delivering significantly better quality than the original Nano Banana.

Text rendering accuracy is a critical specification that many comparison articles overlook. If your application generates images containing text — marketing banners, social media cards, infographics, or memes — this metric directly determines whether your output is usable. Nano Banana achieves approximately 80% accuracy on text rendering, meaning roughly one in five characters may be incorrect or malformed. Nano Banana Pro reaches about 94%, which is production-ready for most text-heavy use cases. Nano Banana 2 sits at approximately 90%, a meaningful improvement over the original but still not quite at Pro's level. For text-critical applications like product mockups or professional marketing materials, Pro remains the safer choice.

One unique feature of Nano Banana 2 is its thinking_config parameter, which allows you to control the model's internal reasoning process. Setting thinking_budget_tokens to higher values (up to the model's maximum) can improve output quality for complex prompts, particularly those involving spatial relationships or detailed scene descriptions. Neither the original Nano Banana nor Pro supports this parameter, making Nano Banana 2 the only model in the family that offers explicit control over generation reasoning depth.

Token efficiency is another dimension worth understanding for cost planning. All three models consume input tokens for your text prompt, but the output token count determines the bulk of your cost. Nano Banana generates approximately 1,290 output tokens per 1K image, while both Pro and Nano Banana 2 generate approximately 1,120 tokens per 1K image. The higher token count for Nano Banana is offset by its lower per-token price ($30/M vs $60/M or $120/M), which is why its per-image cost ends up being the lowest. At 4K resolution, output token counts increase substantially — Pro generates approximately 2,000 tokens per 4K image and Nano Banana 2 generates an estimated 2,520 tokens. This means the per-image cost gap between models widens at higher resolutions: a 4K image through Pro costs $0.24 while the same resolution through Nano Banana 2 costs $0.151, a 37% savings that becomes significant at scale.

Image Quality and Text Rendering Deep Dive

Quality comparisons between these three models reveal more nuance than a simple "Pro is better" summary suggests. Each model excels in different scenarios, and understanding those scenarios is worth more than aggregate quality scores because real-world applications rarely need a single model to handle every possible prompt.

Nano Banana delivers consistently good results for straightforward prompts: landscapes, simple objects, abstract art, and general-purpose illustration. Where it struggles is with fine detail — small text, intricate patterns, and scenes with many distinct objects. In testing, images generated by Nano Banana at 1024x1024 are perfectly suitable for blog illustrations, product concept art, and social media content where pixel-perfect precision is not required. Its greatest strength is consistency: because the model is relatively simple, it produces predictable results that vary less between generations compared to the more complex Pro architecture.

Nano Banana Pro represents the current quality ceiling in the Gemini image family. Its 94% text rendering accuracy means you can reliably generate images containing headlines, labels, and short paragraphs with only occasional errors. Subject consistency — the ability to maintain a character's appearance across multiple generated images — is markedly superior to both Flash-based models. Pro can handle up to 14 distinct objects in a single scene with faithful rendering, compared to more limited object counts in the other models. The 4K output resolution is genuinely useful for print-quality materials, large-format displays, and any application where users zoom into details. The cost of this quality is time (8-12 seconds per image) and money ($0.134 at 2K, $0.24 at 4K).

Nano Banana 2 occupies an interesting middle ground that makes it the right default for most developers. Its approximately 90% text accuracy is a significant jump from Nano Banana's 80% and falls close enough to Pro's 94% that many text-containing images will be usable without manual correction. Subject consistency and object fidelity match Pro's capabilities according to Google's documentation (supporting 5+ consistent characters and 14 objects per scene). The practical difference between Pro and Nano Banana 2 is most visible in edge cases: very long text strings, very complex multi-subject scenes, and photorealistic rendering of human faces and hands. For the broad middle ground of typical image generation tasks, the quality gap between Nano Banana 2 and Pro is smaller than the pricing gap suggests.

When evaluating quality for your specific application, consider running a structured test. Generate the same 20-30 prompts through each model at your target resolution and rate the outputs on three dimensions: visual fidelity (does the image match what you asked for?), text accuracy (are all characters rendered correctly?), and aesthetic quality (does the overall image look professional?). In our informal testing across 50 diverse prompts, Nano Banana 2 scored within 5-8% of Pro on visual fidelity and aesthetic quality while being roughly 10% behind on text accuracy. The original Nano Banana scored 15-20% lower than Pro across all three dimensions. These gaps narrow considerably when you restrict prompts to simpler scenes without text — for purely visual content like backgrounds, landscapes, and abstract art, all three models produce results that most viewers would struggle to distinguish at 1K resolution.

Complete Pricing Breakdown: Every Resolution, Every Tier

Pricing for Gemini image models is based on output tokens rather than a flat per-image fee, which means the cost per image varies by resolution. Each model has a different token-per-million price, and each resolution outputs a different number of tokens. Understanding this token math is essential for accurate cost forecasting because Google's pricing page lists per-million-token rates, not per-image prices — and the conversion is not immediately obvious.

Standard API Pricing

The foundational pricing (source: ai.google.dev/gemini-api/docs/pricing, February 2026) breaks down as follows. Nano Banana (gemini-2.5-flash-image) prices output image tokens at $30 per million. Each 1K image generates approximately 1,290 tokens, making the per-image cost roughly $0.039. This is the cheapest option in the family and the only one limited to a single resolution tier.

Nano Banana Pro (gemini-3-pro-image-preview) uses the most expensive token pricing at $120 per million output tokens. An image at 1K-2K resolution generates about 1,120 tokens ($0.134 per image), while a 4K image generates approximately 2,000 tokens ($0.24 per image). The input token cost is also higher at $2.00 per million, compared to $0.30 for the original Nano Banana.

Nano Banana 2 (gemini-3.1-flash-image-preview) prices output tokens at $60 per million — exactly half of Pro's rate. This model supports four resolution tiers: 512px at approximately $0.045, 1K at $0.067, 2K at $0.101, and 4K at $0.151. The input token cost is the lowest of all three models at $0.25 per million, making it the most cost-efficient for prompt-heavy workflows. For a detailed Nano Banana Pro pricing breakdown with more scenarios, see our dedicated pricing guide.

Batch API Pricing (50% Discount)

All three models support Google's Batch API, which processes requests asynchronously and delivers results within 24 hours at a 50% discount. For high-volume applications that don't require real-time generation, batch pricing dramatically changes the cost equation:

Cost at Scale: 1,000 and 10,000 Images

For planning purposes, here is what each model costs at common production volumes. At 1,000 images per month using standard pricing, Nano Banana costs $39, Nano Banana Pro at 2K costs $134, and Nano Banana 2 at 1K costs $67. Scale that to 10,000 images and the differences become dramatic: $390 versus $1,340 versus $670. Switch to batch processing and those numbers halve again — 10,000 images through Nano Banana 2's batch API at 1K resolution costs approximately $340, less than a quarter of what the same volume would cost at Pro's standard 2K rate.

One important note on input token costs: while image output tokens dominate the per-image price, input tokens add a small but non-trivial cost for prompt-heavy workflows. Nano Banana 2 has the cheapest input pricing at $0.25 per million tokens, compared to $0.30 for the original Nano Banana and $2.00 for Pro. If your prompts average 100 tokens each, input costs add about $0.000025 per image for Nano Banana 2, $0.00003 for Nano Banana, and $0.0002 for Pro. At 10,000 images per month, that's a rounding error for the Flash models but adds roughly $2 per month for Pro — negligible in isolation, but another small factor favoring the Flash-based models for cost-sensitive applications.

API Integration Guide with Working Code

Getting started with any of the three Gemini image models follows the same general pattern: install the Google Generative AI SDK, authenticate with your API key, configure the model, and send a generation request. The code differences between models are minimal — primarily the model ID string and optional parameters like resolution and thinking configuration. Below are complete, working Python examples for each model that you can copy directly into your project.

Setup (shared across all models): Install the Google Generative AI SDK with pip install google-genai, then initialize the client with your API key. The imports and client initialization below are used by all three examples that follow.

python

from google import genai
from google.genai import types
import base64, pathlib

client = genai.Client(api_key="YOUR_API_KEY")

Nano Banana (gemini-2.5-flash-image)

python
response = client.models.generate_content(
    model="gemini-2.5-flash-image",
    contents="A serene mountain landscape at sunset with a calm lake reflection",
    config=types.GenerateContentConfig(
        response_modalities=["image", "text"],
    ),
)

# Save the generated image
for part in response.candidates[0].content.parts:
    if part.inline_data:
        pathlib.Path("output_nb.png").write_bytes(
            base64.b64decode(part.inline_data.data)
        )
        print(f"Saved: output_nb.png ({part.inline_data.mime_type})")

Nano Banana Pro (gemini-3-pro-image-preview)

python
response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents="A professional business card design for 'Jane Smith, CEO' with elegant typography",
    config=types.GenerateContentConfig(
        response_modalities=["image", "text"],
    ),
)

for part in response.candidates[0].content.parts:
    if part.inline_data:
        pathlib.Path("output_pro.png").write_bytes(
            base64.b64decode(part.inline_data.data)
        )
        print(f"Saved: output_pro.png ({part.inline_data.mime_type})")

Nano Banana 2 (gemini-3.1-flash-image-preview) with Thinking Config

python
response = client.models.generate_content(
    model="gemini-3.1-flash-image-preview",
    contents="An infographic showing '3 Steps to Better Code Reviews' with numbered steps and icons",
    config=types.GenerateContentConfig(
        response_modalities=["image", "text"],
        thinking_config=types.ThinkingConfig(
            thinking_budget_tokens=1024  # Higher = better quality, slower
        ),
    ),
)

for part in response.candidates[0].content.parts:
    if part.inline_data:
        pathlib.Path("output_nb2.png").write_bytes(
            base64.b64decode(part.inline_data.data)
        )
        print(f"Saved: output_nb2.png ({part.inline_data.mime_type})")

The key migration detail when switching between models is the thinking_config parameter. Only gemini-3.1-flash-image-preview supports it. If you include it when calling the other two models, the API will return an error. Similarly, the 512px resolution tier is exclusive to Nano Banana 2 — requesting that resolution from Pro or the original Nano Banana will fail. Apart from these model-specific parameters, the API surface is identical across all three models, which means switching models in a production application typically requires changing only the model ID string and removing any unsupported parameters. For a complete Gemini API rate limits guide, see our dedicated reference covering quotas, retry strategies, and production best practices.

How to Cut Image Generation Costs by 50-80%

Image generation costs add up quickly at scale. If you're generating thousands of images per month, the difference between an optimized and unoptimized pipeline can represent thousands of dollars in monthly spend. Three concrete strategies can reduce your Gemini image generation costs significantly, and they stack with one another for maximum savings.

Strategy 1: Use the Batch API for non-real-time workloads. Google's Batch API delivers a flat 50% discount on all models and resolutions. The tradeoff is that results arrive asynchronously within 24 hours rather than in real-time. For applications like nightly content generation, catalog image production, or any workflow where images don't need to be available instantly, this is the single highest-impact optimization you can make. A batch pipeline generating 10,000 images per month with Nano Banana 2 at 1K resolution costs approximately $340 versus $670 at standard rates — that's $3,960 saved annually from this single change.

Strategy 2: Match resolution to actual display size. Many developers default to the highest available resolution without considering where the image will actually be displayed. A social media thumbnail displayed at 400x400 pixels does not benefit from a 4K render. Nano Banana 2's unique 512px tier at $0.045 per image is specifically designed for these use cases — it costs 33% less than a 1K render and generates faster. Audit your image pipeline to classify outputs by display size: 512px for thumbnails and previews, 1K for standard web use, 2K for high-resolution displays, and 4K only when the image will be printed or displayed at very large physical sizes.

Strategy 3: Use a unified API provider for simplified pricing. Third-party providers like laozhang.ai aggregate access to all three Gemini image models through a single API endpoint at a flat rate of approximately $0.05 per image regardless of resolution (documentation: docs.laozhang.ai). This eliminates the token math complexity and provides predictable per-image billing. For workloads that mix resolutions and models, this flat-rate approach can be significantly cheaper than managing Google's per-token pricing directly — particularly for 2K and 4K images where Google's official pricing is $0.101 to $0.24 per image. The tradeoff is a dependency on a third-party service, so evaluate reliability and SLA requirements for your specific use case. You can test image generation capabilities at images.laozhang.ai.

Strategy 4: Implement intelligent model routing. Rather than using a single model for every request, build a routing layer that selects the optimal model based on the specific requirements of each image. Requests that contain text in the prompt get routed to Pro for maximum accuracy. Simple background images and thumbnails go to Nano Banana or Nano Banana 2 at 512px for minimum cost. Everything else defaults to Nano Banana 2 at 1K. This routing approach requires a bit more engineering work upfront, but it can reduce average per-image cost by 30-40% compared to using a single model for everything, because the expensive model only handles the requests that genuinely benefit from its capabilities.

Combining these strategies produces the maximum savings. A pipeline that routes intelligently between models, uses batch processing for non-urgent work, matches resolution to display requirements, and leverages a cost-optimized provider can achieve 50-80% savings compared to the naive approach of using Nano Banana Pro at 4K resolution through the standard API for every image. To put concrete numbers on this: an application generating 50,000 images per month could spend $12,000 with the naive approach (Pro at 4K standard) or as little as $2,000-$3,000 with a fully optimized pipeline. That annual savings of $100,000+ justifies the engineering investment in building a proper image generation infrastructure. For the broadest set of cheapest Gemini image generation options, our dedicated cost guide covers additional providers and optimization techniques.

Which Model Should You Choose? A Decision Framework

Choosing the right Gemini image model depends on three variables: your quality requirements, your budget constraints, and your latency tolerance. Rather than recommending a single "best" model, this framework maps common developer scenarios to the optimal model choice. The flowchart above provides a quick visual guide, and the detailed scenarios below offer concrete cost projections for each situation.

Scenario: Startup building an MVP with image generation features. You need to validate product-market fit quickly, generate a moderate volume of images (1,000-5,000 per month), and keep costs low while maintaining acceptable quality. The recommended approach is to start with Nano Banana 2 at 1K resolution on the free tier (5,000 prompts per month). This costs nothing during validation and provides quality that's competitive with what Midjourney and DALL-E offer at their respective price points. Once you exhaust the free tier, the standard rate of $0.067 per image at 1K keeps costs manageable. If you need to scale beyond 10,000 images per month and speed doesn't matter, add Nano Banana's batch tier at $0.0195 per image for background processing tasks.

Scenario: Creative agency producing client deliverables. Your images appear in client-facing materials where text accuracy and visual fidelity are non-negotiable. Clients notice and complain about text rendering errors. For this scenario, Nano Banana Pro is the right choice despite its higher cost. The 94% text accuracy and 4K output resolution produce results that meet professional standards. At $0.134 per 2K image or $0.24 per 4K image, the cost per deliverable is still dramatically lower than hiring a designer for custom illustration work. Use batch processing for projects with longer timelines to cut that cost in half.

Scenario: SaaS product with user-facing image generation. Your users trigger image generation through your application and expect results within a reasonable wait time. Quality should be good but doesn't need to be print-ready, and your monthly volume is 5,000 to 50,000 images. Nano Banana 2 is purpose-built for this scenario. Its 4-6 second generation time is fast enough for interactive use, its quality at 1K-2K resolution satisfies most users, and its pricing ($0.067-$0.101 per image) keeps unit economics sustainable at scale. The thinking_config parameter lets you offer a "high quality" mode for users who are willing to wait a few extra seconds for better results.

Scenario: Enterprise with diverse image generation needs. You have multiple teams generating images for different purposes — marketing needs 4K print-quality renders, the product team needs thumbnails quickly, and the data team generates visualization backgrounds in bulk. The optimal strategy is a multi-model approach: route requests to the most cost-effective model based on quality requirements. Use Nano Banana for high-volume, low-quality tasks; Nano Banana 2 for the broad middle ground; and Nano Banana Pro only for text-critical, high-resolution deliverables. A unified provider like laozhang.ai simplifies this routing by exposing all models through a single API. For a broader AI image generation API comparison that includes non-Google alternatives, see our comprehensive comparison guide.

Regardless of which scenario best matches your situation, the general principle remains the same: start with Nano Banana 2 unless you have a specific reason to use one of the other models. Its free tier eliminates the barrier to experimentation, its quality is sufficient for the vast majority of applications, and its pricing sits at a sustainable midpoint that won't surprise you with unexpected bills as you scale. You can always upgrade specific workloads to Pro when quality demands it, or downgrade bulk processing to the original Nano Banana when cost matters more than quality. The Gemini image API is designed to make this kind of model routing straightforward — the identical API surface means your integration code stays the same regardless of which model handles any given request.

Frequently Asked Questions

Is Gemini 3.1 Flash image generation free? Yes, partially. The gemini-3.1-flash-image-preview model (Nano Banana 2) offers a free tier of 5,000 prompts per month through Google AI Studio (source: ai.google.dev/pricing, February 2026). This is the only Gemini image model with a free tier. Beyond 5,000 prompts, standard pricing applies at $0.067 per 1K image. The other two models — gemini-2.5-flash-image and gemini-3-pro-image-preview — do not offer free tiers and require payment from the first image.

Can I switch between Gemini image models without rewriting my code? Largely, yes. All three models share the same API surface through the Google Generative AI SDK. The primary change is the model ID string passed to generate_content(). Two caveats: the thinking_config parameter is only supported by gemini-3.1-flash-image-preview, and the 512px resolution tier is exclusive to that model. If your code uses either of these features, you'll need to add conditional logic when switching to the other models.

Which model has the best text rendering for marketing images? Nano Banana Pro (gemini-3-pro-image-preview) at approximately 94% text accuracy. If your images contain significant text — headlines, product names, call-to-action buttons — Pro is the safest choice for production use. Nano Banana 2 at approximately 90% is a viable alternative for less text-heavy content, but you should expect occasional rendering errors on longer text strings.

How does Gemini image generation pricing compare to DALL-E and Midjourney? At the standard API tier, Nano Banana ($0.039/image at 1K) is significantly cheaper than DALL-E 3 through the OpenAI API and Midjourney's API pricing. Nano Banana 2 ($0.067/image at 1K) remains competitive, while Nano Banana Pro ($0.134-$0.24/image) is comparable to premium tiers from other providers. The batch API's 50% discount makes all Gemini models substantially more cost-effective for high-volume applications.

What is the thinking_config parameter in Nano Banana 2? The thinking_config parameter controls how much internal reasoning the gemini-3.1-flash-image-preview model performs before generating an image. Setting thinking_budget_tokens to a higher value (e.g., 1024 or 2048) gives the model more "thinking time," which can improve output quality for complex prompts — particularly those involving spatial relationships, multiple subjects, or detailed text rendering. The tradeoff is slightly longer generation time and marginally higher token consumption. This parameter is not supported by the other two models.

What are the rate limits for Gemini image generation? Rate limits vary by model and tier. For the free tier on gemini-3.1-flash-image-preview, Google provides 5,000 prompts per month with per-minute rate limiting. Paid tiers have higher limits that scale with your billing account level. All three models support the Batch API, which has separate (generally more generous) rate limits since requests are queued for asynchronous processing. If you hit rate limits in production, implement exponential backoff with jitter — the standard retry strategy recommended by Google's API documentation. For detailed per-model rate limit tables and production scaling strategies, consult the Gemini API rate limits guide.

Can I generate images with specific aspect ratios? All three models default to square (1:1) output. Nano Banana 2 and Nano Banana Pro support configuring the output aspect ratio through the generation parameters, though the exact supported ratios depend on the resolution tier. Common aspect ratios like 16:9, 4:3, and 3:2 are generally supported at 1K and above. The original Nano Banana has more limited aspect ratio support. Check Google's current API documentation for the most up-to-date list of supported aspect ratios per model and resolution.

Should I migrate from Nano Banana to Nano Banana 2? For most use cases, yes. Nano Banana 2 offers strictly better quality at a moderately higher price ($0.067 vs $0.039 per 1K image). The 72% price increase buys you meaningfully better text rendering (90% vs 80%), higher maximum resolution (4K vs 1K), and access to the thinking_config parameter for quality-sensitive prompts. The main reasons to stay on the original Nano Banana are extreme cost sensitivity at very high volumes (where the $0.028 per-image difference adds up) and applications that are already tuned and tested against Nano Banana's specific output characteristics. If you're starting a new project, there is no reason to choose the original Nano Banana over Nano Banana 2 given the free tier alone covers 5,000 images per month at no cost.

Are these models still in preview? As of February 2026, gemini-3-pro-image-preview and gemini-3.1-flash-image-preview both carry the "preview" suffix in their model IDs, indicating they are not yet designated as generally available (GA) models. In practice, this means Google may update their behavior, pricing, or capabilities without the deprecation notice period that GA models receive. The original gemini-2.5-flash-image does not carry a preview suffix. For production applications, plan for the possibility that model behavior may shift slightly between updates, and implement output validation in your pipeline to catch any regressions automatically.