Nano Banana 2.1 vs Nano Banana 2 vs Nano Banana Pro: Which Should You Use?

Google now sells four Nano Banana image models through the Gemini API. The newest model, Nano Banana 2.1, launched on October 6, 2026 at half the per-image price of the model it replaces. That makes it the default for most teams, but not every workload.

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This guide compares Nano Banana 2.1, Nano Banana 2, Nano Banana Pro, and Nano Banana 2 Lite. Prices and limits below come from Google’s image generation documentation and pricing page as of October 7, 2026.

Quick verdict

• Default to Nano Banana 2.1 (gemini-nano-banana-2.1) for new projects. Google recommends it for new projects, and it costs half as much as Nano Banana 2 at 1K, 2K, and 4K.
• Keep Nano Banana 2 (gemini-3.1-flash-image) if you need 0.5K (512px) output. Nano Banana 2.1 does not support that size.
• Use Nano Banana 2 Lite (gemini-3.1-flash-lite-image) for high-volume 1K generation when you do not need grounding or many reference images. It has the same 1K image price as 2.1 with cheaper input tokens.
• Use Nano Banana Pro (gemini-3-pro-image) for workloads that need Google’s premium tier for professional asset production and can justify roughly four times the 1K image cost.

One caveat: Google has not published benchmarks for Nano Banana 2.1. Its launch announcement says 2.1 “outperforms our previous models across the board,” but provides no side-by-side measurements. Test your own prompts before making quality claims or migrating production traffic.

Side-by-side specs

	Nano Banana 2.1	Nano Banana 2	Nano Banana Pro
API model ID	gemini-nano-banana-2.1	gemini-3.1-flash-image	gemini-3-pro-image
Google’s positioning	Primary high-efficiency workhorse	Previous-generation workhorse	Premium choice for complex visual tasks
Resolutions	1K, 2K, 4K	0.5K, 1K, 2K, 4K	1K, 2K, 4K
Input price per 1M tokens	$1.50	$0.50	$2.00
Image output price per 1M tokens	$30	$60	$120
Per image at 0.5K	n/a	$0.045	n/a
Per image at 1K	$0.0336	$0.067	$0.134
Per image at 2K	$0.0504	$0.101	$0.134
Per image at 4K	$0.0756	$0.151	$0.24
Batch price per image at 1K	$0.0168	$0.034	$0.067
Google Search grounding	Web Search + Image Search	Web Search + Image Search	Web Search
Reference images	Up to 14: up to 10 objects, 4 characters	Up to 14: up to 10 objects, 4 characters	Up to 14: up to 6 objects, 5 characters
Video input	Yes	Yes	No
Free API tier	No; test in AI Studio	No	No

Key details behind the table:

• Output token math: A 1K image uses 1,120 output tokens, 2K uses 1,680, and 4K uses 2,520 for both 2.1 and Nano Banana 2. The token counts are the same, but 2.1 has half the output-token rate.
• Batch pricing: Nano Banana 2.1 batch prices are $0.0168 at 1K, $0.0252 at 2K, and $0.0378 at 4K.
• Thinking: Gemini 3 image models think by default, and the API does not let you disable it. Up to two interim “thought images” can be generated without charge. Text and thinking output on 2.1 costs $7.50 per 1M tokens, compared with $3 on Nano Banana 2.
• Grounding: The Gemini 3.x models share 5,000 free search requests per month. After that, search costs $14 per 1,000 requests.

Cost per 1,000 images

The following prices are output-only, standard non-batch API, paid tier.

Model	0.5K	1K	2K	4K
Nano Banana 2.1	n/a	$33.60	$50.40	$75.60
Nano Banana 2	$45.00	$67.00	$101.00	$151.00
Nano Banana 2 Lite	n/a	$33.60	n/a	n/a
Nano Banana Pro	n/a	$134.00	$134.00	$240.00

With the Batch API, Nano Banana 2.1 drops to:

• $16.80 per 1,000 images at 1K
• $25.20 per 1,000 images at 2K
• $37.80 per 1,000 images at 4K

Two comparisons are especially useful when choosing a model:

1. A 4K image from 2.1 costs $0.0756, which is less than a 1K Pro image at $0.134.
2. A 1K image from 2.1 costs $0.0336, less than Nano Banana 2’s 512px tier at $0.045. Keep Nano Banana 2 only when you specifically need 0.5K output.

When input-heavy editing changes the math

Image output is not the entire bill. Nano Banana 2.1 charges $1.50 per 1M input tokens, three times Nano Banana 2’s $0.50 per 1M input tokens.

This matters for image-editing workflows that attach many reference images or send long multi-turn histories.

According to Google’s Vertex AI model page, each input image uses 1,120 tokens.

Example: 14 reference images plus a 500-token prompt

Assume a product-shot pipeline sends 14 reference images, a 500-token prompt, and requests one 1K image.

	Nano Banana 2.1	Nano Banana 2
Input tokens: 14 × 1,120 + 500	16,180	16,180
Input cost	$0.0243	$0.0081
1K output	$0.0336	$0.0670
Total per request	$0.0579	$0.0751
Per 1,000 requests	$57.90	$75.10

Even at the 14-image limit, Nano Banana 2.1 is still cheaper. However, the savings shrink from 50% for output alone to roughly 23% for this input-heavy request.

Calculate the break-even point

For the same request:

• Nano Banana 2.1 costs $0.000001 more per input token than Nano Banana 2.
• Its output savings are:
  • $0.0334 at 1K
  • $0.0506 at 2K
  • $0.0754 at 4K

That means 2.1 becomes more expensive only above approximately:

Output size	Approximate input-token break-even point
1K	33,400 input tokens
2K	50,600 input tokens
4K	75,400 input tokens

Fourteen reference images alone do not reach those limits. However, two patterns can get closer:

1. Long multi-turn edit sessions
   
   If you chain edits with previous_interaction_id, inspect the usage data returned for every response. Earlier turns can add context tokens that you pay for. A ten-turn session that repeatedly includes full references can cross the 1K break-even point.

2. Thinking and text output
   
   Nano Banana 2.1 charges $7.50 per 1M thinking and text-output tokens, compared with $3 for Nano Banana 2. You cannot disable thinking, so prompts that trigger more reasoning narrow 2.1’s cost advantage.

For long-history 1K editing, run real production-like requests through both models and compare total usage rather than image-output price alone.

Where Nano Banana 2 Lite fits

Nano Banana 2 Lite is the budget model. Google describes it as “the fastest and cheapest Gemini image model.”

Its pricing is:

• $0.25 per 1M input tokens
• $0.0336 per 1K generated image

That gives Lite the same 1K output cost as Nano Banana 2.1 with input tokens that cost one-sixth as much.

Use Lite when your workload is straightforward:

• Plain 1K text-to-image generation
• High-volume generation
• Few or no reference images
• No Google Search grounding
• No multi-turn editing requirement

Lite only generates 1K output, does not support Google Search grounding, and Google says it is “not optimized” for multiple reference inputs or multi-turn editing.

Once you need 2K, 4K, grounding, or iterative editing, move to Nano Banana 2.1.

Google also recommends Lite as the migration path for the original Nano Banana model, gemini-2.5-flash-image, which is now legacy. See our Nano Banana 1 vs Nano Banana 2 comparison for the earlier migration.

Where Nano Banana Pro still makes sense

Google describes Pro as “the premium choice for the most complex visual tasks.” It positions Pro for professional asset production, the highest level of world knowledge, advanced localization, accurate brand consistency, and precision creative control.

Google’s prompting guide also recommends Pro for professional text-heavy assets.

Those are positioning statements, not published comparative measurements. Use them as a starting point, then validate the choice with your own prompts.

• Choose Pro if your existing Pro workflows already meet your quality bar or you depend on its five-character consistency limit.
• Test 2.1 first for volume work. At 4K, 2.1 costs about one-third as much as Pro: $0.0756 versus $0.24. At 1K, it costs about one-quarter as much.
• Account for capability differences: Pro does not support Image Search grounding or video input.

For the full Pro request format, see the Nano Banana Pro API guide.

Migration notes: Nano Banana 2 to 2.1

For most API calls, migration is a one-line model change.

The endpoint, response_format, and google_search tool remain the same:

curl -s -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": [
      {
        "type": "text",
        "text": "A studio product photo of a ceramic mug on a walnut desk"
      }
    ],
    "response_format": {
      "type": "image",
      "mime_type": "image/png",
      "aspect_ratio": "16:9",
      "image_size": "2K"
    }
  }'

Before sending production traffic to 2.1, check the following.

1. Find 512px calls

If your code sends:

{
  "image_size": "512px"
}

or requests 0.5K output, keep those requests on:

gemini-3.1-flash-image

Nano Banana 2.1 does not support that size.

2. Use uppercase K

Use:

{
  "image_size": "2K"
}

Do not use:

{
  "image_size": "2k"
}

Lowercase k is rejected on every model.

3. Configure Image Search grounding correctly

To enable both Web Search and Image Search, include:

{
  "google_search": {
    "search_types": ["web_search", "image_search"]
  }
}

Google requires you to display the returned search suggestions.

4. Update budget assumptions

For equivalent requests:

• Your input-token line item can roughly triple when moving from Nano Banana 2 to 2.1.
• Your image-output line item is cut roughly in half at 1K, 2K, and 4K.

Track both values in your billing and observability pipeline.

5. Plan for paid API usage

None of these models has a free Gemini API tier. See how to use Nano Banana 2.1 for free for testing options.

For a full walkthrough covering multi-turn editing and grounding, see the Nano Banana 2.1 API guide. For the release changes, see the Nano Banana 2.1 overview.

How to A/B test the three models in Apidog

Without published benchmarks, the useful comparison is your own prompt set, references, and acceptance criteria. Apidog lets you run the same API request against all three models without building a separate test harness.

1. Create a reusable request

Create:

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

Store your API key in an environment variable and set:

x-goog-api-key: {{GEMINI_API_KEY}}

Start with the request body from the migration example. For reference images, add image parts like:

{
  "type": "image",
  "mime_type": "image/jpeg",
  "data": "<BASE64>"
}

2. Duplicate the request for each model

Create three copies and change only the model field:

gemini-nano-banana-2.1
gemini-3.1-flash-image
gemini-3-pro-image

Keep everything else identical:

• Prompt
• Reference images
• Aspect ratio
• image_size
• Grounding configuration

This ensures the model is the only variable.

3. Send requests and compare responses

For every request, capture:

• HTTP status
• Latency
• Response size
• Usage data
• Generated base64 image

Decode the returned images and review them side by side against the same acceptance criteria.

4. Turn it into a regression test

Add checks for:

• A 200 status
• A non-empty generated image
• Expected response fields

Group the requests into a test scenario and rerun it when Google updates a model or when your prompting pipeline changes.

Run at least 10 to 20 real prompts, not one example. Then multiply each model’s measured per-request cost by your actual monthly volume. Download Apidog free to follow along.

FAQ

Is Nano Banana 2.1 better than Nano Banana 2?

Google says 2.1 “outperforms our previous models across the board,” citing visual design, mask-based editing, subject consistency, and more natural-looking images. Google has not published benchmarks, so verify this with your own prompts.

Is Nano Banana 2.1 cheaper than Nano Banana 2?

For image output, yes. It costs half as much at 1K, 2K, and 4K. For input tokens, no: 2.1 costs $1.50 per 1M tokens versus $0.50 for Nano Banana 2. For most workloads, output savings still win.

Should I replace Nano Banana Pro with 2.1?

Test first. Nano Banana 2.1 costs about one-quarter of Pro at 1K, but Google still positions Pro as its premium model.

Can I use Nano Banana 2.1 for free?

Not through the Gemini API. Google’s pricing page lists no free API tier for any Nano Banana model, though you can test 2.1 in Google AI Studio.

How many reference images can I send?

All three models support up to 14 reference images. Nano Banana 2.1 and Nano Banana 2 support up to 10 objects and 4 characters. Pro supports up to 6 objects and 5 characters.

Verdict

Nano Banana 2.1 is the practical default for new Gemini image workloads. It cuts Nano Banana 2’s per-image cost in half while keeping the same grounding, video-input, and reference-image limits.

Stay on Nano Banana 2 only when you need 512px output or when measured long-history editing costs favor it. Use Lite for simple high-volume 1K generation. Keep Pro where its premium positioning matches your production requirements, but validate that value with a side-by-side test before paying roughly four times as much per 1K image.