Nano Banana 2 vs Pro: 30 paired images, timing and cost

Runbo Li
Runbo Li
·
· 6 min read
Nano Banana 2 vs Nano Banana Pro benchmark — hero

Quick answer

In fifteen paired 1K requests through Magic Hour on July 21, 2026, Nano Banana 2 averaged 15.5 seconds and Nano Banana Pro averaged 30.8 seconds from request submission to the first completion poll. They charged 100 and 150 credits per image in that run. These are historical timing and billing observations; the output pairs are unscored, so this study does not establish which model makes better images.

A quiet glass observatory above a cloud layer at blue hour, warm interior lights, one telescope, cinematic realistic lighting, balanced composition, fine architectural detail, no people, text, or logos.

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Magic Hour publishes this benchmark and supplied the platform route and credits used for the paired runs. The retained 30 images, prompts, timings, and recorded costs support the findings below; the result is limited to the tested model versions and settings rather than a universal quality ranking.

What did we measure?

Five prompts covered a Lisbon street scene, typography, a five-subject composition, watercolor illustration, and a labeled diagram. Each prompt ran three times per model at 1K, 1:1, one image per request. Requests were serial and interleaved by model. Three additional Nano Banana 2 requests used 640px, and one request used the default router: 34 requests in total, all completed.

The collector polled every two seconds. Reported time includes the Magic Hour request path, queue, generation, and polling delay; it is not raw Gemini inference latency. The public benchmark repository contains the prompts, code, request log, responses, and output images.

Timing results at 1K

Model (1K, n=15)

Mean

Median

Min

Max

nano-banana-2

15.5s

13.9s

11.5s

27.3s

nano-banana-pro

30.8s

29.1s

24.6s

48.2s

All 15 paired benchmark runs at 1K, grouped by prompt: each line connects Nano Banana 2's time to Nano Banana Pro's time for the same prompt and repeat. The two clusters barely overlap around means of 15.5 and 30.8 seconds, and the one pair Pro won is the photoreal repeat where Nano Banana 2 ran slow

Pro was slower in 14 of the 15 paired requests. The single exception was a photoreal repeat: 27.3 seconds for Nano Banana 2 and 25.6 for Pro. These observations describe one day's small sample, not a service-level promise.

Every paired request

Prompt

Repeat

nano-banana-2 (s)

nano-banana-pro (s)

photoreal

1

15.2

29.6

photoreal

2

27.3

25.6

photoreal

3

13.7

26.9

typography

1

15.1

31.3

typography

2

23.5

24.6

typography

3

12.4

41.6

five-subject

1

11.5

48.2

five-subject

2

15.8

25.0

five-subject

3

13.6

26.1

illustration

1

12.9

28.5

illustration

2

13.7

31.2

illustration

3

13.9

27.1

diagram

1

13.8

37.8

diagram

2

16.0

29.1

diagram

3

13.9

29.1

Does the prompt change the gap?

Prompt

nano-banana-2 mean (s)

nano-banana-pro mean (s)

Pro / NB2

photoreal

18.7

27.4

1.46x

typography

17.0

32.5

1.91x

five-subject

13.6

33.1

2.43x

illustration

13.5

28.9

2.14x

diagram

14.6

32.0

2.20x

Mean generation seconds per prompt type at 1K: Nano Banana 2 stays between 13.5 and 18.7 seconds across all five prompts while Nano Banana Pro ranges 27.4 to 33.1, with the Pro-to-NB2 ratio running from 1.46x on photoreal to 2.43x on the five-subject composition

The prompt-level ratios ranged from 1.46 to 2.43 in these three-repeat groups. That is too little evidence to predict the speed gap for a future workload from its subject matter. The three 640px Nano Banana 2 runs averaged 17.3 seconds; this small sample does not establish a resolution-speed relationship.

Actual images from the first paired attempt

The images below are prompt 1, repeat 1, chosen because they are the first pair in the log—not because they were selected as the best outputs. The prompt requested a photorealistic Lisbon street scene with a yellow Tram 28, cobblestones, balconies, laundry, and golden-hour light.

Nano Banana 2 output for the first Lisbon tram prompt, repeat one
Nano Banana 2: 15.2 seconds, 100 credits
Actual Nano Banana 2 benchmark output, July 21, 2026: prompt 1, repeat 1, 1K. First paired attempt, not a selected quality winner. Source: https://github.com/maniculehq/image-benchmark-mh.
Nano Banana Pro output for the first Lisbon tram prompt, repeat one
Nano Banana Pro: 29.6 seconds, 150 credits
Actual Nano Banana Pro benchmark output, July 21, 2026: prompt 1, repeat 1, 1K. First paired attempt, not a selected quality winner. Source: https://github.com/maniculehq/image-benchmark-mh.

Inspect composition, requested details, text, and unwanted additions in the full image collection. The study has no completed human quality scorecard. A preference for this pair is not a general ranking, and the generation prompts do not evaluate localized image editing.

What did each image cost?

Every measured 1K Nano Banana 2 request charged 100 Magic Hour credits; every 1K Pro request charged 150. The three 640px Nano Banana 2 requests also charged 100 each. These are the recorded July 21 charges, not a flat rate for every output size, provider, or future request.

The historical comparison chart below should be read with that scope. Its Google price figures describe a different provider and are not measured Magic Hour invoices.

Google's list price per image by resolution: Nano Banana 2 runs $0.045 at 0.5K up to $0.151 at 4K while Pro is $0.134 at both 1K and 2K and $0.240 at 4K, so the price ratio falls from 2.0x at 1K to 1.33x at 2K — and Pro has no bar at 0.5K because it refuses anything below 1K

For current purchasing, check Magic Hour's model reference and the quote for your request. The reference checked September 9, 2026 lists Nano Banana 2 from 100 credits and Pro from 150; higher resolutions and additional images can change the charge. Nano Banana 2 supports 640px, 1K, 2K, and 4K; Pro starts at 1K. The current catalog lists both on paid Creator, Pro, and Business tiers. The benchmark's earlier account restriction does not establish today's plan-wide resolution limits.

For Google's standard direct-API billing, use the Nano Banana Pro pricing guide and its primary pricing source. Keep input charges, output size, billing mode, and provider distinct.

How should you choose for your workload?

Requirement

Practical next step

Output below 1K

Evaluate Nano Banana 2; Pro's documented floor is 1K

Repeated 1K generation with a tight budget

Start by comparing Nano Banana 2 against your acceptance criteria

Exact typography, composition, or product fidelity

Review repeated outputs from both; this benchmark has no quality winner

Localized editing

Compare the image-editor workflow with permitted references; this generation study does not measure editing fidelity

2K or 4K output

Check current quotes and compare at the actual delivery size

Record accepted outputs and rejection reasons. A lower charge per candidate is useful only if enough candidates meet your brief. Do not infer that underlying Gemini features such as document input or search grounding are exposed by a third-party endpoint; use that endpoint's documented fields.

Use both models through one API

The measured requests used Magic Hour's image-generation API. The paired requests kept the prompt, image count, aspect ratio, and resolution fixed and changed the model field from nano-banana-2 to nano-banana-pro.

Use Magic Hour's image generator for an interactive comparison. Choose Pro when its observed output better satisfies your brief, not because this timing study proved an accuracy advantage. Verify the current quote before increasing resolution or batch size.

Existing readers using the article's API signup link should confirm any promotional offer at checkout; this benchmark does not verify a current discount.

Does Nano Banana 2 replace Nano Banana Pro?

They are separate choices: Nano Banana 2 corresponds to Gemini 3.1 Flash Image and Nano Banana Pro to Gemini 3 Pro Image. The current Magic Hour catalog lists both. This study supports a narrower conclusion: Nano Banana 2 was faster and charged fewer credits in the tested 1K requests. It does not establish that one replaces the other for every task.

Sources and verification

The request log, exact prompts, and all output images preserve the experiment. The timing summary was recomputed from the published log during this September 9, 2026 update. No new generations or human quality scores were added.

Frequently asked questions

Five prompts covered a Lisbon street scene, typography, a five-subject composition, watercolor illustration, and a labeled diagram. Each prompt ran three times per model at 1K, 1:1, one image per request. Requests were serial and interleaved by model. Three additional Nano Banana 2 requests used 640px, and one request used the default router: 34 requests in total, all completed.

The collector polled every two seconds. Reported time includes the Magic Hour request path, queue, generation, and polling delay; it is not raw Gemini inference latency. The public benchmark repository contains the prompts, code, request log, responses, and output images.

Every measured 1K Nano Banana 2 request charged 100 Magic Hour credits; every 1K Pro request charged 150. The three 640px Nano Banana 2 requests also charged 100 each. These are the recorded July 21 charges, not a flat rate for every output size, provider, or future request.

The historical comparison chart below should be read with that scope. Its Google price figures describe a different provider and are not measured Magic Hour invoices.

Record accepted outputs and rejection reasons. A lower charge per candidate is useful only if enough candidates meet your brief. Do not infer that underlying Gemini features such as document input or search grounding are exposed by a third-party endpoint; use that endpoint's documented fields.

They are separate choices: Nano Banana 2 corresponds to Gemini 3.1 Flash Image and Nano Banana Pro to Gemini 3 Pro Image. The current Magic Hour catalog lists both. This study supports a narrower conclusion: Nano Banana 2 was faster and charged fewer credits in the tested 1K requests. It does not establish that one replaces the other for every task.

Runbo Li
Runbo Li
CEO of Magic Hour
Runbo Li is the Co-founder and CEO of Magic Hour, where he builds AI video and image tools for content creation. He is a Y Combinator W24 founder and former Data Scientist at Meta, where he worked on 0-1 consumer social products in New Product Experimentation. He writes about AI video generation, AI image creation, creative workflows, and creator tools.
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