Flux Kontext Max vs Google Nano Banana 2
Generated on PixelDojo. Produced by PixelDojo's generation pipeline.
Flux Kontext Max costs 2 credits per image and Google Nano Banana 2 cost 2 credits on our run at its default 1K tier. We sent both the same instruction to place a specific woman on a bicycle in a rainy crosswalk on August 26, 2026. Google Nano Banana 2 took the reference photo we gave it and preserved her actual face. Flux Kontext Max's apiId has no reference image field in its schema, so it read the same wording as a plain text prompt and invented its own woman who matches the description but not the photo.
The same instruction, only one given a reference photo
Every example below was produced on PixelDojo. Hover to see the prompt.
Place this exact woman on a bicycle crossing a rainy city crosswalk at dusk, neon shop signs reflecting in puddles, cinematic wide shot, keep her face, glasses, red curly hair, denim jacket and yellow shirt identical
Flux Kontext Max
Place this exact woman on a bicycle crossing a rainy city crosswalk at dusk, neon shop signs reflecting in puddles, cinematic wide shot, keep her face, glasses, red curly hair, denim jacket and yellow shirt identical
Google Nano Banana 2
Same scene instruction, both 2 credits. Nano Banana 2 used the reference photo. Flux Kontext Max could not take one and generated from text alone.
Why Choose Pixel Dojo for Flux Kontext Max vs Google Nano Banana 2
Professional-quality results with cutting-edge AI technology
Nano Banana 2 kept the real face
Given the reference photo, it preserved the actual bone structure, glasses style and hair from the source image, not just the text description of them.
Flux Kontext Max still followed the text closely
Red curly hair, round glasses, denim jacket over a yellow shirt, a rainy neon lit crosswalk, all present, just on a different face since it had no photo to work from.
How It Works
How we ran the comparison, so you can repeat it:
Send the same instruction to both
The identical scene wording went to flux-kontext-max as a text prompt and to nano-banana-2 with the reference photo attached.
Bracket each request with a timestamp
We recorded the second before submitting and the second the finished file arrived, for both models.
Compare the face against the source photo
We checked which output actually matched the person in the reference image, not just the words in the prompt.
The Pixel Dojo Advantage
Same wording, same day. The real difference is which one can take a photo at all.
| Others | Pixel Dojo |
|---|---|
| Flux Kontext Max: 2 credits flat per image, about 13 seconds on our run | Google Nano Banana 2: 2 credits at the 1K tier we ran, 3 at 2K, 4 at 4K, about 21 seconds at 1K |
| Flux Kontext Max: text prompt only on this apiId, no reference image field in its schema | Google Nano Banana 2: accepts up to 14 reference images through reference_images at the same price tier |
| Flux Kontext Max: generated a new woman matching the text description, not the photo | Google Nano Banana 2: preserved the actual face, glasses and hair from the reference photo we sent |
| Flux Kontext Max: description promises improved typography for lettering-heavy generation work | Google Nano Banana 2: description promises stronger consistency across a reference image set |
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Common Questions
Everything you need to know about Flux Kontext Max vs Google Nano Banana 2
Can Flux Kontext Max edit an existing photo?
Not through this apiId. Its documented schema is prompt, aspect_ratio, seed, output_format and output_quality, with no image input field, so a call to it is text-to-image only regardless of the wording you send.
Does Google Nano Banana 2 actually use a reference photo?
Yes. Send one or more URLs through reference_images alongside your prompt and it works from them, up to 14 in a single request. On our run it preserved the source face rather than inventing a new one.
Which one is cheaper?
They matched on our run: 2 credits each, Flux Kontext Max flat and Google Nano Banana 2 at its 1K resolution tier. Nano Banana 2 rises to 3 credits at 2K and 4 credits at 4K.
Which one is faster?
Flux Kontext Max, in our test: about 13 seconds against about 21 seconds for Google Nano Banana 2 at 1K on the same wording.
So which should I use for a reference-based scene edit?
Google Nano Banana 2, since it is the one that can actually take the photo. Flux Kontext Max is a fit when you want its generation quality from a text description alone and do not have, or do not need, a reference image.
How do I call either from the API?
POST to /api/v1/models/flux-kontext-max/run with a prompt, or to /api/v1/models/google-gemini-image/run with modelTier set to pro_fal_v2 and reference_images for Nano Banana 2. Same API key for both.