Nano Banana vs FLUX.2 Pro: Which to Use for Product Images

You have 50 SKUs, a launch on Monday, and two image models everyone recommends. FLUX.2 [pro] costs a fraction of Nano Banana Pro per image on the official price lists. Nano Banana ranks higher on the public leaderboards and edits better in conversation. Both claims are true, and they point in opposite directions, which is why "which one is better" is the wrong question for a product team.

The useful question is which model to send each job to. This piece works through the official prices, the documented capabilities, the leaderboard numbers and the failure modes, then gives you a routing rule and a test protocol you can run in an afternoon before committing a catalog to either one.

What you are actually comparing

Both names cover a family of models, and the family member matters more than the brand.

The Nano Banana family (Google)

• Nano Banana is Gemini 2.5 Flash Image. Google lists it as deprecated, and it shuts down on October 2, 2026. If a pipeline still calls it by model ID, that pipeline needs a migration this week.
• Nano Banana Pro is Gemini 3 Pro Image. It outputs at 1K, 2K and 4K, and Google's docs describe an integrated thinking step and optional grounding with Google Search.
• Nano Banana 2 is Gemini 3.1 Flash Image, the faster and cheaper sibling. The Batch reports it runs around four times faster than Nano Banana Pro, at 4 to 6 seconds per image and about half the cost.

The FLUX.2 family (Black Forest Labs)

• The FLUX.2 overview lists five variants: klein, pro, flex, max and dev, with output up to 4MP.
• [pro], [flex] and [max] are proprietary and API-only. klein 4B is Apache 2.0, and FLUX.2 [dev] is a 32B open-weight model, which matters if you ever want to self-host.
• Picsart's comparison describes the range as klein at sub-second speed up to max, with flex tuned for typography.

The realistic head-to-head for product work is FLUX.2 [pro] against Nano Banana 2 and Nano Banana Pro, and those three are the reference points below.

Price per image, from the official lists

Google prices image output at $30 per million tokens, with an image up to 1024x1024 consuming 1290 tokens. In practice you read the per-image numbers.

• Nano Banana Pro: $0.134 per 1K or 2K image, $0.24 per 4K image. Batch mode halves that to $0.067 and $0.12 (Google).
• Nano Banana 2: $0.045 at 512px, $0.067 at 1024px, $0.101 at 2048px and $0.151 at 4096px (The Batch).
• FLUX.2: klein 4B from $0.014, klein 9B from $0.015, [pro] from $0.03, [flex] from $0.05, [max] from $0.07. Editing with [pro] starts at $0.045. Pricing is megapixel-based, so the "from" price rises with resolution (Black Forest Labs).

Artificial Analysis normalises this to cost per thousand images: $134 for Nano Banana Pro, $67 for Nano Banana 2, and $30 for FLUX.2 [pro]. At list price, FLUX.2 [pro] is roughly four times cheaper than Nano Banana Pro and a bit over twice as cheap as Nano Banana 2.

The arithmetic on a real batch

Take a 50-SKU catalog refresh at 1K to 2K, with three candidates per SKU so someone can pick the best one. That is 150 generations.

1. FLUX.2 [pro] at list: 150 x $0.03 = $4.50, before any resolution uplift.
2. Nano Banana 2 at 1024px: 150 x $0.067 = $10.05.
3. Nano Banana Pro at 2K: 150 x $0.134 = $20.10. In batch mode, $10.05.

Now add the job most catalogs actually need: 20 lifestyle hero images at 4K. Nano Banana Pro is 20 x $0.24 = $4.80; Nano Banana 2 is 20 x $0.151 = $3.02. The gap only matters at volume. At 10,000 images a month the list-price difference between FLUX.2 [pro] and Nano Banana Pro is $300 against $1,340.

Two costs hide outside the headline number. Reference images are input, and inputs are billed. Retries are real: Magic Hour's test write-up recommends costing the complete job, meaning input images, resolution, number of candidates and retries, instead of the sticker price. A cheaper model that needs three attempts to get a label right is not cheaper for that shot. If you are deciding between subscriptions and metered access for this kind of variable load, the trade-offs are laid out in our breakdown of credits versus pay-per-call.

Where each model is stronger, per the documentation

FLUX.2: brand control and structured prompts

• Exact hex colors. The FLUX.2 docs show color control through the prompt with a literal hex value such as #02eb3c. Picsart names this as the reason it favours Flux 2 for brand work. For packaging in a brand color, this is the most practical difference between the two families.
• JSON prompting. FLUX.2 accepts structured prompts with fields for subject, background, lighting, style, camera angle and composition. For a catalog this turns prompting into templating: fix the lighting and camera fields, swap the subject per SKU.
• References. Up to 8 reference images through the API on pro, max and flex, up to 10 in the playground, up to 4 on klein.
• Product-shot rendering. Black Forest Labs claims sharper textures and more stable lighting suited to product shots, and editing at up to 4MP. Treat that as a vendor claim and verify it on your own products.

Nano Banana: editing, logic and text

• Conversational editing. Picsart's comparison notes that Nano Banana's multi-turn editing keeps the rest of the image intact turn after turn. "Move the bottle left, keep everything else" is the workflow it is good at.
• Counts and logic. In one Magic Hour test, Nano Banana Pro kept correct object counts where FLUX.2 got them wrong. It is one test, worth reproducing on "three jars on a shelf" scenes.
• Multilingual text. Google says Nano Banana Pro renders more accurate legible text in multiple languages, and the API docs list infographics, menus, diagrams and marketing assets as targets.
• Consistency across many inputs. Nano Banana Pro blends up to 14 images while preserving the resemblance of up to 5 people. The API docs break the reference limits down as up to 6 object references, up to 5 for character consistency and up to 3 style references (Google). Nano Banana 2 holds consistency across up to 5 characters and 14 objects, per The Batch.
• Camera and grade controls. Adjustable camera angle, focus, color grading and lighting, with output up to 4K.

The provenance difference

Every image Gemini generates carries a SynthID watermark, and Nano Banana 2 also attaches C2PA Content Credentials. Shoppers will not see it, but it is detectable; check your marketplace policy on labelled AI imagery.

What the leaderboards say, and what they do not

On the Artificial Analysis text-to-image leaderboard (September 2026), Nano Banana 2 ranks 6th with an Elo of 1126 and Nano Banana Pro 11th at 1102. FLUX.2 [flex] is 22nd at 1028, [max] 27th at 1021 and [pro] 39th at 1004. On Arena.ai, The Batch reports Nano Banana 2 at 1,280 against Nano Banana Pro at 1,238. For image editing on Artificial Analysis, the order flips inside the Google family: Nano Banana Pro 1,250, Nano Banana 2 1,233.

Read those numbers with three caveats:

1. They measure general preference. Voters compare open-ended prompts. Nobody is checking whether your brand green is #02eb3c or whether the label says "200 ml" instead of "20O ml".
2. An Elo gap measures how often voters prefer one image in a head-to-head. It says little about failure rate: FLUX.2 [pro] at 1004 still produces usable product images and simply loses more of those comparisons on aesthetic appeal.
3. Price is not on the Elo axis. Divide the leaderboard's own cost column by quality and FLUX.2 [pro] looks very different: less than a quarter of Nano Banana Pro's cost for a lower but still competitive score.

The Picsart and Magic Hour write-ups cited here are vendor opinions, useful for hypotheses. Your own five-task test (below) should decide.

A decision rule you can reuse

Run each image job through these questions in order and stop at the first yes.

1. Must a brand color match a hex value exactly? Use FLUX.2 [pro], with the hex in the prompt. Nano Banana has no documented hex control.
2. Is this a high-volume, templated run (catalog, variants, backgrounds)? Use FLUX.2 [pro] with JSON prompts, or klein for drafts and thumbnails at $0.014 to $0.015 a render.
3. Does the image contain text a customer will read, or text in a language other than English? Use Nano Banana 2 first, Nano Banana Pro if it fails. Check every character either way.
4. Does the scene depend on counts, spatial logic or several consistent people or products? Use Nano Banana Pro.
5. Are you refining one image over several instructions? Use Nano Banana 2 for its speed in the loop, then finish on Pro if the edit needs more care.
6. None of the above? Default to FLUX.2 [pro] on cost, and escalate only the shots that fail review.

The rule deliberately defaults to the cheaper model and escalates. On a 150-image run where 20% of shots escalate to Nano Banana Pro, the list-price bill is 120 x $0.03 plus 30 x $0.134, or $3.60 + $4.02 = $7.62, against $20.10 for sending everything to Pro.

Static images earn their place in paid social more often than people expect; our piece on when a static beats a video covers where they win.

Prompt patterns for each model

A FLUX.2 [pro] catalog template

Keep everything fixed except the subject, so the whole set reads as one shoot:

• subject: "amber glass dropper bottle, 30 ml, matte black cap, white label reading LUMEN"
• background: "seamless paper backdrop, color #F2EDE4"
• lighting: "large softbox camera left, soft shadow falling right"
• style: "clean commercial product photography"
• camera angle: "eye level, 85mm"
• composition: "product centered, 30% negative space above for a headline"

The fields map to the structure Black Forest Labs documents. Locking background hex, lighting and camera across SKUs is the cheapest way to get consistency at volume, and it is the same discipline described in keeping every asset on-brand when you ship at volume.

A Nano Banana edit sequence

Nano Banana works best as a conversation on one image, with each turn changing one thing:

1. "Place this bottle on a travertine bathroom shelf, morning light from a window on the right."
2. "Add exactly two folded white towels behind it. Keep the bottle and light unchanged."
3. "Add the headline 'Clear skin, slow mornings' in the top third, dark serif type."
4. "Warm the color grade slightly. Change nothing else."

The "keep X unchanged" clause is doing work in every turn. Turn 2 also asks for an exact count, which is where Nano Banana Pro tested stronger. If the product must look photographed rather than generated, the checks in making AI creative that does not look AI-generated apply to both models.

A five-task test before you commit a catalog

Magic Hour suggests testing five tasks: product labels, character edits, text posters, localized graphics and revisions. Turned into a protocol:

1. Pick 5 real SKUs, including your hardest one (reflective, transparent or text-heavy packaging).
2. Run each task on FLUX.2 [pro], Nano Banana 2 and Nano Banana Pro, 3 candidates each. That is 5 tasks x 3 models x 3 candidates = 45 images.
3. Score each image pass or fail on four checks: label text correct to the character, brand color within tolerance, object count correct, product shape faithful to the reference.
4. Record attempts to first pass, and multiply by the per-image price. That is your real cost per usable image.
5. Route by task using the results, and write the routing into your pipeline so nobody chooses by habit.

At list prices the whole test costs under $5 across all three models.

The review checklist

• Every character of in-image text matches the source copy, including numbers and units.
• Brand colors match the style guide values, checked with an eyedropper and not by eye.
• Counts in the prompt match counts in the image.
• Logo, cap, label position and proportions match the reference photo.
• No invented claims on packaging (certifications, badges, ingredients).
• Resolution fits the placement; do not pay 4K for a thumbnail.

Running both models from code instead of a web UI

The routing rule pays off when it runs without a human picking a model per shot: a script or an agent reads a product list, applies the rule, calls the right model, and writes back the file and its cost.

1. Input: a CSV of SKUs with name, reference photo, label copy, brand hex and a job type (catalog, hero, localized, edit).
2. Route: map job type to model with the six questions above.
3. Generate: one API call per candidate, with the reference images attached.
4. Log: store the file reference, the model and the cost per call next to each SKU.
5. Review: run the checklist, mark failures, and re-queue failures on the escalation model only.

The same loop works from an agent: ask Claude Code or Cursor to "generate the 50 catalog shots in products.csv, FLUX.2 for catalog rows, Nano Banana for anything with text, and give me a cost table", and let it make the calls. Our working setup for agent-driven production shows the same pattern applied to video, and the ecommerce playbook covers what to do with the stills once you have them.

What this costs through one account

On Aitachyon, at the time of writing, FLUX.2 [pro] is $0.057 to $0.086 per image and Nano Banana is $0.13 per image. Seedream 4.0 is $0.057 per image and gpt-image-2 is $0.10 to $0.40 per image, which gives you two more options in the same routing table.

The same 150-image catalog run costs $8.55 to $12.90 on FLUX.2 [pro] and $19.50 on Nano Banana. The escalation mix from the decision rule (120 on FLUX.2 [pro], 30 on Nano Banana) comes to between $10.74 and $14.22. A 45-image five-task test split evenly across FLUX.2 [pro] and Nano Banana stays under $5. These are prices per call, charged from a prepaid balance, with each job itemised by model and cost, and a failed render refunded automatically.

FAQ

Is Nano Banana better than FLUX.2 for product photos?

On general preference leaderboards, yes: Nano Banana 2 and Nano Banana Pro both rank above every FLUX.2 variant on Artificial Analysis. For product photos specifically, it depends on the job. FLUX.2 [pro] offers exact hex color control and JSON prompting at a much lower price, which suits catalogs. Nano Banana is stronger for multi-turn edits, object counts and in-image text.

How much does FLUX.2 Pro cost per image compared to Nano Banana Pro?

At official list prices, FLUX.2 [pro] starts at $0.03 per image and scales with megapixels, while Nano Banana Pro is $0.134 per 1K or 2K image and $0.24 at 4K. Artificial Analysis puts them at $30 and $134 per thousand images. Budget for retries and reference-image input on top of those numbers.

Which model is better at text in images?

Google positions Nano Banana Pro for legible, multilingual text in infographics, menus and marketing assets, and FLUX.2 [flex] is tuned for typography. Check every character either way.

Is the original Nano Banana still available?

Gemini 2.5 Flash Image, the original Nano Banana, is deprecated and shuts down on October 2, 2026, according to Google's pricing page. Pipelines calling it directly should move to Nano Banana 2 or Nano Banana Pro.

Can I edit an existing product photo with FLUX.2?

Yes. Black Forest Labs documents image editing at up to 4MP, with up to 8 reference images through the API, and prices FLUX.2 [pro] editing from $0.045. For turn-by-turn edits on one image, Nano Banana is the stronger documented choice.

Sources

1. Google AI for Developers: Gemini Developer API pricing
2. Google AI for Developers: Gemini API image generation
3. Google (The Keyword): Nano Banana Pro announcement
4. DeepLearning.AI The Batch: Nano Banana 2 makes edits easier and faster
5. Black Forest Labs docs: FLUX.2 pricing
6. Black Forest Labs docs: FLUX.2 overview
7. Black Forest Labs: FLUX.2 launch post
8. Artificial Analysis: Text-to-Image Model Leaderboard
9. Magic Hour: FLUX.2 vs Nano Banana Pro
10. Picsart: Nano Banana vs Flux 2 comparison

If you want to run the routing rule from code without separate Google and Black Forest Labs accounts, Aitachyon puts FLUX.2 [pro], Nano Banana, Seedream and gpt-image-2 behind one API key and a hosted MCP server for Claude Code or Cursor, billed per call with every image's model and cost itemised.