Prompting Nano Banana Pro for Production-Ready Images

Google launched Nano Banana Pro, the Gemini 3 Pro Image model, on November 20, 2025. It is aimed at high-fidelity image generation and editing: accurate text inside images, complex compositions, multilingual captions, multi-image references, and outputs up to 4K.

The model is especially useful when an image needs to function as more than decoration. Infographics, product mockups, advertising assets, maps, diagrams, and controlled photo edits all benefit from the additional reasoning and editing controls.

What Nano Banana Pro Adds

Nano Banana Pro is Google’s professional image model built on Gemini 3 Pro Image. Google positions it as a “thinking-mode” model for visual work where layout, factual constraints, text fidelity, and contextual understanding matter.

Its relevant capabilities include:

• Legible long-form and multilingual text rendering.
• Blending up to 14 reference images.
• Maintaining subject or character likeness across images, with launch notes mentioning up to 5 people.
• Camera-angle, lighting, color-grading, and local-area edits.
• Studio-oriented 2K and 4K export options.
• Infographic and diagram generation grounded in broader world knowledge.
• Availability through the Gemini app, Google AI Studio, developer APIs, and partnerships such as Adobe integrations reported during the initial launch.

The official Nano Banana Pro service is currently congested, particularly for free users. Free users can generate only three low-resolution images. For applications that need a unified multi-model API, CometAPI provides access to the Gemini 3 Pro Image API.

Nano Banana Flash vs. Pro

The original Nano Banana, also referred to as Nano Banana Flash, is optimized for speed. I use that kind of model for quick concepts, storyboards, and early visual exploration.

Nano Banana Pro makes a different tradeoff:

Area	Nano Banana Flash	Nano Banana Pro
Primary goal	Fast iteration	Higher-fidelity production output
Processing	Speed-oriented	Includes a “thinking” phase
Text	More limited	Better with long strings, paragraphs, and multilingual text
References	Fewer references typically used	Up to 14 reference images
Consistency	Useful for ideation	Stronger person and character consistency
Editing	Basic image transformation	More robust local edits, lighting, and camera changes
Best use	Concepts and drafts	Infographics, campaigns, print, and final renders

The practical difference is not simply resolution. Pro spends more effort planning the visual result before producing it. That makes it better at coordinating typography, factual labels, subject identity, and spatial relationships.

Traditional image generation can be thought of as a prompt-to-noise-to-denoise pipeline. Nano Banana Pro adds a reasoning or “thinking” phase, exposed as a mode in the UI and implicitly used in higher-fidelity API calls.

That extra planning helps with:

• Layout and typography for embedded text.
• Maps, technical visuals, and labeled diagrams.
• Maintaining identity across multiple frames or blended references.
• Multi-step editing workflows.

A short prompt can still produce a good image, but it does not give the planning phase enough information to optimize for a production constraint.

A Prompt Structure That Holds Up

I get more reliable results when the prompt reads like a compact creative brief instead of a pile of adjectives.

The structure I use is:

1. Intent and deliverable: what asset is being created and where it will be used.
2. Subject and composition: subjects, pose, camera angle, framing, and negative space.
3. Style: medium, lighting, lens or camera cues, palette, and visual references.
4. Text specification: exact strings, language, typography, placement, and color.
5. Constraints: factual requirements, brand rules, identity restrictions, or elements to preserve.
6. Output and edits: aspect ratio, resolution, file target, and local modifications.

A compact version:

Intent: .
Subject: .
Composition: .
Style: .
Text: .
Constraints: .
Output: .

For example, “make a nice poster” leaves too much unresolved. “Create a 2K poster for a jazz festival, use a centered 3/4 portrait, reserve the right side for the event information, and render the title in a bold condensed sans serif” gives the model a usable layout and objective.

Text Needs Its Own Specification

Nano Banana Pro is substantially better at text rendering than earlier image models, but typography still benefits from explicit instructions.

I specify:

• The exact characters and punctuation.
• The language and diacritics.
• Font family or visual category.
• Case, size, weight, and spacing.
• Alignment and position.
• What should happen if the text does not fit.

For example:

Render the headline: "SUSTAINABLE FUTURES" in bold condensed sans, all caps, 48 pt, kerning -5%, color #0B3D91.

I also define the available area:

Place the headline in the bottom 10% banner, left aligned. Render the text exactly. If it overflows, scale it down equally by up to 12% and increase leading while preserving legibility.

That is more useful than asking for “a caption” or “some readable text.” When an image contains a paragraph, diagram labels, or multilingual content, the exact strings should be part of the prompt.

A Practical Workflow

1. Select the model and mode

Use the Nano Banana Pro model selection in Gemini, Google AI Studio, or the API. Depending on the interface, the model may be identified as gemini-3-pro-image or gemini-3-pro-image-preview.

For early exploration, I switch to the non-Pro model for faster iterations and use Pro for the final render.

2. State the purpose first

Start with one or two sentences describing the audience, use case, and intended feeling.

Intent: A poster for a climate-tech webinar aimed at corporate sustainability managers — modern, credible, minimal, with clear multilingual headline space.

This gives the model a reason to make visual tradeoffs instead of treating every instruction as equally important.

3. Lock down composition

Specify the focal point, camera view, proportions, and areas reserved for text or supporting information.

Composition: centered product on white studio surface, three-quarter lighting, soft shadow; left column for 40% width headline and bullet list.

For nonstandard formats, include the aspect ratio explicitly. If text and image share the canvas, reserve their regions rather than describing them vaguely.

4. Use concrete style anchors

Words such as “cool,” “modern,” or “beautiful” are underspecified. I get more consistent results from concrete anchors:

• Kodak Portra 400 film look
• flat 2-color vector infographic
• isometric 3D product render, cinematic rim light
• HDR cinematic

When building a series, style references can be chained:

Style examples: 1) "Polaroid, high-contrast vintage", 2) "Minimalist flat icons", 3) "HDR cinematic". Use #2 for this infographic, preserve flat iconography and two-tone palette.

5. Provide source assets and masks

For image-to-image work or local edits, upload clean source images and clear masks. Name or describe the inputs by their intended operation, for example:

mask_replace_logo.png

Then state exactly what should change and what must remain fixed. Nano Banana Pro supports multi-image editing and blending, and structured inputs make those operations more predictable.

6. Ask for an approach when layout is difficult

When translation or layout decisions are central, I ask for a short description of the approach rather than leaving the model to make silent tradeoffs.

Explain: Prioritize legibility when translating to Spanish and German; if headline overflows, reduce font size by up to 12% and increase leading.

This is particularly useful when the same design must accommodate languages with different text lengths.

Reusable Prompt Patterns

Constrained photo transformation

For edits, list the transformation and the invariants separately:

Edit: replace sky with dusk gradient (orange→indigo), keep subject exposure constant, add soft rim light, increase saturation of jacket by 10%. Preserve EXIF camera metadata.

The more explicitly the prompt separates changed and preserved regions, the fewer correction passes are usually needed.

Factual infographic

Charts, diagrams, and maps need explicit labels and relationships. The model should not have to infer the wording or topology.

Create an infographic showing solar panel energy flow:
- Top: title "Solar Energy Flow"
- Left: sun icon with arrow to panel labeled "Insolation (kWh/m²)"
- Middle: solar panel illustration with callouts for "PV cells", "Inverter"
- Right: house icon labeled "Consumption (kWh/day)"
- Color palette: cool blues/greens, flat icons, legible labels, use metric units.

For fact-sensitive visuals, include units and exact labels. A visually polished diagram with incorrect annotations is still a failed output.

Multi-image character consistency

When blending references, describe each subject with stable identity markers and state that those markers must persist.

Blend three reference photos into a single scene: character A (brown hair, scar on left eyebrow, worn leather jacket), character B (short curly hair, glasses). Keep consistent facial features across all deliverables; place both characters at table, mid-shot, warm tungsten lighting.

Use a clear reference set and, where supported, subject IDs or tokens. Physical details such as hair length, moles, scars, and earrings are more useful than broad descriptors.

Failure Modes I Watch For

Incorrect or unstable text

Use exact strings, specify font characteristics and placement, request that the text be rendered exactly, and define overflow behavior. For edits, masks can reserve the text area.

Inconsistent characters

Provide clear reference images and repeat the identity anchors. If the interface supports subject IDs or tokens, use them consistently across generations.

Artifacts at high zoom

Request higher internal sampling when the API exposes sampling or guidance controls. Generate 2–3 variations, select the strongest result, or render at higher pixel dimensions and downsize during post-processing.

Conflicting requirements

Do not give every constraint equal priority. State one primary objective, such as:

Priority: legibility over ultra-photorealism.

A model cannot simultaneously maximize every visual property. Making the priority explicit gives it a sensible way to resolve conflicts.

Final Notes

Nano Banana Pro is most useful when an image needs reliable typography, reasoned layout, reference-image consistency, or controlled editing. I treat it less like a text-to-image toy and more like a visual production system: define the brief, specify the constraints, provide the assets, and iterate against a clear priority.

Nano Banana Flash remains the practical choice for fast concepting. Pro is the better fit for high-resolution final renders, multilingual text, advertising assets, technical diagrams, and edits where preserving identity or exposure matters. Versioning prompts, references, masks, and selected outputs is part of making the workflow reproducible.

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Originally published at cometapi.com