Nano Banana: which model to choose and how to use it

Choose a Nano Banana model and access route

Nano Banana AI describes a family of Google image workflows rather than one website or subscription. The original Nano Banana maps to Gemini 2.5 Flash Image. Nano Banana 2 maps to Gemini 3.1 Flash Image and was announced on 26 February 2026. Model names matter: an interface displaying only the nickname may not make the underlying version obvious.

For occasional image creation, Gemini provides a conversational workflow: describe an image, upload a reference when needed, and request revisions. Google AI Studio suits direct model experimentation. The Gemini API and Vertex AI are the relevant routes for integrating generation into software or repeatable production workflows. Third-party interfaces add another layer of controls and billing.

• Choose Gemini when you want to generate and revise images in a conversation.
• Choose AI Studio when you want to explore the model directly before building a workflow.
• Choose an API when your application needs to submit instructions and handle outputs programmatically.
• Choose a third-party tool when its workspace or credit system better fits your existing process. Krea offers Nano Banana 2 through credit-based generation.

Check the selected model, supported inputs, and output settings before uploading assets. Gemini Nano Banana access does not automatically mean every interface exposes every model capability. For phone-based edits, Pict.AI is another option: a free AI photo editor app for iPhone and Android, alongside a website with guides and free image tools.

Create or edit an image in seven steps

Effective nano banana prompts describe both the desired change and the details that must remain intact. A clear instruction is more useful than a long string of style adjectives. Start with the task, assign a role to each reference, and separate visual requirements from output settings.

1. Select the interface and model. Confirm whether you are using the original model or Nano Banana 2. Check that your interface supports the resolution and reference inputs you need.
2. State the task first. Use a direct instruction such as “Create a product image,” “Replace the background,” or “Edit the headline.” Avoid mixing an edit request with an unrelated new scene.
3. Describe the subject and composition. Specify the number of people or objects, their positions, camera angle, background, materials, and lighting direction.
4. Assign reference roles. Explain which image supplies the person’s identity, which supplies the product, and which supplies the visual style. Nano Banana 2 supports up to 14 reference images in relevant interfaces.
5. Protect important details. Explicitly identify features that should not change, such as a product’s shape, label wording, clothing, or facial appearance.
6. Specify text and output. Quote the exact wording and describe its placement, hierarchy, margins, aspect ratio, and resolution. Request legibility rather than merely asking for “a poster.”
7. Revise one issue at a time. Ask for a targeted adjustment, then compare the new result with the previous image. Inspect the final export before publishing.

For example: “Edit the uploaded bottle photo into a square product image on a pale gray background. Keep the bottle shape, cap, and label wording unchanged. Place the bottle centrally with a soft shadow extending right. Add ‘DAILY CARE’ above it in large, dark sans-serif lettering. Output at 1K.” This separates the scene, protected details, typography, and delivery requirement.

Check identity, typography and realism before publishing

A convincing thumbnail can hide errors that become obvious in a product listing or printed layout. Review the image at its intended viewing size and inspect important areas more closely. Resolution is only one part of quality: a larger file does not correct a misspelled label or an altered face.

• Faces and identity: compare facial proportions, hairstyle, accessories, and distinctive features against the reference. Check every person in a group, not just the main subject.
• Hands and anatomy: inspect fingers, joints, limb placement, and how a person grips an object. Cropping should not conceal an error you still need in the final layout.
• Product fidelity: verify shape, cap position, label layout, material, and visible branding. Compare details directly rather than relying on a general resemblance.
• Text accuracy: read every word, number, and punctuation mark. Check small labels as well as the headline, including text near curved surfaces or image edges.
• Composite consistency: examine shadows, reflections, perspective, and the boundary between an inserted object and its new background.
• Layout fit: check margins, crop tolerance, and whether important content remains readable at the actual delivery size.

Use the next instruction to fix a specific defect: “Keep the composition unchanged; correct only the headline to ‘DAILY CARE.’” If an exact logo or label remains inaccurate, preserve or place the original asset in a conventional editing workflow instead of repeatedly regenerating it. Treat generative output as an editable asset, not an automatically finished deliverable.

Compare Nano Banana versions and alternative workflows

The useful comparison is not simply which model produces the most attractive sample. Ask whether it supports your input assets, preserves the details you need, offers the required output size, and fits your editing workflow. Google’s image-generation documentation lists the original Nano Banana and Nano Banana 2 as model options.

If you are asking “is nano banana better than gpt image,” compare the same task rather than unrelated showcase images. Use one reference product, one portrait edit, and one text-heavy graphic. Judge preserved details, correct wording, useful composition, and how many revisions each result needs. There is no single ranking that settles every editing task.

For “what is nano banana pro,” the important distinction is that Pro is a separate Google image-model option, not another name for Nano Banana 2. Do not assume a tool labeled Pro uses Gemini 3.1 Flash Image. Confirm the actual selected model before comparing capabilities or costs.

Understand API prices, resolution tiers and free access

As of September 2026, Nano Banana 2 image output in the Gemini API is priced at $60 per million output tokens. The approximate charge changes with the resolution tier. These figures describe image-output charges, not a universal subscription price or a third-party platform’s credit rate.

Budget for revisions, not just finished assets. Three 2K image outputs for each of 100 completed assets would total approximately $30.30 in image-output charges. Input charges and any other applicable costs are separate. The Gemini API pricing page details the model’s billing rates.

The answer to “is nano banana free” depends on the access route and the allowance available to your account or project. There is no universal unlimited free image quota across Gemini, AI Studio, APIs, and third-party services. Check your current allowance before starting a batch.

Nano Banana 2 supports 512px, 1K, 2K, and 4K tiers. Google Cloud lists 1K and 2K as generally available, while 4K remains in preview. Supported aspect ratios include 4:1, 1:4, 8:1, and 1:8; your interface still needs to expose those controls.

Common Nano Banana assumptions that cause avoidable mistakes

“Nano Banana is a standalone app.”

It is an informal model name used across Google services and other interfaces. Before paying for a service, identify both the provider and the model it gives you access to.

“Every interface has the same features.”

Model capabilities and interface controls are different things. A service may expose fewer reference inputs, resolution choices, or aspect ratios than the underlying model supports. Check the actual workflow you intend to use.

“More references always produce a better result.”

The maximum reference count is a capacity limit, not a recommended target. Give each image a clear purpose. Conflicting backgrounds, identities, or lighting instructions make the desired result harder to specify.

“Good text rendering means proofreading is unnecessary.”

Nano Banana 2 emphasizes text rendering and translation, but the output still needs inspection. Exact wording, punctuation, hierarchy, and readable sizing should be part of both the instruction and the final check.

“A 4K export is automatically the best choice.”

Choose resolution for the delivery requirement. Smaller drafts can help you settle composition before requesting a larger output. Increasing resolution does not replace correcting inaccurate content.

A practical starting point is one clearly defined task, a small set of purposeful references, and an explicit list of protected details. Increase complexity only after the model produces a usable base image.