Nano Banana 2 is Google DeepMind's image generation and editing model, officially named Gemini 3.1 Flash Image. Google announced it on February 26, 2026, with access through its hosted products and developer services. It has no open weights and is not a downloadable Stable Diffusion checkpoint. Google announcement and developer release.
What are the key facts about Nano Banana 2?
| Developer | Released | Type | Size or parameters | License and access | Where it runs |
|---|---|---|---|---|---|
| Google DeepMind. Announcement | February 26, 2026. Announcement | Image generation and conversational editing. Model page | Not published in the model documentation. Model page | Hosted service under Google's API terms; no open weights. Terms and release | Google services, including Gemini API and AI Studio. Developer release |
Evaluate Nano Banana 2 by turning each relevant feature into a task: generate an image containing approved text, edit a supplied reference, or create a composition using search grounding. Choose the task first, then record the model and enabled features so you can judge whether the result meets the brief.
Use the official model identifier when comparing evidence. The current model page lists gemini-3.1-flash-image as the stable version. Include that identifier in your evaluation record so the result is tied to a specific model. Current version documentation.
Which features did Nano Banana 2 introduce?
Google's developer announcement describes image generation with web-search references, text rendering and localization, additional aspect ratios, and more control over reasoning before an image is produced. Those are documented features you can turn into specific evaluation tasks. They are not a substitute for inspecting your own outputs. Developer announcement.
Start with an evaluation brief that has observable requirements. A shop-window illustration might need a readable business name, recognizable product shapes, and room for a caption. Mark each requirement separately when reviewing the output. This is more informative than recording only whether the image looks impressive.
For editing, choose a reference and a narrow change. Ask for the setting to change while preserving the subject, then compare the result against the reference. Keep the rejected versions as well as the accepted one during evaluation. They help you judge how much revision work the task required.
Google's model documentation also lists search grounding and thinking as supported capabilities. If your use case depends on either, record whether it was enabled in the request. A test with extra information or a different reasoning setting should be labeled accordingly. Capability list.
What are the limits of Nano Banana 2?
The official model page does not publish a parameter count. It also does not specify a local graphics-card requirement, because the documented access path is a hosted service. Model page and access documentation.
Google's image-generation guide notes that requested image counts may not be followed exactly. It also says Nano Banana 2's image-search grounding does not currently support real-world images of people from web search. Treat these as limits on the documented workflow when designing requests. Image-generation limitations.
A vendor's description of faster generation is not an independently measured latency guarantee. This article reports no fixed seconds-per-image figure. For your own comparison, write down the model, settings, input assets, and timing method before collecting results, then report the conditions alongside the measurements.
Apply the same discipline to quality. Decide which visible failures matter for the job before seeing the outputs. A readable title and an accurate product shape may be essential; a different decorative background may be acceptable. Make that distinction explicit so the evaluation answers a real production question.
How do you access and test Nano Banana 2?
Set up a Gemini API key through Google AI Studio and expose it to your shell as GEMINI_API_KEY. Google's developer release states that using this model in AI Studio requires a paid API key. Check the account you are using before starting a test. Key setup and developer access.
Use the stable identifier in a minimal request. This example follows Google's current Interactions API image-generation pattern and sends a deliberately simple prompt. It returns a JSON response; use the documented image-data handling to save the generated result. API guide.
curl --fail-with-body \
https://generativelanguage.googleapis.com/v1beta/interactions \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "gemini-3.1-flash-image",
"input": "Create a shop window illustration with the exact sign OPEN TODAY.",
"response_format": {"type": "image"}
}'
Inspect the sign before elaborating the scene. Record whether every word is present and readable. Then add a new requirement, such as a particular layout, while keeping the earlier acceptance criteria. This creates a traceable progression from a basic request to the task you actually want to automate.
For format-specific work, continue with the sibling Nano Banana 2 aspect-ratio and output-size guide. Keep its size configuration separate from changes to the wording of your prompt so you can explain which variable you changed.
If testing through the Gemini app instead, follow Google's current image-generation help rather than reconstructing the launch interface. The help page documents creating and editing images, including which model choices correspond to Nano Banana variants. Gemini app instructions.
How does Nano Banana 2 compare with the original and Pro?
| Model | Documented distinction |
|---|---|
| Original Nano Banana | Gemini 2.5 Flash Image; its model page lists image and text inputs and outputs, without search grounding or thinking. Documentation |
| Nano Banana 2 | Gemini 3.1 Flash Image; its model page lists search grounding and thinking. Documentation |
| Nano Banana Pro | Gemini 3 Pro Image; Google positions it for complex design and product visualization. Documentation |
These are capability distinctions, not a quality leaderboard. Use the AI model-release timeline to orient the releases, then evaluate the exact model against your own brief. A later release date alone does not establish which result you will prefer.
What else should you know about Nano Banana 2?
When was Nano Banana 2 released?
Google announced Nano Banana 2 on February 26, 2026. The current developer model page documents the stable Gemini 3.1 Flash Image identifier. Announcement and model page.
Is Nano Banana 2 the same as Nano Banana Pro?
No: Google identifies Nano Banana 2 as Gemini 3.1 Flash Image and Nano Banana Pro as Gemini 3 Pro Image. Record the complete identifier when sharing an example or comparison. Nano Banana 2 and Nano Banana Pro.
Can I download Nano Banana 2 for local inference?
Google documents hosted access and does not publish an open-weight download for Nano Banana 2. The parameter count is not published in the model documentation. Developer release and model page.
How should I check a claimed speed advantage?
Use the same task brief and record each model's settings, inputs, and complete request time. Present your result as a measurement of that test, without converting it into a universal promise about the model.
Sources
Primary sources checked on September 5, 2026.
- Google announcement of Nano Banana 2
- Google developer announcement of Nano Banana 2
- Google model documentation for Gemini 3.1 Flash Image
- Google Gemini API terms
- Google Gemini API image-generation documentation
- Google documentation for Gemini API keys
- Google help for generating and editing images in Gemini Apps
- Google model documentation for Gemini 2.5 Flash Image
- Google model documentation for Gemini 3 Pro Image
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