Google Nano Banana Pro Text To Image Ultra
Playground
Try it on WavespeedAI!Google’s Nano Banana Pro (Gemini 3.0 Pro Image) is a cutting-edge text-to-image model enabling high-res image generation optimized for phones. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Features
Google Nano Banana Pro Ultra Text-to-Image
Nano Banana Pro Text-to-Image (Gemini 3.0 Pro Image) is Google’s lightweight yet powerful AI image generation model, built for creators who need fast, high-quality visuals from simple text prompts. It transforms words into expressive, realistic images with remarkable clarity, composition, and style diversity — all within seconds.
🌟 Why it stands out
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Native 4K image generation Produce crisp, production-ready images with fine detail and clean edges.
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Natural-language, context-aware editing Modify images using simple text instructions. The model understands scene structure, objects, and relationships for realistic edits.
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Multilingual on-image text with auto translation Generate and edit text inside images in multiple languages, with improved font clarity and layout.
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Camera-style controls Support for camera-related parameters such as angle, focus, depth of field, and color adjustment for more photographic results.
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Aspect ratio flexibility Supports formats from 1:1 to 9:16 (and beyond, such as 4:3, 16:9, 21:9), suitable for feeds, stories, banners, and print concepts.
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Consistent character and style rendering Maintain character identity, brand elements, and overall style across related images.
⚙️ Capabilities
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Input: text prompt
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Output: high-quality image (JPEG/PNG)
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Supports multiple aspect ratios and output format
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Compatible with descriptive prompts such as:
- “A golden retriever playing in a field of sunflowers at sunset.”
- “A futuristic city skyline with neon reflections on wet streets.”
- “An elegant still-life photo of coffee and croissants by a window.”
💰 Pricing
| Resolution | Cost per image |
|---|---|
| 4k | $0.15 |
| 8k | $0.18 |
💡 Best Use Cases
- Social Media & Marketing — Create on-brand visuals instantly.
- Concept Art & Storyboarding — Generate design ideas and moodboards effortlessly.
- E-commerce & Advertising — Produce high-quality product images without photography.
- Education & Presentation — Visualize complex ideas or creative concepts easily.
📝 Notes
- Please ensure your prompts comply with Google’s Safety Guidelines.
- If an error occurs, review your prompt for restricted content, adjust it, and try again.
- For this model, it is not as same Google, the total pixcels like 2k is 2560 * 1440 = 3686400 and 4k is 3840 * 2160 = 8294400.
🌏 Where Nano Banana Pro Fits In
Compare Nano Banana Pro T2I with:
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FLUX.1 [dev] – Nano Banana Pro Edit focuses on semantic understanding and layout-aware editing via Gemini 3’s reasoning, making it ideal for complex, text-driven transformations without manual masking. FLUX.1 [dev] emphasizes maximum resolution control and fine detail preservation for highly technical workflows.
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Stable Diffusion 3.5 – Nano Banana Pro Edit leverages Google’s production-scale multimodal training and advanced reasoning for context-aware edits that understand object relationships and composition. Stable Diffusion 3.5 offers open-source flexibility for custom fine-tuning and local deployment in highly specialized pipelines.
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Gpt-Image-1 (OpenAI) – Nano Banana Pro Edit emphasizes layout control, multilingual on-image text, and tightly directed edits for design and marketing workflows, while openai/gpt-image-1 shines as a general-purpose creative generator with strong style variety and fast, natural-language image synthesis for broad consumer and developer use.
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Original Nano Banana (Gemini 2.5 Flash Image) – Nano Banana Pro Edit trades pure speed for quality, delivering better reasoning, sharper text, improved character consistency, and richer camera controls at a higher unit cost. Original Nano Banana remains ideal for rapid, low-latency iterations and lightweight edits.
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Seedream – Nano Banana Pro Edit is tuned for reliable typography, photo-real edits, and mixed media layouts, while SeeDream excels at fast, stylized T2I generation with strong anime and illustration aesthetics, making it a good choice for heavily stylized concept art.
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Qwen Image 2509 – Nano Banana Pro Edit focuses on high-fidelity 4K outputs and multilingual on-image design control, whereas Qwen Image shines in open-source ecosystems and document-style rendering, offering flexible integration for developer-centric and research workflows.
Authentication
For authentication details, please refer to the Authentication Guide.
API Endpoints
Submit Task & Query Result
# Submit the task
curl --location --request POST "https://api.wavespeed.ai/api/v3/google/nano-banana-pro/text-to-image-ultra" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
"resolution": "4k",
"output_format": "png",
"enable_sync_mode": false,
"enable_base64_output": false
}'
# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"
Parameters
Task Submission Parameters
Request Parameters
| Parameter | Type | Required | Default | Range | Description |
|---|---|---|---|---|---|
| prompt | string | Yes | - | The positive prompt for the generation. | |
| aspect_ratio | string | No | - | 1:1, 3:2, 2:3, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9 | The aspect ratio of the generated media. |
| resolution | string | No | 4k | 4k, 8k | The resolution of the output image. |
| output_format | string | No | png | png, jpeg | The format of the output image. |
| enable_sync_mode | boolean | No | false | - | If set to true, the function will wait for the result to be generated and uploaded before returning the response. It allows you to get the result directly in the response. This property is only available through the API. |
| enable_base64_output | boolean | No | false | - | If enabled, the output will be encoded into a BASE64 string instead of a URL. This property is only available through the API. |
Response Parameters
| Parameter | Type | Description |
|---|---|---|
| code | integer | HTTP status code (e.g., 200 for success) |
| message | string | Status message (e.g., “success”) |
| data.id | string | Unique identifier for the prediction, Task Id |
| data.model | string | Model ID used for the prediction |
| data.outputs | array | Array of URLs to the generated content (empty when status is not completed) |
| data.urls | object | Object containing related API endpoints |
| data.urls.get | string | URL to retrieve the prediction result |
| data.has_nsfw_contents | array | Array of boolean values indicating NSFW detection for each output |
| data.status | string | Status of the task: created, processing, completed, or failed |
| data.created_at | string | ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”) |
| data.error | string | Error message (empty if no error occurred) |
| data.timings | object | Object containing timing details |
| data.timings.inference | integer | Inference time in milliseconds |
Result Request Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| id | string | Yes | - | Task ID |
Result Response Parameters
| Parameter | Type | Description |
|---|---|---|
| code | integer | HTTP status code (e.g., 200 for success) |
| message | string | Status message (e.g., “success”) |
| data | object | The prediction data object containing all details |
| data.id | string | Unique identifier for the prediction, the ID of the prediction to get |
| data.model | string | Model ID used for the prediction |
| data.outputs | string | Array of URLs to the generated content (empty when status is not completed). |
| data.urls | object | Object containing related API endpoints |
| data.urls.get | string | URL to retrieve the prediction result |
| data.status | string | Status of the task: created, processing, completed, or failed |
| data.created_at | string | ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”) |
| data.error | string | Error message (empty if no error occurred) |
| data.timings | object | Object containing timing details |
| data.timings.inference | integer | Inference time in milliseconds |