> ## Documentation Index > Fetch the complete documentation index at: https://docs.oxen.ai/llms.txt > Use this file to discover all available pages before exploring further. # Nano Banana Pro > Studio-quality 4K image generation Run this model interactively, tune parameters, and compare outputs. **Model ID:** `google-nano-banana-pro` Nano Banana Pro is an image generation model that excels in generating studio-quality visuals with advanced text rendering, high-fidelity 2K and 4K outputs, and superior character consistency across multiple images. It is built on Google's Gemini 3 Pro Image architecture and leverages multimodal understanding for nuanced, context-aware creative direction rather than simple keyword matching. It is designed for professional creative workflows, making it particularly effective for marketing campaigns, product visualization, and educational content requiring precise, legible text directly embedded in images.\ Nano Banana Pro supports complex editing tasks, such as multi-image fusion, localized edits, and maintaining branding consistency across diverse asset types. Some other noteworthy features of Nano Banana Pro include multi-image blending (up to 14 images per generation), accurate infographic and diagram creation, and advanced controls for scene lighting, camera angle, and color grading. | Metric | Value | | ------------------ | ------- | | Parameter Count | Unknown | | Mixture of Experts | Unknown | | Context Length | Unknown | | Multilingual | Yes | | Quantized\* | Yes | \**Quantization is specific to the inference provider and the model may be offered with different quantization levels by other providers.* ## Example request Use the [Workbench](https://www.oxen.ai/ai/workbench?model=google-nano-banana-pro) as a request builder: configure parameters for this model in the UI, then open the **API** tab to copy the exact cURL or Python call. See the [image editing reference](/inference-api/reference/image_editing) for more details. ```bash cURL theme={null} curl -X POST https://hub.oxen.ai/api/ai/images/edit \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OXEN_API_KEY" \ -d '{ "model": "google-nano-banana-pro", "prompt": "A botanist / researcher looking up at a banana tree in an Amazon rainforest. Photorealistic. It could be used for a National Geographic cover." }' ``` ```python Python theme={null} import os import requests response = requests.post( "https://hub.oxen.ai/api/ai/images/edit", headers={ "Content-Type": "application/json", "Authorization": f"Bearer {os.environ['OXEN_API_KEY']}", }, json={ "model": "google-nano-banana-pro", "prompt": "A botanist / researcher looking up at a banana tree in an Amazon rainforest. Photorealistic. It could be used for a National Geographic cover." }, ) response.raise_for_status() print(response.json()) ``` ```bash cURL theme={null} curl -X POST https://hub.oxen.ai/api/ai/images/edit \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OXEN_API_KEY" \ -d '{ "model": "google-nano-banana-pro", "prompt": "A botanist / researcher looking up at a banana tree in an Amazon rainforest. Photorealistic. It could be used for a National Geographic cover.", "input_image": [ "https://hub.oxen.ai/api/repos/elau/assets/file/main/bloxy/bloxy_cropped_512x512.png" ] }' ``` ```python Python theme={null} import os import requests response = requests.post( "https://hub.oxen.ai/api/ai/images/edit", headers={ "Content-Type": "application/json", "Authorization": f"Bearer {os.environ['OXEN_API_KEY']}", }, json={ "model": "google-nano-banana-pro", "prompt": "A botanist / researcher looking up at a banana tree in an Amazon rainforest. Photorealistic. It could be used for a National Geographic cover.", "input_image": [ "https://hub.oxen.ai/api/repos/elau/assets/file/main/bloxy/bloxy_cropped_512x512.png" ] }, ) response.raise_for_status() print(response.json()) ``` ```bash cURL theme={null} curl -X POST https://hub.oxen.ai/api/ai/images/edit \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OXEN_API_KEY" \ -d '{ "model": "google-nano-banana-pro", "prompt": "A botanist / researcher looking up at a banana tree in an Amazon rainforest. Photorealistic. It could be used for a National Geographic cover.", "input_image": [ "https://hub.oxen.ai/api/repos/elau/assets/file/main/bloxy/bloxy_cropped_512x512.png" ], "aspect_ratio": "16:9", "resolution": "2K", "safety_tolerance": "6" }' ``` ```python Python theme={null} import os import requests response = requests.post( "https://hub.oxen.ai/api/ai/images/edit", headers={ "Content-Type": "application/json", "Authorization": f"Bearer {os.environ['OXEN_API_KEY']}", }, json={ "model": "google-nano-banana-pro", "prompt": "A botanist / researcher looking up at a banana tree in an Amazon rainforest. Photorealistic. It could be used for a National Geographic cover.", "input_image": [ "https://hub.oxen.ai/api/repos/elau/assets/file/main/bloxy/bloxy_cropped_512x512.png" ], "aspect_ratio": "16:9", "resolution": "2K", "safety_tolerance": "6" }, ) response.raise_for_status() print(response.json()) ``` See the [async queue reference](/inference-api/reference/async_queue) for more details. ```bash cURL theme={null} # Enqueue, capture the generation id. GEN_ID=$(curl -s -X POST https://hub.oxen.ai/api/ai/queue \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OXEN_API_KEY" \ -d '{ "model": "google-nano-banana-pro", "prompt": "A botanist / researcher looking up at a banana tree in an Amazon rainforest. Photorealistic. It could be used for a National Geographic cover." }' | jq -r '.generations[0].generation_id') # Poll until the generation reaches a terminal status. while true; do STATUS=$(curl -s -H "Authorization: Bearer $OXEN_API_KEY" \ "https://hub.oxen.ai/api/ai/queue/$GEN_ID" | jq -r '.status') echo "Status: $STATUS" case $STATUS in succeeded|failed|cancelled) break;; esac sleep 5 done # Print the result. curl -s -H "Authorization: Bearer $OXEN_API_KEY" \ "https://hub.oxen.ai/api/ai/queue/$GEN_ID" | jq . ``` ```python Python theme={null} import os import time import requests HEADERS = { "Content-Type": "application/json", "Authorization": f"Bearer {os.environ['OXEN_API_KEY']}", } enqueue = requests.post( "https://hub.oxen.ai/api/ai/queue", headers=HEADERS, json={ "model": "google-nano-banana-pro", "prompt": "A botanist / researcher looking up at a banana tree in an Amazon rainforest. Photorealistic. It could be used for a National Geographic cover." }, ) enqueue.raise_for_status() generation_id = enqueue.json()["generations"][0]["generation_id"] while True: data = requests.get( f"https://hub.oxen.ai/api/ai/queue/{generation_id}", headers=HEADERS, ).json() if data["status"] in {"succeeded", "failed", "cancelled"}: break time.sleep(5) if data["status"] == "succeeded": print(f"Result: {data['result_url']}") else: print(f"Generation {data['status']}: {data.get('error_message')}") ``` ```bash cURL theme={null} # Enqueue, capture the generation id. GEN_ID=$(curl -s -X POST https://hub.oxen.ai/api/ai/queue \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OXEN_API_KEY" \ -d '{ "model": "google-nano-banana-pro", "prompt": "A botanist / researcher looking up at a banana tree in an Amazon rainforest. Photorealistic. It could be used for a National Geographic cover.", "input_image": [ "https://hub.oxen.ai/api/repos/elau/assets/file/main/bloxy/bloxy_cropped_512x512.png" ] }' | jq -r '.generations[0].generation_id') # Poll until the generation reaches a terminal status. while true; do STATUS=$(curl -s -H "Authorization: Bearer $OXEN_API_KEY" \ "https://hub.oxen.ai/api/ai/queue/$GEN_ID" | jq -r '.status') echo "Status: $STATUS" case $STATUS in succeeded|failed|cancelled) break;; esac sleep 5 done # Print the result. curl -s -H "Authorization: Bearer $OXEN_API_KEY" \ "https://hub.oxen.ai/api/ai/queue/$GEN_ID" | jq . ``` ```python Python theme={null} import os import time import requests HEADERS = { "Content-Type": "application/json", "Authorization": f"Bearer {os.environ['OXEN_API_KEY']}", } enqueue = requests.post( "https://hub.oxen.ai/api/ai/queue", headers=HEADERS, json={ "model": "google-nano-banana-pro", "prompt": "A botanist / researcher looking up at a banana tree in an Amazon rainforest. Photorealistic. It could be used for a National Geographic cover.", "input_image": [ "https://hub.oxen.ai/api/repos/elau/assets/file/main/bloxy/bloxy_cropped_512x512.png" ] }, ) enqueue.raise_for_status() generation_id = enqueue.json()["generations"][0]["generation_id"] while True: data = requests.get( f"https://hub.oxen.ai/api/ai/queue/{generation_id}", headers=HEADERS, ).json() if data["status"] in {"succeeded", "failed", "cancelled"}: break time.sleep(5) if data["status"] == "succeeded": print(f"Result: {data['result_url']}") else: print(f"Generation {data['status']}: {data.get('error_message')}") ``` ```bash cURL theme={null} # Enqueue, capture the generation id. GEN_ID=$(curl -s -X POST https://hub.oxen.ai/api/ai/queue \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OXEN_API_KEY" \ -d '{ "model": "google-nano-banana-pro", "prompt": "A botanist / researcher looking up at a banana tree in an Amazon rainforest. Photorealistic. It could be used for a National Geographic cover.", "input_image": [ "https://hub.oxen.ai/api/repos/elau/assets/file/main/bloxy/bloxy_cropped_512x512.png" ], "aspect_ratio": "16:9", "resolution": "2K", "safety_tolerance": "6" }' | jq -r '.generations[0].generation_id') # Poll until the generation reaches a terminal status. while true; do STATUS=$(curl -s -H "Authorization: Bearer $OXEN_API_KEY" \ "https://hub.oxen.ai/api/ai/queue/$GEN_ID" | jq -r '.status') echo "Status: $STATUS" case $STATUS in succeeded|failed|cancelled) break;; esac sleep 5 done # Print the result. curl -s -H "Authorization: Bearer $OXEN_API_KEY" \ "https://hub.oxen.ai/api/ai/queue/$GEN_ID" | jq . ``` ```python Python theme={null} import os import time import requests HEADERS = { "Content-Type": "application/json", "Authorization": f"Bearer {os.environ['OXEN_API_KEY']}", } enqueue = requests.post( "https://hub.oxen.ai/api/ai/queue", headers=HEADERS, json={ "model": "google-nano-banana-pro", "prompt": "A botanist / researcher looking up at a banana tree in an Amazon rainforest. Photorealistic. It could be used for a National Geographic cover.", "input_image": [ "https://hub.oxen.ai/api/repos/elau/assets/file/main/bloxy/bloxy_cropped_512x512.png" ], "aspect_ratio": "16:9", "resolution": "2K", "safety_tolerance": "6" }, ) enqueue.raise_for_status() generation_id = enqueue.json()["generations"][0]["generation_id"] while True: data = requests.get( f"https://hub.oxen.ai/api/ai/queue/{generation_id}", headers=HEADERS, ).json() if data["status"] in {"succeeded", "failed", "cancelled"}: break time.sleep(5) if data["status"] == "succeeded": print(f"Result: {data['result_url']}") else: print(f"Generation {data['status']}: {data.get('error_message')}") ``` See the [async queue reference](/inference-api/reference/async_queue) for more details. ```bash cURL theme={null} # Enqueue, capture the generation id. GEN_ID=$(curl -s -X POST https://hub.oxen.ai/api/ai/queue \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OXEN_API_KEY" \ -d '{ "model": "google-nano-banana-pro", "prompt": "A botanist / researcher looking up at a banana tree in an Amazon rainforest. Photorealistic. It could be used for a National Geographic cover." }' | jq -r '.generations[0].generation_id') # Stream the SSE channel, grab the data line that follows a # media_generation_completed event for our id, and pretty-print it. curl -sN -H "Authorization: Bearer $OXEN_API_KEY" https://hub.oxen.ai/api/events \ | awk -v id="$GEN_ID" ' /^event: media_generation_completed$/ { expect=1; next } /^data: / && expect { payload = substr($0, 7) if (index(payload, "\"generation_id\":\"" id "\"")) { print payload; exit } expect = 0 } ' | jq . ``` ```python Python theme={null} import json import os import requests API_KEY = os.environ["OXEN_API_KEY"] AUTH = {"Authorization": f"Bearer {API_KEY}"} enqueue = requests.post( "https://hub.oxen.ai/api/ai/queue", headers={**AUTH, "Content-Type": "application/json"}, json={ "model": "google-nano-banana-pro", "prompt": "A botanist / researcher looking up at a banana tree in an Amazon rainforest. Photorealistic. It could be used for a National Geographic cover." }, ) enqueue.raise_for_status() generation_id = enqueue.json()["generations"][0]["generation_id"] with requests.get( "https://hub.oxen.ai/api/events", headers=AUTH, stream=True, ) as stream: event_name = None for line in stream.iter_lines(decode_unicode=True): if line.startswith("event: "): event_name = line.removeprefix("event: ") elif line.startswith("data: ") and event_name == "media_generation_completed": payload = json.loads(line.removeprefix("data: ")) if payload.get("generation_id") == generation_id: print(payload) break ``` ```bash cURL theme={null} # Enqueue, capture the generation id. GEN_ID=$(curl -s -X POST https://hub.oxen.ai/api/ai/queue \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OXEN_API_KEY" \ -d '{ "model": "google-nano-banana-pro", "prompt": "A botanist / researcher looking up at a banana tree in an Amazon rainforest. Photorealistic. It could be used for a National Geographic cover.", "input_image": [ "https://hub.oxen.ai/api/repos/elau/assets/file/main/bloxy/bloxy_cropped_512x512.png" ] }' | jq -r '.generations[0].generation_id') # Stream the SSE channel, grab the data line that follows a # media_generation_completed event for our id, and pretty-print it. curl -sN -H "Authorization: Bearer $OXEN_API_KEY" https://hub.oxen.ai/api/events \ | awk -v id="$GEN_ID" ' /^event: media_generation_completed$/ { expect=1; next } /^data: / && expect { payload = substr($0, 7) if (index(payload, "\"generation_id\":\"" id "\"")) { print payload; exit } expect = 0 } ' | jq . ``` ```python Python theme={null} import json import os import requests API_KEY = os.environ["OXEN_API_KEY"] AUTH = {"Authorization": f"Bearer {API_KEY}"} enqueue = requests.post( "https://hub.oxen.ai/api/ai/queue", headers={**AUTH, "Content-Type": "application/json"}, json={ "model": "google-nano-banana-pro", "prompt": "A botanist / researcher looking up at a banana tree in an Amazon rainforest. Photorealistic. It could be used for a National Geographic cover.", "input_image": [ "https://hub.oxen.ai/api/repos/elau/assets/file/main/bloxy/bloxy_cropped_512x512.png" ] }, ) enqueue.raise_for_status() generation_id = enqueue.json()["generations"][0]["generation_id"] with requests.get( "https://hub.oxen.ai/api/events", headers=AUTH, stream=True, ) as stream: event_name = None for line in stream.iter_lines(decode_unicode=True): if line.startswith("event: "): event_name = line.removeprefix("event: ") elif line.startswith("data: ") and event_name == "media_generation_completed": payload = json.loads(line.removeprefix("data: ")) if payload.get("generation_id") == generation_id: print(payload) break ``` ```bash cURL theme={null} # Enqueue, capture the generation id. GEN_ID=$(curl -s -X POST https://hub.oxen.ai/api/ai/queue \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OXEN_API_KEY" \ -d '{ "model": "google-nano-banana-pro", "prompt": "A botanist / researcher looking up at a banana tree in an Amazon rainforest. Photorealistic. It could be used for a National Geographic cover.", "input_image": [ "https://hub.oxen.ai/api/repos/elau/assets/file/main/bloxy/bloxy_cropped_512x512.png" ], "aspect_ratio": "16:9", "resolution": "2K", "safety_tolerance": "6" }' | jq -r '.generations[0].generation_id') # Stream the SSE channel, grab the data line that follows a # media_generation_completed event for our id, and pretty-print it. curl -sN -H "Authorization: Bearer $OXEN_API_KEY" https://hub.oxen.ai/api/events \ | awk -v id="$GEN_ID" ' /^event: media_generation_completed$/ { expect=1; next } /^data: / && expect { payload = substr($0, 7) if (index(payload, "\"generation_id\":\"" id "\"")) { print payload; exit } expect = 0 } ' | jq . ``` ```python Python theme={null} import json import os import requests API_KEY = os.environ["OXEN_API_KEY"] AUTH = {"Authorization": f"Bearer {API_KEY}"} enqueue = requests.post( "https://hub.oxen.ai/api/ai/queue", headers={**AUTH, "Content-Type": "application/json"}, json={ "model": "google-nano-banana-pro", "prompt": "A botanist / researcher looking up at a banana tree in an Amazon rainforest. Photorealistic. It could be used for a National Geographic cover.", "input_image": [ "https://hub.oxen.ai/api/repos/elau/assets/file/main/bloxy/bloxy_cropped_512x512.png" ], "aspect_ratio": "16:9", "resolution": "2K", "safety_tolerance": "6" }, ) enqueue.raise_for_status() generation_id = enqueue.json()["generations"][0]["generation_id"] with requests.get( "https://hub.oxen.ai/api/events", headers=AUTH, stream=True, ) as stream: event_name = None for line in stream.iter_lines(decode_unicode=True): if line.startswith("event: "): event_name = line.removeprefix("event: ") elif line.startswith("data: ") and event_name == "media_generation_completed": payload = json.loads(line.removeprefix("data: ")) if payload.get("generation_id") == generation_id: print(payload) break ``` ## Fetch model details The [models endpoint](/inference-api/reference/models/overview) returns the full model object, including its `json_request_schema`. ```bash theme={null} curl -H "Authorization: Bearer $OXEN_API_KEY" https://hub.oxen.ai/api/ai/models/google-nano-banana-pro ``` ## Request parameters ### Required parameters | Field | Type | Default | Description | | -------- | -------- | -------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------- | | `prompt` | `string` | `"A botanist / researcher looking up at a banana tree in an Amazon rainforest. Photorealistic. It could be used for a National Geographic cover."` | Text description of what you want to generate, or the instruction on how to edit the given image. | ### Optional parameters | Field | Type | Default | Description | | ------------------ | --------------- | -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `input_image` | `array` | — | Input images to transform or use as reference (supports multiple image urls) | | `aspect_ratio` | `string` | `"16:9"` | Aspect ratio for the generated image One of: auto, 1:1, 16:9, 9:16, 4:3, 3:4, 3:2, 2:3. | | `resolution` | `string` | `"2K"` | Resolution of the generated image One of: 1K, 2K, 4K. | | `safety_tolerance` | `string` | `"6"` | The safety tolerance level for content moderation. 1 is the most strict (blocks most content), 6 is the least strict. Default value: 4 One of: 1, 2, 3, 4, 5, 6. |