Nano Banana Pro

Try Nano Banana Pro in the Workbench

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 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.

Sync
Async
Async with SSE

See the image editing reference for more details.

Minimal
Basic parameters
All parameters

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."
}'

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"
  ]
}'

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": "auto",
  "resolution": "2K",
  "safety_tolerance": "6"
}'

See the async queue reference for more details.

Minimal
Basic parameters
All parameters

# 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 the single generation until it 404s (terminal state).
while curl -s -o /dev/null -w "%{http_code}" \
    -H "Authorization: Bearer $OXEN_API_KEY" \
    "https://hub.oxen.ai/api/ai/queue/$GEN_ID" | grep -q "^200$"; do
  sleep 5
done
echo "Done. See the 'Async with SSE' tab to receive the result URL."

# 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 the single generation until it 404s (terminal state).
while curl -s -o /dev/null -w "%{http_code}" \
    -H "Authorization: Bearer $OXEN_API_KEY" \
    "https://hub.oxen.ai/api/ai/queue/$GEN_ID" | grep -q "^200$"; do
  sleep 5
done
echo "Done. See the 'Async with SSE' tab to receive the result URL."

# 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": "auto",
  "resolution": "2K",
  "safety_tolerance": "6"
}' | jq -r '.generations[0].generation_id')

# Poll the single generation until it 404s (terminal state).
while curl -s -o /dev/null -w "%{http_code}" \
    -H "Authorization: Bearer $OXEN_API_KEY" \
    "https://hub.oxen.ai/api/ai/queue/$GEN_ID" | grep -q "^200$"; do
  sleep 5
done
echo "Done. See the 'Async with SSE' tab to receive the result URL."

See the async queue reference for more details.

Minimal
Basic parameters
All parameters

# 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 .

# 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 .

# 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": "auto",
  "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 .

Fetch model details

The models endpoint returns the full model object, including its json_request_schema.

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<string>`	—	Input images to transform or use as reference (supports multiple image urls)
`aspect_ratio`	`string`	`"auto"`	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.

Inference API

Try Nano Banana Pro in the Workbench

​Example request

​Fetch model details

​Request parameters

​Required parameters

​Optional parameters

Example request

Fetch model details

Request parameters

Required parameters

Optional parameters