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fal-ai/nano-banana-2

fal-ai/nano-banana-2 is a text to image tool from fal that runs in the background and returns images. Pay as you go from $0.0026 per request, with one API key.

Try itfrom $0.0026 / request

Pricing

resolution: 0.5K $0.078, 1K (default) $0.104, 2K $0.156, 4K $0.208 per image. thinking_level high adds $0.0026 per request. enable_web_search adds $0.0195 per request. Billed on num_images, default 1. The final charge comes from usage.

Limits

Runs: In the background
Returns: image
Longest queue wait: 3600s
Run deadline: 1800s
Largest inline input: 1024 KB
Largest input file: 500 MB
Largest output: 4768 MB
Outputs per run: 16

Input

Field	Kind	Default	Description
`prompt` required	text	—	The text prompt to generate an image from.
`history`	JSON	—	Previous native Gemini turns, including original parts and thought signatures from conversations. The current prompt starts a new user turn.
`context`	JSON	—	Ordered text, image, PDF, UTF-8 text-file or video parts appended to the current prompt. Original media bytes are preserved; images count toward the total 14-reference limit.
`return_history`	true or false	false	Return reusable native conversations with original image parts and provider thought signatures. Uses a native provider. Each generated conversation is independent; send its messages as history for the next turn.
`thinking_level`	one of minimal, high	—	Controls thinking effort ('minimal' or 'high') and requests thought summaries. Omit to use the provider default ('minimal'); thinking cannot be disabled.
`image_passthrough`	true or false	false	Send reference images and return provider images without resizing or re-encoding, preserving transparency and metadata. Uses direct Google or Vertex APIs; the returned image format may differ from output_format.
`include_thoughts`	true or false	—	Whether to include thought summaries. Omit to preserve the existing behavior: an explicit thinking_level requests summaries. Set limit_generations=false to return summary text in description. This does not disable thinking or return intermediate thought images.
`temperature`	number 0 to 2	—	Sampling temperature. Omit to use the provider default of 1. Explicit values use a direct Google or Vertex API.
`top_p`	number 0 to 1	—	Nucleus sampling probability. Omit to use the provider default of 0.95. Explicit values use a direct Google or Vertex API.
`max_output_tokens`	number (whole) 1 to 32768	—	Maximum output tokens for the generation. Omit to retain the provider default. Explicit values use a direct Google or Vertex API.
`num_images`	number (whole) 1 to 4	1	The number of images to generate.
`seed`	number (whole)	—	The seed for the random number generator.
`aspect_ratio`	one of auto, 21:9, 16:9, 3:2, 4:3, 5:4, 1:1, 4:5, 3:4, 2:3, 9:16, 4:1, 1:4, 8:1, 1:8	auto	The aspect ratio of the generated image. Supports extreme ratios: 4:1, 1:4, 8:1, 1:8. Use "auto" to let the model decide based on the prompt.
`output_format`	one of jpeg, png, webp	png	The format of the generated image.
`safety_tolerance`	one of 1, 2, 3, 4, 5, 6	4	The safety tolerance level for content moderation. 1 is the most strict (blocks most content), 6 is the least strict.
`system_prompt`	text		Optional system instruction that steers the model's persona and output style across the request. Leave blank to omit; when provided, it is sent as the system instruction to Gemini (or as a system message on OpenAI-compatible providers).
`resolution`	one of 0.5K, 1K, 2K, 4K	1K	The resolution of the image to generate.
`limit_generations`	true or false	true	Experimental parameter to limit the number of generations from each round of prompting to 1. Set to `True` to to disregard any instructions in the prompt regarding the number of images to generate and ignore any intermediate images generated by the model. This may affect generation quality.
`enable_web_search`	true or false	false	Enable web search for the image generation task. This will allow the model to use the latest information from the web to generate the image.
`enable_image_search`	true or false	false	Ground image generation with Google Image Search. Can be combined with enable_web_search; source attribution is returned in grounding_metadata. Uses direct Google or Vertex APIs.

Same request in code

curl https://api.spiral.ai/v1/runs \
  -H "Authorization: Bearer $SPIRAL_API_KEY" \
  -H "Content-Type: application/json" \
  -H "Idempotency-Key: $(uuidgen)" \
  -d '{
    "provider": "fal",
    "model": "fal-ai/nano-banana-2",
    "input": {
      "prompt": "An action shot of a black lab swimming in an inground suburban swimming pool. The camera is placed meticulously on the water line, dividing the image in half,…"
    }
  }'