Google Nano Banana Pro Text To Image API Documentation

Google Nano Banana Pro Text To Image API Documentation

Playground

Google’s Nano Banana pro (Gemini 3.0 Pro Image) is a cutting-edge text-to-image model enabling high-res 4K image generation optimized for phones. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Features

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.

• Need higher resolution? Try Nano Banana Pro Text-to-Image Ultra
• Need image editing? Try Nano Banana Pro Edit

🌟 Why it stands out

• Natural-language, context-aware editing Modify images using simple text instructions. The model understands scene structure, objects, and relationships for realistic edits.

• Multilingual on-image text with auto translation Generate and edit text inside images in multiple languages, with improved font clarity and layout.

• Camera-style controls Support for camera-related parameters such as angle, focus, depth of field, and color adjustment for more photographic results.

• 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.

• Consistent character and style rendering Maintain character identity, brand elements, and overall style across related images.

⚙️ Capabilities

• Input: text prompt

• Output: high-quality image (JPEG/PNG)

• Supports multiple aspect ratios and output format

• 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

💡 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.

🌏 Where Nano Banana Pro Fits In

Compare Nano Banana Pro T2I with:

• 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.

• 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.

• 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.

• 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.

• 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

set -euo pipefail

export WAVESPEED_API_KEY="your-api-key"

REQUEST_BODY=$(cat <<'JSON'
{
  "prompt": "A cinematic ocean wave at sunrise, highly detailed",
  "aspect_ratio": "1:1",
  "resolution": "1k",
  "output_format": "png"
}
JSON
)

# 1. Submit the prediction once. Never retry this request automatically:
# a retry after an unclear failure can create a second, billed task.
CHECK='The task may already exist. Check History or the predictions list before submitting again.'
if ! SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  --connect-timeout 10 --max-time 60 \
  -X POST "https://api.wavespeed.ai/api/v3/google/nano-banana-pro/text-to-image" \
  -H "Authorization: Bearer ${WAVESPEED_API_KEY}" \
  -H "Content-Type: application/json" \
  -d "${REQUEST_BODY}"); then
  printf 'Submit failed. %s\n' "${CHECK}" >&2
  exit 1
fi

TASK=$(printf '%s' "${SUBMIT_RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "${TASK}" | jq -r '.id // empty')
if [ -z "${PREDICTION_ID}" ]; then
  printf 'Submission response did not contain a prediction id. %s\n' "${CHECK}" >&2
  exit 1
fi
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/${PREDICTION_ID}/result"

# 2. Poll until the prediction reaches a final status.
BODY_FILE=$(mktemp)
trap 'rm -f "${BODY_FILE}"' EXIT
while true; do
  # Transient errors (429, 5xx) are retried, honoring Retry-After.
  HTTP_CODE=$(curl --silent --show-error --fail-with-body \
    --connect-timeout 10 --max-time 60 --retry 5 --retry-delay 2 \
    --output "${BODY_FILE}" --write-out '%{http_code}' \
    "${RESULT_URL}" \
    -H "Authorization: Bearer ${WAVESPEED_API_KEY}" || true)
  case "${HTTP_CODE}" in
    2??) ;;
    000|429|5??)  # dropped connection or temporary error: keep polling
      printf 'Task %s: HTTP %s, polling again in 10s\n' "${PREDICTION_ID}" "${HTTP_CODE}" >&2
      sleep 10; continue ;;
    *) printf 'Task %s: HTTP %s\n' "${PREDICTION_ID}" "${HTTP_CODE}" >&2
      cat "${BODY_FILE}" >&2; exit 1 ;;
  esac
  RESULT=$(jq 'if type == "object" and has("data") then .data else . end' "${BODY_FILE}")
  STATUS=$(printf '%s' "${RESULT}" | jq -r '.status // empty')

  case "${STATUS}" in
    completed) printf '%s\n' "${RESULT}" | jq '.outputs'; break ;;
    failed|cancelled|timeout|deleted)
      printf 'Task %s ended with status %s\n' "${PREDICTION_ID}" "${STATUS}" >&2
      printf '%s\n' "${RESULT}" | jq . >&2; exit 1 ;;
    *) sleep 2 ;;
  esac
done

Parameters

Task Submission Parameters

Request Parameters

Every model also accepts the global request options enable_sync_mode and enable_base64_output; see their pages for details.

Response Parameters

Result Request Parameters

Result Response Parameters