Google Nano Banana 2 Fast (Gemini 3.1 Flash Image) is the cheapest Nano Banana 2 option, starting at just $0.045 per image. Delivers fast text-to-image generation with 2K default output and 4K support. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle

$0.045per run·~22 / $1

an epic sci-fi movie poster, a lone astronaut standing on a vast desert planet, a gigantic ringed planet rising in the sky, dramatic sunset lighting, blowing sand, cinematic composition, IMAX scale, photorealistic, space for title at the top, film poster design, ultra detailed
!["resolution": "8K ultra high definition",
"style": "hyper - realistic food illustration with editorial infographic overlays",
"lighting": "soft directional key light, subtle rimlight'
"scene_description": "A vertical stack of cake slices floating above a plate against soft pink gradient background",
"motion_elements": ["floating fruits", "floating macarons", "crumbs suspended in air"],
"text_design": {
"ingredient_name_color": "metallic gold",
"indicator_lines": "long, thin, smooth golden lines with rounded corners"](https://static.wavespeed.ai/media/images/1783682980959071421_Nk3dnwFP.webp)
"resolution": "8K ultra high definition", "style": "hyper - realistic food illustration with editorial infographic overlays", "lighting": "soft directional key light, subtle rimlight' "scene_description": "A vertical stack of cake slices floating above a plate against soft pink gradient background", "motion_elements": ["floating fruits", "floating macarons", "crumbs suspended in air"], "text_design": { "ingredient_name_color": "metallic gold", "indicator_lines": "long, thin, smooth golden lines with rounded corners"

a man standing in the middle of a busy city street while everything around him is frozen in time, suspended rain drops and floating papers, dramatic cinematic lighting, epic composition, hyper realistic, movie still, 8k

a quiet train platform at sunset, golden sunlight streaming through the station roof, a traveler holding a suitcase waiting alone, cinematic composition, film still, warm color grading, 35mm lens
Nano Banana 2 Text-to-Image Fast (Gemini 3.1 Flash Image) is the cheapest Nano Banana 2 option, starting at just $0.045 per image. It delivers faster generation times while maintaining high visual quality, with 2K resolution by default.
Lowest cost Nano Banana 2 The cheapest Nano Banana 2 option at just $0.045 per image.
Speed-optimized Faster generation times compared to the standard variant.
High-resolution default 2K output by default with 4K available for maximum detail.
Flexible aspect ratios Multiple options including 1:1, 3:2, 2:3, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9, 1:4, 4:1, 1:8, and 8:1.
Cinematic quality Excels at photorealistic scenes with atmospheric lighting and anamorphic lens effects.
Prompt Enhancer Built-in tool to automatically improve your descriptions.
Format choice Export in PNG or JPEG format.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text description of the desired image |
| aspect_ratio | No | Aspect ratio: 1:1, 3:2, 2:3, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9, 1:4, 4:1, 1:8, 8:1 |
| resolution | No | Output resolution: 2k (default), 4k |
| enable_web_search | No | Enable web search to enhance generation with real-time info (default: false) |
| output_format | No | Output format: png (default), jpeg |
| Resolution | Cost |
|---|---|
| 2k | $0.045 |
| 4k | $0.05 |
| Web search | +$0.014 |
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/google/nano-banana-2/text-to-image-fast with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Nano Banana 2 Text To Image Fast below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "1:1",
"resolution": "2k",
"enable_web_search": false,
"output_format": "png"
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/google/nano-banana-2/text-to-image-fast" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d "$REQUEST_BODY")
TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
printf 'Submission response did not contain a prediction id
' >&2
exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi
# 2. Poll until the prediction finishes.
while true; do
RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
-H "Authorization: Bearer $WAVESPEED_API_KEY")
RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
case "$STATUS" in
completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
created|processing) sleep 2 ;;
*) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
esac
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/google/nano-banana-2/text-to-image-fast";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');
async function requestJson(url, options = {}) {
const response = await fetch(url, options);
if (!response.ok) throw new Error(await response.text());
return response.json();
}
// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
method: "POST",
headers: {
"Authorization": `Bearer ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "1:1",
"resolution": "2k",
"enable_web_search": false,
"output_format": "png"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;
// 2. Poll until the prediction finishes.
while (true) {
const resultBody = await requestJson(resultUrl, {
headers: { "Authorization": `Bearer ${apiKey}` },
});
const result = resultBody.data ?? resultBody;
if (result.status === "completed") {
console.log(result.outputs);
break;
}
if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
await new Promise(resolve => setTimeout(resolve, 2000));
}import json
import os
import time
from urllib.request import Request, urlopen
api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "1:1",
"resolution": "2k",
"enable_web_search": False,
"output_format": "png"
}
def request_json(url, data=None):
request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
with urlopen(request) as response:
return json.load(response)
# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/google/nano-banana-2/text-to-image-fast", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"
# 2. Poll until the prediction finishes.
while True:
result_body = request_json(result_url)
result = result_body.get("data", result_body)
status = result.get("status")
if status == "completed":
print(result.get("outputs", []))
break
if status in {"failed", "cancelled", "timeout"}:
raise RuntimeError(result)
if status not in {"created", "processing"}:
raise RuntimeError(f"Unexpected status: {status}")
time.sleep(2)Nano Banana 2 Text To Image Fast is a Google model for image generation, exposed as a REST API on WaveSpeedAI. Google Nano Banana 2 Fast (Gemini 3.1 Flash Image) is the cheapest Nano Banana 2 option, starting at just $0.045 per image. Delivers fast text-to-image generation with 2K default output and 4K support. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.
POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/google/google-nano-banana-2-text-to-image-fast.
Nano Banana 2 Text To Image Fast starts at $0.045 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.
Key inputs: `prompt`, `aspect_ratio`, `resolution`, `enable_base64_output`, `enable_sync_mode`, `enable_web_search`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/google/google-nano-banana-2-text-to-image-fast.
Median end-to-end generation time on WaveSpeedAI is around 53 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.
Commercial usage rights depend on the model's license, set by its provider (Google). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.