Qwen Image 3.0 Pro Text-to-Image is a professional-grade image generation model that creates high-quality images from text prompts, with advanced prompt understanding, strong visual quality, and up to 2K output for creative and production workflows. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Ocioso

$0.04por execução·~25 / $1

A young woman carrying a candle explores an abandoned palace at night. She enters a long hall of mirrors and notices that one reflection is moving differently from her. The camera begins behind her shoulder, glides smoothly between the mirrors, and repeatedly changes focus between her real body and the false reflection. The reflection suddenly turns and runs deeper into the mirrored corridor. She follows it until every mirror reveals a different version of her life. Gothic mystery, elegant Steadicam movement, candlelit shadows, slow-building tension, highly detailed production design.
Qwen Image 3.0 Pro is a high-quality text-to-image model built for production-grade visual creation, with strong detail rendering, prompt adherence, and flexible output resolution. It is designed for polished image generation across character art, portraits, fashion visuals, commercial assets, marketing content, and creative concept workflows.
Pro-level image quality
Generate polished images with stronger detail, texture, and visual fidelity.
Strong prompt adherence
Follow detailed prompts for subjects, composition, lighting, style, and visual attributes.
Flexible output sizing
Choose 1k or 2k resolution and select the aspect ratio that matches your target layout.
Prompt expansion support
Enable intelligent prompt expansion to enrich the prompt before generation.
Seed control
Use a fixed seed for reproducible results, or -1 for random generation.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text description of the desired image. |
| resolution | No | Output resolution tier: 1k or 2k. Default: 1k. |
| aspect_ratio | No | Output aspect ratio. Default: 1:1. |
| enable_prompt_expansion | No | Enable intelligent prompt expansion. Default: true. |
| seed | No | Random seed for reproducibility. Use -1 for a random seed. |
1k for standard generation or 2k when higher-resolution output is needed.| Output | Cost |
|---|---|
| 1k image | $0.04 |
| 2k image | $0.075 |
1k for faster prompt iteration and lower-cost generation.2k when final image detail or higher-resolution output matters.enable_prompt_expansion enabled when you want richer prompt interpretation.seed when comparing prompt variations or reproducing similar outputs.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/alibaba/qwen-image-3.0-pro/text-to-image with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Qwen Image 3.0 Pro Text To Image below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "1:1",
"resolution": "1k",
"enable_prompt_expansion": true,
"seed": -1
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/alibaba/qwen-image-3.0-pro/text-to-image" \
-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/alibaba/qwen-image-3.0-pro/text-to-image";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "1:1",
"resolution": "1k",
"enable_prompt_expansion": true,
"seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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": "1k",
"enable_prompt_expansion": True,
"seed": -1
}
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/alibaba/qwen-image-3.0-pro/text-to-image", 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)Qwen Image 3.0 Pro Text To Image is a Alibaba model for image generation, exposed as a REST API on WaveSpeedAI. Qwen Image 3.0 Pro Text-to-Image is a professional-grade image generation model that creates high-quality images from text prompts, with advanced prompt understanding, strong visual quality, and up to 2K output for creative and production workflows. 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/alibaba/alibaba-qwen-image-3.0-pro-text-to-image.
Qwen Image 3.0 Pro Text To Image starts at $0.040 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`, `seed`, `enable_prompt_expansion`. 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/alibaba/alibaba-qwen-image-3.0-pro-text-to-image.
Sign up for a free WaveSpeedAI account to claim starter credits, copy your API key from /accesskey, then call the endpoint shown in the API tab of the playground. The playground also auto-generates a code sample in Python, JavaScript, or cURL for the parameters you've set.
Commercial usage rights depend on the model's license, set by its provider (Alibaba). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.