GPT-6 Astra Text-to-3D generates textured 3D models or scenes from text descriptions, supporting fast 3D asset creation for game assets, product visualization, concept design, virtual scenes, and production workflows. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle
$8per run
3D model output
Open preview to inspect the generated asset.
A compact uncrewed exploration spacecraft resting on three landing legs inside a maintenance hangar. Build a coherent rounded fuselage, short swept wings, a sealed smoked-glass cockpit canopy, recessed engine nozzles, thin ceramic hull panels with visible seams, and landing gear firmly supporting the craft on the floor. Add a maintenance gantry, two equipment carts, organized cables and tool cases. Surround the craft with structural ribs, a high ceiling and a large closed hangar door. Use brushed metal, off-white ceramic, dark rubber and glass, with subtle surface wear and contrasting orange service panels. Keep the whole spacecraft visible and leave room around it for inspection. No people, weapons, brands, logos or readable text.
GPT-6 Astra Text to 3D turns a creative brief into a textured 3D object or scene that can be rotated, explored, and developed further. Describe an object, place, product, interior, vehicle, or imagined world, set the subject scale, and generate a portable GLB model for downstream 3D workflows.
Text-to-3D generation
Generate a 3D object or scene directly from a written prompt.
Object and scene creation
Create standalone products, props, vehicles, interiors, buildings, environments, or broader spatial concepts.
Material-aware output
Guide materials such as timber, stone, glass, painted metal, fabric, ceramic, or worn surfaces through the prompt.
PBR texture support
The exported model includes geometry and embedded PBR textures.
Portable GLB output
Download a GLB model that can be viewed in compatible browser viewers or imported into 3D applications.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Describe the object or scene to generate. Maximum length: 4000 characters. |
| size | No | Longest subject dimension in meters. Range: 0.05–20. Default: 1. |
outputs.Pricing is fixed at $8 per generation.
| Output | Cost |
|---|---|
| One GLB model generation | $8.00 |
Each successful request returns a GLB model URL in outputs.
The generated model includes geometry and embedded PBR textures. Tasks have a 120-minute timeout.
A compact exploration spacecraft resting on three landing legs inside a service hangar. Give it a rounded ceramic hull, a smoked-glass cockpit, recessed engine nozzles, and visible panel seams. Include maintenance carts and a grated floor, with enough space to inspect the craft from all sides.
prompt is required.prompt supports up to 4000 characters.size controls the longest subject dimension in meters.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/openai/gpt-6-astra/text-to-3d with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Gpt 6 Astra Text To 3d below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": 1
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/openai/gpt-6-astra/text-to-3d" \
-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="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
# 2. Poll until the prediction finishes.
while true; do
RESPONSE=$(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' "$RESULT" | jq '.outputs'; break ;;
failed|cancelled|timeout|deleted) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
*) sleep 2 ;;
esac
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/openai/gpt-6-astra/text-to-3d";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": 1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${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", "deleted"].includes(result.status)) throw new Error(JSON.stringify(result));
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",
"size": 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/openai/gpt-6-astra/text-to-3d", 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 = 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", "deleted"}:
raise RuntimeError(result)
time.sleep(2)Gpt 6 Astra Text To 3d is a OpenAI model for 3D asset generation, exposed as a REST API on WaveSpeedAI. GPT-6 Astra Text-to-3D generates textured 3D models or scenes from text descriptions, supporting fast 3D asset creation for game assets, product visualization, concept design, virtual scenes, 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/openai/openai-gpt-6-astra-text-to-3d.
Gpt 6 Astra Text To 3d starts at $8.00 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`, `size`. 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/openai/openai-gpt-6-astra-text-to-3d.
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 (OpenAI). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.