Openai Gpt 6 Astra Text To 3d API Documentation

Openai Gpt 6 Astra Text To 3d API Documentation

Playground

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.

Features

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.

Why Choose This?

• 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.

Parameters

How to Use

1. Write your prompt — Describe the subject, shape, materials, surrounding space, and important visual details.
2. Set size optional — Define the longest subject dimension when a specific physical scale is needed.
3. Submit — Generate the 3D result.
4. Download the GLB — Retrieve the generated GLB model URL from outputs.

Pricing

Pricing is fixed at $8 per generation.

Output

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.

Best Use Cases

• Text-based 3D asset creation — Generate 3D models directly from creative briefs.
• Product visualization — Turn product ideas into 3D assets for inspection and presentation.
• Concept design — Explore vehicles, architecture, props, interiors, environments, and imagined worlds.
• Scene prototyping — Generate spatial concepts such as courtyards, workshops, hangars, rooms, or landscapes.
• Interactive experiences — Create starting assets for viewers, prototypes, games, or immersive workflows.
• Creative iteration — Compare multiple design directions from different prompt variations.

Pro Tips

• Start with the overall silhouette, structure, and scene layout.
• Describe how parts connect and how the subject relates to its surroundings.
• Include material details such as surface wear, glass, metal, stone, fabric, ceramic, or wood.
• For scenes, describe both the main subject and the surrounding space.
• Add practical details such as door openings, furniture placement, props, lighting, or landscape elements.
• Inspect the first result from several angles before refining the next prompt.
• Generated assets may need cleanup for production use.
• The result is a generated interpretation, not a dimensionally exact CAD model.

Example Prompt

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.

Notes

• prompt is required.
• prompt supports up to 4000 characters.
• size controls the longest subject dimension in meters.
• Exact physical accuracy, likeness, and photorealism are not guaranteed.
• Lighting and material appearance may vary by 3D viewer.

Related Models

• GPT-6 Astra Image to 3D — Generate a textured 3D object or scene from reference images.

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",
  "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 "Authorization: Bearer ${WAVESPEED_API_KEY}" \
  -H "Content-Type: application/json" \
  -d "${REQUEST_BODY}")

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

  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
done

Parameters

Task Submission Parameters

Request Parameters

Response Parameters

Result Request Parameters

Result Response Parameters