Inference Logoinference shell

Hunyuan 3D 3.1

Tencent Hunyuan 3D 3.1 Pro. 3D models from a text prompt, an image, or a front image with up to seven side views. Textured or geometry only, PBR materials, face count up to 1.5M.

text to 3dimage to 3d
run in browser →run via APIrun with your agent
maker
tencent
category
3d
input → output
text → 3d, image → 3d
runs via
api, javascript and python sdk, belt cli, mcp
updated
2026-10-28
price
$0.02/credit; Geometry 15cr, Normal 25cr, +10cr views/PBR/face-count

api reference

about

tencent hunyuan 3d 3.1 pro. 3d models from a text prompt, an image, or a front image with up to seven side views. textured or geometry only, pbr materials, face count up to 1.5m.

get an api key

create one in settings → api keys. the examples read it from INFERENCE_API_KEY.

bash
1export INFERENCE_API_KEY="inf_your_key"

1. calling the api

install the client

nothing to install. any http client works; the examples use curl, and jq to read responses. every response wraps its result in a data field.

run and get result

send "wait": true to hold the connection until the task finishes. without it the call returns right away with the task still running.

bash
12# "wait": true holds the connection until the task finishes.3# the response is {"data": {"id", "status", "status_text", "output", ...}}4curl -X POST https://api.inference.sh/apps/run \5  -H "Authorization: Bearer $INFERENCE_API_KEY" \6  -H "Content-Type: application/json" \7  -d '{8    "app": "tencent/hunyuan-3d-3-1",9    "input": {10      "prompt": "a low wooden stool with three legs",11      "views": [12        {13          "view": "left",14          "image": "https://example.com/file.png"15        }16      ]17    },18    "wait": true19  }'

stream live updates

get real-time progress updates as the task runs. ideal for showing progress bars, partial results, or long-running tasks. send "stream": true and the response is a server-sent event stream that ends when the task does.

bash
12# "stream": true answers with server-sent events until the task finishes3curl -N -X POST https://api.inference.sh/apps/run \4  -H "Authorization: Bearer $INFERENCE_API_KEY" \5  -H "Content-Type: application/json" \6  -d '{7    "app": "tencent/hunyuan-3d-3-1",8    "input": {9      "prompt": "a low wooden stool with three legs",10      "views": [11        {12          "view": "left",13          "image": "https://example.com/file.png"14        }15      ]16    },17    "stream": true18  }'

run in the background

start the task and get its id back right away, then check on it later. for long tasks, or when you'd rather be called back, see webhooks.

bash
1# without "wait" the call returns the task right away (status 1, "received")2TASK_ID=$(curl -s -X POST https://api.inference.sh/apps/run \3  -H "Authorization: Bearer $INFERENCE_API_KEY" \4  -H "Content-Type: application/json" \5  -d '{6    "app": "tencent/hunyuan-3d-3-1",7    "input": {8      "prompt": "a low wooden stool with three legs",9      "views": [10        {11          "view": "left",12          "image": "https://example.com/file.png"13        }14      ]15    }16  }' | jq -r '.data.id')1718# later: read its state. done when status is 10 (completed), 11 (failed) or 12 (cancelled)19curl -s https://api.inference.sh/tasks/$TASK_ID \20  -H "Authorization: Bearer $INFERENCE_API_KEY" | jq '.data | {status, status_text, output}'

2. authentication

the api uses api keys for authentication. see the authentication docs for detailed setup instructions.

3. files

file inputs are automatically handled by the sdk. you can pass local paths, urls, or base64 data.

automatic upload

pass urls directly or use data uris for inline content.

bash
1# pass URLs or data URIs as input values2curl -X POST https://api.inference.sh/apps/run \3  -H "Authorization: Bearer $INFERENCE_API_KEY" \4  -H "Content-Type: application/json" \5  -d '{6    "app": "tencent/hunyuan-3d-3-1",7    "input": {8      "image": "https://example.com/image.png",9      "audio": "data:audio/mp3;base64,..."10    },11    "wait": true12  }'

manual upload

you can also upload files manually and use the returned url.

bash
1# 1. create the file: the response has an upload_url and the file's uri2FILE=$(curl -s -X POST https://api.inference.sh/files \3  -H "Authorization: Bearer $INFERENCE_API_KEY" \4  -H "Content-Type: application/json" \5  -d '{"files": [{"filename": "image.png", "content_type": "image/png"}]}')67# 2. PUT the bytes to upload_url8curl -X PUT "$(echo "$FILE" | jq -r '.data[0].upload_url')" \9  -H "Content-Type: image/png" \10  --data-binary @image.png1112# 3. pass the uri as an input value13echo "$FILE" | jq -r '.data[0].uri'

4. webhooks

get notified when a task completes by providing a webhook url. when the task reaches a terminal state (completed, failed, or cancelled), a POST request is sent to your url with the task result.

bash
1curl -X POST https://api.inference.sh/apps/run \2  -H "Authorization: Bearer $INFERENCE_API_KEY" \3  -H "Content-Type: application/json" \4  -d '{5    "app": "tencent/hunyuan-3d-3-1",6    "input": {7        "prompt": "a low wooden stool with three legs",8        "views": [9            {10                "view": "left",11                "image": "https://example.com/file.png"12            }13        ]14    },15    "webhook": "https://your-server.com/webhook"16  }'

webhook payload

your endpoint receives a JSON POST with the task result:

json
1{2  "event": "task.completed",3  "timestamp": "2024-01-15T10:30:05Z",4  "data": {5    "id": "task_abc123",6    "short_id": "abc123",7    "status": 10,8    "status_text": "completed",9    "output": { ... },10    "created_at": "2024-01-15T10:30:00Z",11    "updated_at": "2024-01-15T10:30:05Z"12  }13}
eventstringtask.completed, task.failed or task.cancelled
timestampstringiso timestamp of the delivery
data.idstringtask id
data.short_idstringshort task id
data.statusnumberterminal status (10=completed, 11=failed, 12=cancelled, 13=rejected)
data.status_textstringthe status as a word: completed, failed or cancelled
data.outputobjecttask output (when completed)
data.errorstringerror message (when failed, omitted otherwise)
data.session_idstringsession id (when the task ran in a session, omitted otherwise)
data.created_atstringiso timestamp
data.updated_atstringiso timestamp

5. schema

input

promptstring

what to make, for text to 3d. used when no image is given.

example: "a low wooden stool with three legs"maxLength:1024
imagestring(file)

the object, or its front view when views are given. jpg, png or webp.

viewsarray

other views of the object, one per side: left, right, back, top, bottom, left_front, right_front. needs image; costs 10 credits more.

generate_typestring

normal is a textured model; geometry an untextured glb and costs less.

default: "Normal"
options:"Normal""Geometry"
enable_pbrboolean

pbr materials. costs 10 credits more; not with geometry.

default: false
face_countinteger

face count, 3000 to 1500000. empty: 500000. setting it costs 10 credits more.

min:3000max:1500000

output

filesarray

every other file tencent returned.

modelstring(file)*

the 3d model.

previewstring(file)

a preview render of the model.

other 3d apps

Tripo Auto Rig

tripo/tripo-rig

$0.01/credit (rig check free, auto rig 25 credits, retarget 10 credits/animation)

Hunyuan 3D Auto Rigging

tencent/hunyuan-3d-auto-rigging

$0.02/credit, 10 credits/call ($0.20/call)

Rodin Gen-2.5

hyper3d/rodin-gen-2-5

$0.58/credit; Extreme-High tier +0.5 credits, texture extreme-high +2 credits

Meshy 7.1

meshy/meshy-7-1

$0.0127/Meshy credit (20 base + 10-15 texture + 5 hi-res geo)

Tripo 3.1

tripo/tripo-3-1

$0.01/credit, $0.10-$0.50/run (text: 10-40 credits, image/multiview: 20-50 credits)

more from tencent

Hunyuan 3D Auto Rigging

tencent/hunyuan-3d-auto-rigging

$0.02/credit, 10 credits/call ($0.20/call)

new models

  1. Tripo Auto Rig

    tripo/tripo-rig

    3d$0.01/credit (rig check free, auto rig 25 credits, retarget 10 credits/animation)

  2. Hunyuan 3D Auto Rigging

    tencent/hunyuan-3d-auto-rigging

    3d$0.02/credit, 10 credits/call ($0.20/call)

  3. Rodin Gen-2.5

    hyper3d/rodin-gen-2-5

    3d$0.58/credit; Extreme-High tier +0.5 credits, texture extreme-high +2 credits

  4. Meshy 7.1

    meshy/meshy-7-1

    3d$0.0127/Meshy credit (20 base + 10-15 texture + 5 hi-res geo)

  5. Tripo 3.1

    tripo/tripo-3-1

    3d$0.01/credit, $0.10-$0.50/run (text: 10-40 credits, image/multiview: 20-50 credits)

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