# Hunyuan 3D 3.1

> Run Hunyuan 3D 3.1 by tencent online or via API. $0.02/credit; Geometry 15cr, Normal 25cr, +10cr views/PBR/face-count, no setup.

**URL:** https://inference.sh/apps/tencent/hunyuan-3d-3-1
**Last updated:** 2026-10-28

---

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.

- app: `tencent/hunyuan-3d-3-1`
- maker: [tencent](https://inference.sh/apps/tencent.md)
- category: [3d](https://inference.sh/apps/category/3d.md)
- input → output: text → 3d, image → 3d
- tasks: [text to 3d](https://inference.sh/apps/tag/text-to-3d.md), [image to 3d](https://inference.sh/apps/tag/image-to-3d.md)
- price: $0.02/credit; Geometry 15cr, Normal 25cr, +10cr views/PBR/face-count
- runs via: api, javascript and python sdk, belt cli, mcp

## hunyuan-3d-3-1 API Guide

### 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](https://app.inference.sh/settings/keys). The examples read it from `INFERENCE_API_KEY`.

```bash
export INFERENCE_API_KEY="inf_your_key"
```

### 1. Calling the API

#### Install the Client

```bash
pip install inferencesh
```

#### Run and Get Result
Submit a request and wait for the final result.

```python
import os
from inferencesh import inference

client = inference(api_key=os.environ["INFERENCE_API_KEY"])

## version @6mbay0dg pinned
result = client.run({
        "app": "tencent/hunyuan-3d-3-1@6mbay0dg",
        "input": {
            "prompt": "a low wooden stool with three legs",
            "views": [
                {
                    "view": "left",
                    "image": "https://example.com/file.png"
                }
            ]
        }
    })

print(result["output"])
```

#### Stream Live Updates
Get real-time progress updates as the task runs.

```python
import os
from inferencesh import inference

client = inference(api_key=os.environ["INFERENCE_API_KEY"])

## version @6mbay0dg pinned
## stream=True yields updates as they arrive
for update in client.run({
        "app": "tencent/hunyuan-3d-3-1@6mbay0dg",
        "input": {
            "prompt": "a low wooden stool with three legs",
            "views": [
                {
                    "view": "left",
                    "image": "https://example.com/file.png"
                }
            ]
        }
    }, stream=True):
    if update.get("progress"):
        print(f"progress: {update['progress']}%")
    if update.get("output"):
        print(f"output: {update['output']}")
```

#### Run in the Background
Start the task and get its id back right away, then check on it with `GET /tasks/{id}`. It is done when `status` is 10 (completed), 11 (failed) or 12 (cancelled).

```python
import os
from inferencesh import inference, TaskStatus

client = inference(api_key=os.environ["INFERENCE_API_KEY"])

## returns as soon as the task is created
task = client.run({
        "app": "tencent/hunyuan-3d-3-1@6mbay0dg",
        "input": {
            "prompt": "a low wooden stool with three legs",
            "views": [
                {
                    "view": "left",
                    "image": "https://example.com/file.png"
                }
            ]
        }
    }, wait=False)

## later: read its current state
state = client.tasks.get(task["id"]).data
if state["status"] == TaskStatus.COMPLETED:
    print(state["output"])
```

### 2. Files

#### Automatic Upload

```python
## local file paths are automatically uploaded
result = client.run({
    "app": "tencent/hunyuan-3d-3-1@6mbay0dg",
    "input": {
        "image": "/path/to/local/image.png",  # detected & uploaded
        "audio": "https://example.com/audio.mp3",  # url passed through
    }
})
```

#### Manual Upload

```python
## upload and get a hosted URL
file = client.files.upload("/path/to/file.png")
print(file.uri)  # https://cloud.inference.sh/...
```

### 3. Schema

#### Input
- **prompt** (string): What to make, for text to 3D. Used when no image is given.
- **image** (string): The object, or its front view when views are given. jpg, png or webp.
- **views** (array): Other views of the object, one per side: left, right, back, top, bottom, left_front, right_front. Needs image; costs 10 credits more.
- **generate_type** (string): Normal is a textured model; Geometry an untextured GLB and costs less.
- **enable_pbr** (boolean): PBR materials. Costs 10 credits more; not with Geometry.
- **face_count** (integer): Face count, 3000 to 1500000. Empty: 500000. Setting it costs 10 credits more.

#### Output
- **files** (array): Every other file Tencent returned.
- **model** (string): The 3D model.
- **preview** (string): A preview render of the model.

## other 3d apps

- [Tripo Auto Rig](https://inference.sh/apps/tripo/tripo-rig.md): $0.01/credit (rig check free, auto rig 25 credits, retarget 10 credits/animation)
- [Hunyuan 3D Auto Rigging](https://inference.sh/apps/tencent/hunyuan-3d-auto-rigging.md): $0.02/credit, 10 credits/call ($0.20/call)
- [Rodin Gen-2.5](https://inference.sh/apps/hyper3d/rodin-gen-2-5.md): $0.58/credit; Extreme-High tier +0.5 credits, texture extreme-high +2 credits
- [Meshy 7.1](https://inference.sh/apps/meshy/meshy-7-1.md): $0.0127/Meshy credit (20 base + 10-15 texture + 5 hi-res geo)
- [Tripo 3.1](https://inference.sh/apps/tripo/tripo-3-1.md): $0.01/credit, $0.10-$0.50/run (text: 10-40 credits, image/multiview: 20-50 credits)

## faq

### how much does Hunyuan 3D 3.1 cost?

$0.02/credit; Geometry 15cr, Normal 25cr, +10cr views/PBR/face-count. you pay per run from your inference shell credits. no subscription is required.

### how do I run Hunyuan 3D 3.1 via api?

call tencent/hunyuan-3d-3-1 with the javascript or python sdk, the rest api, or `belt app run tencent/hunyuan-3d-3-1` from a terminal. the api reference on this page lists every input.

### who makes Hunyuan 3D 3.1?

Hunyuan 3D 3.1 is made by tencent and runs on inference shell. every tencent app is listed at https://inference.sh/apps/tencent.

### can my agent use Hunyuan 3D 3.1?

yes. agents call it as a tool through the inference shell mcp server (https://api.inference.sh/mcp), the belt cli or the sdk, and pay per run like you do.

### what are the alternatives to Hunyuan 3D 3.1?

other 3d apps on inference shell include Tripo Auto Rig, Hunyuan 3D Auto Rigging, Rodin Gen-2.5, Meshy 7.1. they run through the same api, so switching is a one-line change.