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SPECIFICTION
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Hi3D vs InstantMesh

Hi3D

hi3d.ai

InstantMesh

github.com

Side by side

FigureHi3DInstantMesh
Price fromFreeFree
Free planYesYes
Free trial——
Open source——
WebYes—
Linux—Yes
iOSYes—
AndroidYes—
APIYes—
Self-hosted—Yes
Plans published41
Spec score · AI 3D Model Generators8.1 (#12)7.7 (#17)

Plans

Hi3D

  • FreeFree
  • MaxPrice not published
  • ProPrice not published
  • UltraPrice not published

Hi3D pricing →

InstantMesh

  • InstantMeshFree

InstantMesh pricing →

In their own words

Hi3D

Hi3D is an AI image-to-3D tool that turns images into models using Math Magic’s Sparc3D and Ultra3D models. It supports both single-image and multi-view generation, along with text-to-3D, AI texturing, 3D relief generation, and a tool for dividing characters into printable parts. Exports include FBX, GLB, OBJ, STL, and USDZ, and models can be sent directly to Bambu Studio and OrcaSlicer. The wider integration list includes Snapmaker, Creality Print, ElegooSlicer, ComfyUI, and Blender. Developers and enterprises can use its Open Platform API; API operations are priced by model and resolution, with one credit listed at $0.020. The Free plan includes 100 lifetime credits, up to 10 asset generations, one concurrent task, and storage for seven models; those models are public under CC BY 4.0. Paid plans include private and commercial use, and the listed pricing note says paid plans start at $19.90/mo. Hi3D Scan supports video capture or gallery upload for model reconstruction. Platforms include Android, iOS, web, and API.

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InstantMesh

InstantMesh is a free, Apache-licensed framework that generates a 3D mesh from one image using the LRM/Instant3D architecture. It runs on Linux in a local or self-hosted setup. A Gradio demo starts with `python app.py`, while `run.py` offers command-line generation. The default export is an OBJ mesh with vertex colors; an alternative adds a texture map and takes longer. rembg can segment the foreground object, or that step can be skipped when the source already has an alpha mask. Four sparse-view reconstruction model variants and a customized Zero123++ UNet for white-background image generation are included. Docker deployment is documented. The recommended environment uses Python 3.10 or later, PyTorch 2.1.0 or later and CUDA 12.1 or later; two GPUs can reduce demo memory use. Training code is included, but the training dataset is unavailable because of its size. Community implementations are noted for ComfyUI, Replicate and Google Colab.

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