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Crisp3D vs InstantMesh

Crisp3D

crisp3d.ai

InstantMesh

github.com

Side by side

FigureCrisp3DInstantMesh
Price from$2.66/moFree
Free planYesYes
Free trial——
Open source——
WebYes—
Linux—Yes
Self-hosted—Yes
Plans published71
Spec score · AI 3D Model Generators8.3 (#7)7.7 (#17)
PurposeCrisp3D turns text prompts, photos, sketches, and concept images into 3D models for printing.InstantMesh is a feed-forward framework for generating a 3D mesh from a single image using the LRM/Instant3D architecture.

Plans

Crisp3D

  • FreeFree
  • 1,000-credit top-up$22, paid once
  • Pro$31.92 a year
  • Ultra$95.92 a year
  • 5,000-credit top-up$99, paid once
  • 10,000-credit top-up$150, paid once
  • Basic$191.04 a year

Crisp3D pricing →

InstantMesh

  • InstantMeshFree

InstantMesh pricing →

In their own words

Crisp3D

Crisp3D is a web tool for creating 3D models for printing from text prompts, photos, sketches, and concept images. It also offers AI texturing, 3D segmentation, and AI retopology to simplify dense geometry. Users can upload supported existing models to preview colors and finishes, split parts, add joints, or simplify geometry. Listed AI models include Tripo3D, Meshy, Hunyuan3D, and Hi3D; supported slicers include Bambu Studio, OrcaSlicer, Creality Print, ElegooSlicer, and Snapmaker Orca. Exports are available in GLB, FBX, OBJ, STL, and 3MF. The free plan permits one concurrent task and limited AI models; its models are public, non-commercial, and unavailable for download. Paid plans start at $25.90/mo. Paid users can choose private visibility, and commercial use is subject to the applicable license, platform terms, and rights in input materials. Subscription credits refresh each billing cycle and do not carry over. Crisp3D says uploaded files, prompts, and generated outputs are not used to train or improve AI models unless users opt into a separate program.

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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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