CogVideoX vs Genmo

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

Tagline

CogVideoX

Open-source text-to-video and image-to-video diffusion transformer from Zhipu AI, runnable on consumer GPUs.

Genmo

Open-source text-to-video model (Mochi 1) with a hosted playground for turning prompts into short clips.

Pricing

CogVideoX

Free· Open-source weights; commercial API via bigmodel.cn

Genmo

Freemium· Free: $0/month · Lite: Loading... · Standard: Loading...

Free trial

CogVideoX

Yes

Genmo

Yes

API

CogVideoX

Yes

Genmo

Not listed

Platforms

CogVideoX

api

Genmo

web

Open source

CogVideoX

Yes · Apache-2.0

Genmo

Yes · Apache-2.0

GitHub stars

CogVideoX

13,048

checked 2026-09-29

Genmo

3,733

checked 2026-09-29

Last GitHub push

CogVideoX

2025-11-04

Genmo

2025-11-14

First commit

CogVideoX

2022-05

Genmo

2024-09

Model used

CogVideoX

CogVideoX / CogVideoX1.5 (diffusion transformer)

Genmo

Mochi 1

Best for

CogVideoX

Pick CogVideoX if you want to self-host or fine-tune a capable text/image-to-video model on a single consumer or prosumer GPU.

Genmo

Pick Genmo if you want a genuinely open-source text-to-video model you can self-host or fine-tune, with a hosted playground to prototype before committing.

Not for

CogVideoX

Skip it if you need real-time generation, minute-long clips, or a polished managed UI rather than a Python and ComfyUI workflow.

Genmo

Skip it if you need long, high-resolution finished clips, fine-grained camera and character control, or a mature paid API with SLAs.

Editorial score

CogVideoX

7.3 / 10

Genmo

8.0 / 10

Use cases

CogVideoX

text-to-videoimage-to-videovideo-continuationresearchfine-tuning

Genmo

text-to-videogenerative-videoresearchprototypingshort-form-clips

Pros

CogVideoX

Genuinely runs on consumer GPUs with INT8 quantization (under 5GB VRAM)
Permissive Apache 2.0 license on code and the 2B model weights
Strong ecosystem: Diffusers, ComfyUI, LoRA fine-tuning, xDiT parallel inference
Supports text-to-video, image-to-video, and video continuation in one family
Backed by Zhipu AI with active releases through 2025 (CogKit, DDIM Inverse)

Genmo

Open weights on Hugging Face and GitHub — self-hostable
Free in-browser playground to try before installing
Strong motion quality for an open text-to-video model
Attractive to researchers who need reproducible pipelines

Cons

CogVideoX

English-only prompts; other languages need LLM translation first
Slow inference: ~1000s per 5s clip for 1.5-5B on an A100
5B weights use a custom non-Apache license with usage restrictions
Max output is 10 seconds at 16fps; not competitive on length with Sora/Veo

Genmo

Local inference needs serious GPU horsepower
Clip length and resolution trail closed competitors like Runway and Sora
Pricing and rate limits on the hosted playground are opaque
No documented public API tier at launch

Website

CogVideoX

github.com

Genmo

www.genmo.ai

Editorial score: rule-based, 0–10, from AI-assisted profile inputs (see /methodology) — not a user rating; “—” means unscored. “Not listed” means we have no record of it, not that it is absent. GitHub figures and prices carry the date they were checked or captured; prices are shown as published, unconverted.

Pick CogVideoX if

✅ Genuinely runs on consumer GPUs with INT8 quantization (under 5GB VRAM)
✅ Permissive Apache 2.0 license on code and the 2B model weights
✅ Strong ecosystem: Diffusers, ComfyUI, LoRA fine-tuning, xDiT parallel inference
✅ Supports text-to-video, image-to-video, and video continuation in one family

Pick Genmo if

✅ Open weights on Hugging Face and GitHub — self-hostable
✅ Free in-browser playground to try before installing
✅ Strong motion quality for an open text-to-video model
✅ Attractive to researchers who need reproducible pipelines

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