CogVideoX AI 视频生成器
An influential open video generation model family from the THUDM/Zhipu ecosystem, useful as a research and self-hosted baseline.
是否适合使用 CogVideoX?
适合用来做： open-source benchmarking, self-hosted T2V, self-hosted I2V, academic comparisons
Skip or compare first 的情况: Consumer UX depends on wrappers such as ComfyUI or hosted spaces. License and model-card terms should be checked per checkpoint.
定位： An influential open video generation model family from the THUDM/Zhipu ecosystem, useful as a research and self-hosted baseline.
公司摘要
Built by Zhipu AI / THUDM
Founded / HQ: 2019 / China
What it does: Zhipu AI is a Chinese foundation-model company with academic roots in Tsinghua's THUDM ecosystem, known for GLM/ChatGLM and open multimodal model releases including CogVideoX.
Main products/ecosystem: CogVideo, CogVideoX, CogVideoX1.5
Founders/leadership: partial/evidence pending
Scale/funding/status: private / academic-origin team, China
Relationship to CogVideoX: CogVideoX is most relevant for developers, researchers, and technically comfortable creators who want open video models they can inspect, fine-tune, or run in community tooling.
No public source link is attached yet.
价格、入口与开源状态
访问方式： Open 权重s, Aggregator
开源/闭源： Open.
Open weights can be self-hosted. Cost is mainly GPU hardware, cloud inference, or third-party hosting fees.
Price, regional access, and commercial terms need a live check before cost-sensitive recommendations.
关键得分驱动
These are the scoring dimensions most responsible for the current ranking position.
- 模型 Quality Proxy: 74CogVideoX is a strong open baseline with paper/repository evidence; current commercial leaderboard relevance is lower.
- Version Maturity: 975 version/history entries in the profile; latest public date is 2024-11-08.
- Capability Depth: 67Supported workflow tags: t2v, i2v, open_weights. Depth rewards T2V/I2V/V2V, references, native audio, editing, API, and open weights where present.
- Ecosystem Popularity: 100GitHub snapshot includes 1 repo(s), 12,924 stars, 1,319 forks, 115 open issues, latest pushed 2025-11-04T11:19:04Z.
替代选择与对比
Use these when price, access, output style, or workflow fit is uncertain.
- SeedanceByteDance's frontier video generation family for multimodal, audio-video, short-form, and cinematic creator workflows.View model对比
- KlingKuaishou's broad AI video and image generation platform for text-to-video, image-to-video, native audio, references, and creator effects.View model对比
- PixVerseA creator-friendly AI video platform with strong API coverage for text-to-video, image-to-video, transitions, extension, and reference fusion.View model对比
- ViduShengShu Technology's video model family for native audio-video storytelling, image-to-video, and scene-oriented short-form production.View model对比
它是什么
Primary content is rendered into static HTML.
CogVideoX is an open video generation family from the THUDM/Zhipu ecosystem. It evolved from the earlier CogVideo research line into practical open checkpoints for text-to-video and image-to-video.
CogVideoX is important for a benchmark site because it gives developers an accessible baseline for open-model comparison. The 1.5 release improved resolution and quality, while I2V variants widened its use beyond pure text prompts.
For consumers, CogVideoX is less polished than closed web products, but it matters for users who prefer self-hosting, community tools, or open experimentation.
版本进展
- CogVideoEarlier large-scale text-to-video research line.
开源 / 来源证据
Status: Open
Open code, weights, or model-card resources are listed; verify license terms before commercial use.
最新消息
模型-specific news entries link directly to the original publisher; article bodies stay off-site.
- CogVideo GitHub repository External source link for this model. 打开原文
- Open-source image-to-video leaderboard External source link for this model. 打开原文
- CogVideoX: Text-to-Video Diffusion 模型s with An Expert Transformer External source link for this model. 打开原文
完整得分拆解
权重ed evidence dimensions separate model quality from access, web authority, ecosystem, and source confidence. See methodology and rankings.
| 维度 | Score | 权重 | 证据 | Confidence |
|---|---|---|---|---|
| 模型 Quality Proxy | 74 | 24% | CogVideoX is a strong open baseline with paper/repository evidence; current commercial leaderboard relevance is lower. | high |
| Capability Depth | 67 | 16% | Supported workflow tags: t2v, i2v, open_weights. Depth rewards T2V/I2V/V2V, references, native audio, editing, API, and open weights where present. | medium |
| Access And Pricing | 70 | 12% | Access types: open_weights, aggregator. Pricing note: Open weights can be self-hosted. Cost is mainly GPU hardware, cloud inference, or third-party hosting fees. | medium |
| Version Maturity | 97 | 12% | 5 version/history entries in the profile; latest public date is 2024-11-08. | high |
| Ecosystem Popularity | 100 | 10% | GitHub snapshot includes 1 repo(s), 12,924 stars, 1,319 forks, 115 open issues, latest pushed 2025-11-04T11:19:04Z. | high |
| Web Authority | 38 | 10% | SEO snapshot found product domain github.com and company domain zhipuai.cn; sampled sitemap URL count is unknown. | medium |
| 公司 Distribution | 62 | 10% | Zhipu AI / THUDM distribution profile: CogVideoX is most relevant for developers, researchers, and technically comfortable creators who want open video models they can inspect, fine-tune, or run in community tooling.. | medium |
| 来源 Confidence | 100 | 6% | Profile source confidence is confirmed with 2 official URL(s), 3 feature evidence item(s), SEO present, GitHub present. | high |
限制与价格
Open weights can be self-hosted. Cost is mainly GPU hardware, cloud inference, or third-party hosting fees.