Baichuan-Omni
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Baichuan,Westlake University,Zhejiang University (ZJU)
- Organisation type
- Industry,Academia,Academia
- Country
- China
- Published
- 11 October 2024
- Authors
- Yadong Li, Haoze Sun, Mingan Lin, Tianpeng Li, Guosheng Dong, Tao Zhang, Bowen Ding, Wei Song, Zhenglin Cheng, Yuqi Huo, Song Chen, Xu Li, Da Pan, Shusen Zhang, Xin Wu, Zheng Liang, Jun Liu, Tao Zhang, Keer Lu, Yaqi Zhao, Yanjun Shen, Fan Yang, Kaicheng Yu, Tao Lin, Jianhua Xu, Zenan Zhou
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language, Vision, Multimodal, Audio, Speech, Video
- Task
- Visual question answering, Language modeling/generation, Video description, Speech synthesis, Image captioning
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 7B
- Training data
- tokens
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
code and weights are supposed to be here, but not yet: https://github.com/westlake-baichuan-mllm/bc-omni?tab=readme-ov-file
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 35
Sources
Where this record came from and when it was last checked.
- Reference
- Baichuan-Omni Technical Report
- Last updated
- 25 May 2026
What the numbers mean
Background
Baichuan-Omni was published by Baichuan,Westlake University,Zhejiang University (ZJU), in China, in October 2024. The organisation is categorised as industry,Academia,Academia.
It works in Language, Vision, Multimodal, Audio, Speech, Video, and is recorded as doing visual question answering, Language modeling/generation, Video description, Speech synthesis, Image captioning.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
Baichuan-Omni — common questions
Is Baichuan-Omni open source?
No. Baichuan-Omni has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Baichuan-Omni have?
Baichuan-Omni has 7B parameters. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created Baichuan-Omni?
Baichuan-Omni was published by Baichuan,Westlake University,Zhejiang University (ZJU), based in China, categorised as industry,Academia,Academia.
When was Baichuan-Omni released?
Baichuan-Omni was published in October 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is Baichuan-Omni used for?
Baichuan-Omni works in Language, Vision, Multimodal, Audio, Speech, Video, and is recorded as handling visual question answering, Language modeling/generation, Video description, Speech synthesis, Image captioning. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
What GPU do I need to run Baichuan-Omni?
None. Baichuan-Omni is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.