Qwen-VL-Max
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
- Alibaba
- Organisation type
- Industry
- Country
- China
- Published
- 25 January 2024
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Multimodal, Language, Vision
- Task
- Chat, Image captioning, Face recognition, Visual question answering
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
Not stated. Qwen-VL (less capable, presumably smaller version) is 9.6B Upd: 7B parameters mentioned here https://github.com/QwenLM/Qwen-VL#qwen-vl-plus
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
- API access
- Training code
- Unreleased
https://help.aliyun.com/zh/dashscope/developer-reference/tongyi-qianwen-vl-plus-api
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Likely above 10²³ FLOP
- Yes
- Why it is tracked
- SOTA improvement
- Record confidence
- Confident
"Notably, Qwen-VL-Max outperforms both GPT-4V from OpenAI and Gemini from Google in tasks on Chinese question answering and Chinese text comprehension"
Sources
Where this record came from and when it was last checked.
- Reference
- Introducing Qwen-VL
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Qwen-VL-Max was published by Alibaba, in China, in January 2024. It comes out of industry.
It works in Multimodal, Language, Vision, and is recorded as doing chat, Image captioning, Face recognition, Visual question answering.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
Its inclusion criterion is sOTA improvement.
Answers
Qwen-VL-Max — common questions
Is Qwen-VL-Max open source?
No. Qwen-VL-Max has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Qwen-VL-Max have?
Qwen-VL-Max has 7B parameters. Not stated. Qwen-VL (less capable, presumably smaller version) is 9.6B Upd: 7B parameters mentioned here https://github.com/QwenLM/Qwen-VL#qwen-vl-plus. 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 Qwen-VL-Max?
Qwen-VL-Max was published by Alibaba, based in China, categorised as industry.
When was Qwen-VL-Max released?
Qwen-VL-Max was published in January 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 Qwen-VL-Max used for?
Qwen-VL-Max works in Multimodal, Language, Vision, and is recorded as handling chat, Image captioning, Face recognition, Visual question answering. 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 Qwen-VL-Max?
None. Qwen-VL-Max 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.