MiniMax-VL-01
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
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
- MiniMax
- Organisation type
- Industry
- Country
- China
- Published
- 14 January 2025
- Authors
- MiniMax, Aonian Li, Bangwei Gong, Bo Yang, Boji Shan, Chang Liu, Cheng Zhu, Chunhao Zhang, Congchao Guo, Da Chen, Dong Li, Enwei Jiao, Gengxin Li, Guojun Zhang, Haohai Sun, Houze Dong, Jiadai Zhu, Jiaqi Zhuang, Jiayuan Song, Jin Zhu, Jingtao Han, Jingyang Li, Junbin Xie, Junhao Xu, Junjie Yan, Kaishun Zhang, Kecheng Xiao, Kexi Kang, Le Han, Leyang Wang, Lianfei Yu, Liheng Feng, Lin Zheng, Linbo Ch…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision, Language, Multimodal
- Task
- Visual question answering, Language modeling/generation, Question answering
- Base model
- MiniMax-Text-01
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.
- Training data
- tokens
"MiniMax-VL-01 undergoes additional training with 512 billion vision-language tokens"
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 2.1 × 10²⁴ FLOP
- How it was established
- Operation counting
- Fine-tuning compute
- 1.4 × 10²³ FLOP
1.98288e+24 FLOP [base model compute] + 1.410048e+23 FLOP [addtional vision-language finetune compute] = 2.1238848e+24 FLOP
Assuming same amount of activated parameters (45.9 * 10^9) as for the base model: 6 FLOP / parameter / token * 45.9 * 10^9 activated parameters * 512 * 10^9 tokens = 1.410048e+23 FLOP
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA H800 SXM5
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
- Open — downloadable
- Model access
- Open weights (restricted use)
- Training code
- Unreleased
- Hugging Face
- MiniMaxAI
https://huggingface.co/MiniMaxAI/MiniMax-VL-01 "MiniMax may terminate this Agreement if you are in breach of any term or condition of this Agreement." code seems to be just inference code: https://github.com/MiniMax-AI/MiniMax-01
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
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- MiniMax-01: Scaling Foundation Models with Lightning Attention
- Last updated
- 28 November 2025
What the numbers mean
Background
MiniMax-VL-01 was published by MiniMax, in China, in January 2025. It comes out of industry.
It works in Vision, Language, Multimodal, and is recorded as doing visual question answering, Language modeling/generation, Question answering.
Its starting point was MiniMax-Text-01 — most models at this scale are adapted from an existing base rather than built from nothing.
The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold. It is published under the MiniMaxAI organisation on Hugging Face.
How it was trained
Training it took roughly 2.1 × 10²⁴ FLOP of computation, on NVIDIA H800 SXM5 — a measure of what producing the model cost, not of how fast it answers.
Answers
MiniMax-VL-01 — common questions
What GPU do I need to run MiniMax-VL-01?
We cannot say. MiniMax-VL-01 has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
Is MiniMax-VL-01 open source?
Its weights are published, so MiniMax-VL-01 can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
How many parameters does MiniMax-VL-01 have?
No parameter count has been published for MiniMax-VL-01, which is why no memory or speed figure appears on this page.
Who created MiniMax-VL-01?
MiniMax-VL-01 was published by MiniMax, based in China, categorised as industry.
When was MiniMax-VL-01 released?
MiniMax-VL-01 was published in January 2025.
What is MiniMax-VL-01 used for?
MiniMax-VL-01 works in Vision, Language, Multimodal, and is recorded as handling visual question answering, Language modeling/generation, Question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.
Where can I download MiniMax-VL-01?
Its weights are published under the MiniMaxAI organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.
How much compute was used to train MiniMax-VL-01?
Around 2.1 × 10²⁴ FLOP, on NVIDIA H800 SXM5. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
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.