Agentar-Fin-R1 32B
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
- Ant Group
- Organisation type
- Industry
- Country
- China
- Published
- 27 July 2025
- Authors
- Yanjun Zheng, Xiyang Du, Longfei Liao, Xiaoke Zhao, Zhaowen Zhou, Jingze Song, Bo Zhang, Jiawei Liu, Xiang Qi, Zhe Li, Zhiqiang Zhang, Wei Wang, Peng Zhang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Financial management, Language modeling/generation, Question answering
- Base model
- Qwen3-32B
- Numerical format
- BF16
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
- 32B
- Training data
- tokens
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 A100
- Chips used
- 64
- Power draw
- 50.1 kW
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
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Agentar-Fin-R1: Enhancing Financial Intelligence through Domain Expertise, Training Efficiency, and Advanced Reasoning
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Agentar-Fin-R1 32B was published by Ant Group, in the country recorded as China, during July 2025. It comes out of an organisation categorised as industry.
It works in the domain of Language, and is recorded as performing the task of financial management, Language modeling/generation, Question answering.
It builds on Qwen3-32B. That is the usual way a specialised model is produced.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
Agentar-Fin-R1 32B — common questions
Agentar-Fin-R1 32B— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Agentar-Fin-R1 32B— how many parameters does it have?
It has a parameter count of 32B. 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.
Agentar-Fin-R1 32B— who created it?
It was published by Ant Group, based in China, an organisation categorised as industry.
Agentar-Fin-R1 32B— when was it released?
It was published in July 2025.
Agentar-Fin-R1 32B— what is it used for?
It works in the domain of Language, and is recorded as handling the task of financial management, Language modeling/generation, Question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.
Agentar-Fin-R1 32B— what GPU do I need to run it?
None. This 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.