Step-1
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
- StepFun
- Organisation type
- Industry
- Country
- China
- Published
- 18 February 2025
- Authors
- Ailin Huang, Boyong Wu, Bruce Wang, Chao Yan, Chen Hu, Chengli Feng, Fei Tian, Feiyu Shen, Jingbei Li, Mingrui Chen, Peng Liu, Ruihang Miao, Wang You, Xi Chen, Xuerui Yang, Yechang Huang, Yuxiang Zhang, Zheng Gong, Zixin Zhang, Hongyu Zhou, Jianjian Sun, Brian Li, Chengting Feng, Changyi Wan, Hanpeng Hu, Jianchang Wu, Jiangjie Zhen, Ranchen Ming, Song Yuan, Xuelin Zhang, Yu Zhou, Bingxin Li, Buyun…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, 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
- 130B
- Training data
- 800,000,000,000 tokens
130B
. The text data, amounting to 800 billion tokens, encompasses web documents, books, code, and proprietary materials.
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
- 6.2 × 10²³ FLOP
- How it was established
- Operation counting
6 FLOP / token / parameter * 130 * 10^9 parameters * 800 * 10^9 tokens = 6.24e+23 FLOP [1 epoch assumed]
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
- Step-Audio: Unified Understanding and Generation in Intelligent Speech Interaction
- Last updated
- 28 November 2025
What the numbers mean
About this model
Step-1 was published by StepFun, in the country recorded as China, during February 2025. The category the publisher falls under is industry.
It works in the domain of Language, and is recorded as performing the task of language modeling/generation, Question answering.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
Producing it required arithmetic totalling around 6.2 × 10²³ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 800,000,000,000 tokens of text.
Answers
Step-1 — common questions
Step-1— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Step-1— how many parameters does it have?
It has a parameter count of 130B. 130B. 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.
Step-1— who created it?
It was published by StepFun, based in China, an organisation categorised as industry.
Step-1— when was it released?
It was published in February 2025.
Step-1— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling/generation, 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.
Step-1— how much compute was used to train it?
Training consumed around 6.2 × 10²³ FLOP. 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.
Step-1— 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.