Hunyuan-TurboS
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
- Tencent
- Organisation type
- Industry
- Country
- China
- Published
- 11 March 2025
- Authors
- Tencent Hunyuan Team: Ao Liu, Botong Zhou, Can Xu, Chayse Zhou, ChenChen Zhang, Chengcheng Xu, Chenhao Wang, Decheng Wu, Dengpeng Wu, Dian Jiao, Dong Du, Dong Wang, Feng Zhang, Fengzong Lian, Guanghui Xu, Guanwei Zhang, Hai Wang, Haipeng Luo, Han Hu, Huilin Xu, Jiajia Wu, Jianchen Zhu, Jianfeng Yan, Jiaqi Zhu, Jihong Zhang, Jinbao Xue, Jun Xia, Junqiang Zheng, Kai Liu, Kai Zhang, Kai Zheng, Kejiao…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Quantitative reasoning, Question answering, Code generation, Text summarization
- Approach
- Supervised fine-tuning (SFT),Reinforcement learning
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
- 560B
- Training data
- 16,000,000,000,000 tokens
the model scales to 56B activated parameters and 560B total parameters
pre-trained on 16T high-quality 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
- 5.4 × 10²⁴ FLOP
using C = 6*ND, 6* 56 billion parameters * 16 trillion tokens = ~5.4e24 FLOP
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
API via Tencent Cloud
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
Based on the photos published by Tencent on X, it achieved top performance on several benchmarks - MMLU, C-Eval, MATH. (https://x.com/TencentHunyuan/status/1899105803073958010/photo/1)
Sources
Where this record came from and when it was last checked.
- Reference
- Tencent HunYuan Turbo S: The fastest reasoning LLM At par with DeepSeek, Claude 3.5 and GPT-4o
- Last updated
- 11 February 2026
What the numbers mean
About this model
Hunyuan-TurboS was published by Tencent, in the country recorded as China, during March 2025. The publishing organisation is categorised as industry.
It works in the domain of Language, and is recorded as performing the task of language modeling/generation, Quantitative reasoning, Question answering, Code generation, Text summarization.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Training it took a computation budget of roughly 5.4 × 10²⁴ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Training consumed a corpus of around 16,000,000,000,000 tokens of text.
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
Hunyuan-TurboS — common questions
Hunyuan-TurboS— how much compute was used to train it?
Training consumed around 5.4 × 10²⁴ 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.
Hunyuan-TurboS— 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.
Hunyuan-TurboS— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Hunyuan-TurboS— how many parameters does it have?
It has a parameter count of 560B. the model scales to 56B activated parameters and 560B total 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.
Hunyuan-TurboS— who created it?
It was published by Tencent, based in China, an organisation categorised as industry.
Hunyuan-TurboS— when was it released?
It was published in March 2025.
Hunyuan-TurboS— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling/generation, Quantitative reasoning, Question answering, Code generation, Text summarization. These are the areas it was designed around; they describe intent rather than a hard boundary.
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.