GLM-4 (0116)
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
- Z.ai (Zhipu AI)
- Organisation type
- Industry
- Country
- China
- Published
- 17 January 2024
- Authors
- Aohan Zeng, Bin Xu, Bowen Wang, Chenhui Zhang, Da Yin, Diego Rojas, Guanyu Feng, Hanlin Zhao, Hanyu Lai, Hao Yu, Hongning Wang, Jiadai Sun, Jiajie Zhang, Jiale Cheng, Jiayi Gui, Jie Tang, Jing Zhang, Juanzi Li, Lei Zhao, Lindong Wu, Lucen Zhong, Mingdao Liu, Minlie Huang, Peng Zhang, Qinkai Zheng, Rui Lu, Shuaiqi Duan, Shudan Zhang, Shulin Cao, Shuxun Yang, Weng Lam Tam, Wenyi Zhao, Xiao Liu, Xiao…
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, Code generation, Quantitative reasoning, Translation
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
- 10,000,000,000,000 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.
- How it was established
- Operation counting
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Data centre
- The paper does not mention any hardware, GPUs or any information regarding the hardware used.
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
GLM-4 (0116) has been made available through the GLM-4 API at https://bigmodel.cn
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Frontier model
- Yes
- Likely above 10²³ FLOP
- Yes
- Why it is tracked
- Training cost
- Record confidence
- Likely
Trained on 10T tokens with similar architecture to GPT-4, probably >$1M compute cost.
Sources
Where this record came from and when it was last checked.
- Reference
- ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools
- Last updated
- 11 February 2026
What the numbers mean
Where it came from
GLM-4 (0116) was published by Z.ai (Zhipu AI), in the country recorded as China, during January 2024. 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, Code generation, Quantitative reasoning, Translation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
It was trained on a corpus of about 10,000,000,000,000 tokens of text.
The reason it appears in this catalogue at all: training cost.
Answers
GLM-4 (0116) — common questions
GLM-4 (0116)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
GLM-4 (0116)— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
GLM-4 (0116)— who created it?
It was published by Z.ai (Zhipu AI), based in China, an organisation categorised as industry.
GLM-4 (0116)— when was it released?
It 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.
GLM-4 (0116)— 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, Code generation, Quantitative reasoning, Translation. 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.
GLM-4 (0116)— 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.