o1
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
- OpenAI
- Organisation type
- Industry
- Country
- United States of America
- Published
- 5 December 2024
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language, Mathematics, Multimodal
- Task
- Code generation, Language modeling/generation, Quantitative reasoning, Chat, Question answering, Translation, Mathematical reasoning
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
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
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,Significant use
- Record confidence
- Unknown
SOTA in GPQA among others: https://openai.com/index/learning-to-reason-with-llms/ "we recruited experts with PhDs to answer GPQA-diamond questions. We found that o1 surpassed the performance of those human experts, becoming the first model to do so on this benchmark." "With its vision perception capabilities enabled, o1 scored 78.2% on MMMU, making it the first model to be competitive with human experts."
Sources
Where this record came from and when it was last checked.
- Reference
- Introducing ChatGPT Pro: Broadening usage of frontier AI.
- Last updated
- 12 February 2026
What the numbers mean
Where it came from
o1 was published by OpenAI, in United States of America, in December 2024. The organisation is categorised as industry.
It works in Language, Mathematics, Multimodal, and is recorded as doing code generation, Language modeling/generation, Quantitative reasoning, Chat, Question answering, Translation, Mathematical reasoning.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
The reason it appears in this catalogue at all is sOTA improvement,Significant use.
Answers
o1 — common questions
Is o1 open source?
No. o1 has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does o1 have?
No parameter count has been published for o1, which is why no memory or speed figure appears on this page.
Who created o1?
o1 was published by OpenAI, based in United States of America, categorised as industry.
When was o1 released?
o1 was published in December 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.
What is o1 used for?
o1 works in Language, Mathematics, Multimodal, and is recorded as handling code generation, Language modeling/generation, Quantitative reasoning, Chat, Question answering, Translation, Mathematical reasoning. 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.
What GPU do I need to run o1?
None. o1 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.