GPT-5
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
- 7 August 2025
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Multimodal, Language, Vision
- 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.
- Training data
- 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
- 6.6 × 10²⁵ FLOP
Fairly speculative, possibly around 6e25 [CI: 2e25 to 2e26] FLOP. See document below for details https://docs.google.com/document/d/1V2jIk365LnhH4WDoCw5dYJjZr1Htw8IHaK1noMf5Y48/edit?tab=t.z871imftkus
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.
- Why it is tracked
- Significant use,Historical significance
- Record confidence
- Speculative
Flagship OpenAI model starting August 2025, with presumably hundreds of millions of active users
Sources
Where this record came from and when it was last checked.
- Reference
- GPT-5 System Card
- Last updated
- 21 July 2026
What the numbers mean
Where it came from
GPT-5 was published by OpenAI, in United States of America, in August 2025. It comes out of industry.
It works in Multimodal, Language, Vision, and is recorded as doing language modeling/generation, Question answering.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
Training it took roughly 6.6 × 10²⁵ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.
Its inclusion criterion is significant use,Historical significance.
Answers
GPT-5 — common questions
How much compute was used to train GPT-5?
Around 6.6 × 10²⁵ 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.
What GPU do I need to run GPT-5?
None. GPT-5 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.
Is GPT-5 open source?
No. GPT-5 has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does GPT-5 have?
No parameter count has been published for GPT-5, which is why no memory or speed figure appears on this page.
Who created GPT-5?
GPT-5 was published by OpenAI, based in United States of America, categorised as industry.
When was GPT-5 released?
GPT-5 was published in August 2025.
What is GPT-5 used for?
GPT-5 works in Multimodal, Language, Vision, and is recorded as handling language modeling/generation, Question answering. 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.