Gemini 1.5 Pro
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
- Google DeepMind
- Organisation type
- Industry
- Country
- United States of America
- Published
- 15 February 2024
- Authors
- Gemini Team
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language, Multimodal
- Task
- Language modeling, Visual 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.
- How it was established
- Benchmarks
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- Google TPU v4
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 access: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models
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
- Significant use
- Record confidence
- Speculative
Google DeepMind's current best public model, being used for their products.
Sources
Where this record came from and when it was last checked.
- Reference
- Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
- Last updated
- 7 April 2026
What the numbers mean
About this model
Gemini 1.5 Pro was published by Google DeepMind, in United States of America, in February 2024. The organisation is categorised as industry.
It works in Language, Multimodal, and is recorded as doing language modeling, Visual 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
The reason it appears in this catalogue at all is significant use.
Answers
Gemini 1.5 Pro — common questions
Is Gemini 1.5 Pro open source?
No. Gemini 1.5 Pro has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Gemini 1.5 Pro have?
No parameter count has been published for Gemini 1.5 Pro, which is why no memory or speed figure appears on this page.
Who created Gemini 1.5 Pro?
Gemini 1.5 Pro was published by Google DeepMind, based in United States of America, categorised as industry.
When was Gemini 1.5 Pro released?
Gemini 1.5 Pro was published in February 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 Gemini 1.5 Pro used for?
Gemini 1.5 Pro works in Language, Multimodal, and is recorded as handling language modeling, Visual question answering. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Gemini 1.5 Pro?
None. Gemini 1.5 Pro 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.