Gemini 2.5 Deep Think
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,Google DeepMind
- Organisation type
- Industry,Industry
- Country
- United States of America
- Published
- 1 August 2025
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language, Multimodal, Vision, Video, Audio, Mathematics
- Task
- Language modeling/generation, Mathematical reasoning, Code generation, Visual question answering, Question answering, Visual puzzles, Video description, Speech recognition (ASR), Speech-to-text
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
- Hosted access (no API)
- 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
- SOTA improvement
- Record confidence
- Unknown
see p4 of the model card https://storage.googleapis.com/deepmind-media/Model-Cards/Gemini-2-5-Deep-Think-Model-Card.pdf
Sources
Where this record came from and when it was last checked.
- Reference
- Gemini 2.5 Deep Think - Model Card
- Last updated
- 28 November 2025
What the numbers mean
About this model
Gemini 2.5 Deep Think was published by Google,Google DeepMind, in United States of America, in August 2025. The organisation is categorised as industry,Industry.
It works in Language, Multimodal, Vision, Video, Audio, Mathematics, and is recorded as doing language modeling/generation, Mathematical reasoning, Code generation, Visual question answering, Question answering, Visual puzzles, Video description, Speech recognition (ASR), Speech-to-text.
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.
Answers
Gemini 2.5 Deep Think — common questions
What is Gemini 2.5 Deep Think used for?
Gemini 2.5 Deep Think works in Language, Multimodal, Vision, Video, Audio, Mathematics, and is recorded as handling language modeling/generation, Mathematical reasoning, Code generation, Visual question answering, Question answering, Visual puzzles, Video description, Speech recognition (ASR), Speech-to-text. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run Gemini 2.5 Deep Think?
None. Gemini 2.5 Deep Think 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 Gemini 2.5 Deep Think open source?
No. Gemini 2.5 Deep Think has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Gemini 2.5 Deep Think have?
No parameter count has been published for Gemini 2.5 Deep Think, which is why no memory or speed figure appears on this page.
Who created Gemini 2.5 Deep Think?
Gemini 2.5 Deep Think was published by Google,Google DeepMind, based in United States of America, categorised as industry,Industry.
When was Gemini 2.5 Deep Think released?
Gemini 2.5 Deep Think was published in August 2025.
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.