Editing Openai/69408af5-1bf8-800f-b5dc-86cd01e07ec0 (section)

=== Assistant: Yes — some variants of Google’s models especially Gemma, not Gemini were reported to be trained on around 11 trillion t… ===
Yes — some variants of Google’s models (especially ''Gemma'', not ''Gemini'') were reported to be trained on around ~11 trillion tokens, but this is specific to those Gemma models, not necessarily the flagship Gemini family itself. Google AI for Developers<ref>{{cite web|title=Google AI for Developers|url=https://ai.google.dev/gemma/docs/gemma-3n/model_card|publisher=Google AI for Developers|access-date=2025-12-16}}</ref>

Clarification:

1) ''Gemma'' (Google’s open-weight models)
* The Gemma 3n models were trained on ~11 trillion tokens. Google AI for Developers<ref>{{cite web|title=Google AI for Developers|url=https://ai.google.dev/gemma/docs/gemma-3n/model_card|publisher=Google AI for Developers|access-date=2025-12-16}}</ref> This is where you likely saw the “~11 trillion tokens” figure.

2) ''Gemini'' (Google DeepMind’s flagship LLM family)
* Gemini itself does not have a widely published official token count like Gemma does. Independent claims vary widely (e.g., internet articles suggesting enormous figures from tens to dozens of trillions), but no authoritative Google research paper or official documentation has confirmed a specific ~11T or ~65T token training dataset for the flagship Gemini models. Wikipedia<ref>{{cite web|title=Wikipedia|url=https://en.wikipedia.org/wiki/Gemini_%28language_model%29|publisher=en.wikipedia.org|access-date=2025-12-16}}</ref>

3) Why the confusion arises
* Models within the Gemma line are distinct from the Gemini family despite similar names — and Gemma’s 11T token training dataset is sometimes mistakenly attributed to Gemini. Google AI for Developers<ref>{{cite web|title=Google AI for Developers|url=https://ai.google.dev/gemma/docs/gemma-3n/model_card|publisher=Google AI for Developers|access-date=2025-12-16}}</ref>

Summary:
* Gemma 3n was trained on ~11T tokens. Google AI for Developers<ref>{{cite web|title=Google AI for Developers|url=https://ai.google.dev/gemma/docs/gemma-3n/model_card|publisher=Google AI for Developers|access-date=2025-12-16}}</ref>
* Gemini flagship models do not have an officially confirmed published training token count, and internet reports vary widely and are not reliably sourced. Wikipedia<ref>{{cite web|title=Wikipedia|url=https://en.wikipedia.org/wiki/Gemini_%28language_model%29|publisher=en.wikipedia.org|access-date=2025-12-16}}</ref>

If you need a comparison of the largest officially documented training dataset sizes across main LLM families, let me know and I can lay that out.