Gemini: A Family of Highly Capable Multimodal Models
Gemini: A Family of Highly Capable Multimodal Models is one of 15 landmark papers in TheLLMWiki's research index — explained here in plain language, without the jargon.
What this paper showed
Presented Google's natively multimodal model family, trained jointly on text, image, audio and video from the start rather than stitching together separate encoders for each modality after the fact.
For the full technical detail — architecture diagrams, training data, ablations and exact benchmark numbers — the original paper is the authoritative source; this page exists to give you the plain-language version before you decide whether to read further.
The lasting impact of Gemini: A Family of Highly Capable Multimodal Models
Native multimodality, rather than bolting a vision encoder onto a text model after the fact, is now the default approach for new frontier models, in large part because Gemini demonstrated it at scale first.
Papers earn a place in this index specifically because their core idea is still visible in production systems today, not just because they were influential when published. If you're trying to understand why a current model or technique works the way it does, tracing it back to a paper like this one is usually more useful than reading a summary of the model's release notes alone.
Gemini: A Family of Highly Capable Multimodal Models, answered
Who published Gemini: A Family of Highly Capable Multimodal Models?
This paper came out of Google.
Why does this paper matter today?
Native multimodality, rather than bolting a vision encoder onto a text model after the fact, is now the default approach for new frontier models, in large part because Gemini demonstrated it at scale first.
Where can I read the full paper?
Search the paper title on arXiv or Google Scholar for the original PDF and any follow-up work that has cited it.
Do I need to read the full paper to understand the idea?
Not necessarily — the plain-language summary above covers the core contribution. The full paper matters most if you're implementing the technique yourself or need the exact experimental details.
What should I read next?
See the related papers above, or the AI research hub for more landmark work organized by topic.
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