Google Gemini is a set of large language models (LLMs) that leverage training techniques from AlphaGo, such as tree search and reinforcement learning. It’s intended to become Google’s “flagship AI,” powering many products and services within the Google portfolio.
Gemini models have been evaluated on a wide variety of tasks. From natural image, audio and video understanding to mathematical reasoning, Gemini Ultra’s performance exceeds current state-of-the-art results on 30 of the 32 widely-used academic benchmarks used in large language model (LLM) research and development.
Unlike other models in the emerging LLM Arms Race, Google Gemini was built to be multimodal from the ground up. It can seamlessly generalize, understand, and combine different data types, such as text, code, audio, video, and images.
Gemini has 3 sizes.
Gemini Ultra — our largest and most capable model for highly complex tasks.
Gemini Pro — our best model for scaling across a wide range of tasks.
Gemini Nano — our most efficient model for on-device tasks
Google Gemini Nano
Gemini Nano is the “lite” pared-down model of the LLM, available in two sizes: Nano-1 (1.8 billion parameters) and Nano-2 (3.25 billion parameters).This version of Gemini is designed to run on mobile devices and will soon preview in Google’s AI Core app via Android 14 on the Pixel 8 Pro app .Nano will power various features previewed by Google during the Pixel 8 Pro unveiling in October, such as summarization within the Record app and suggested replies for messaging apps.
Google Gemini Pro
Google Gemini Pro runs on Google’s data centers and powers things like Google Bard, the chatbot similar to Microsoft's Co Pilot solution. It will soon roll out into other Google tools, such as Duet AI, Google Chrome, Google Ads, and the Google Generative Search experience.According to Google, Gemini Pro is more effective at tasks like brainstorming, writing, and summarizing content – outperforming OpenAI GPT-3.5 in six core benchmarks.
Google Gemini Ultra
Gemini Ultra, still unavailable for widespread use at this point, is the most capable model in the collection. Like Pro, it’s trained to be natively multimodal and was pre-trained and fine-tuned on various codebases.
Gemini Ultra can comprehend nuanced information in text, code, and audio and answer questions related to complicated topics. Ultra exceeds current state-of-the-art results on around 30 of the 32 widely-used benchmarks used for LLM Deveopment.
Exploring the Capabilities of Gemini and GPT
As demand for generative AI solutions and LLM models grows, Google has plenty of competition in the current market.
However, many tech enthusiasts are only interested in answering one question: “Is it better than GPT-4?” GPT-4, OpenAI’s multimodal large language model, is pretty much the benchmark all developers are using to assess the potential of new LLMs.
Google has made comparing the performance of Gemini and GPT-4 pretty simple, with a simple graph you can find here.
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