Each is optimized for specific tasks, with Gemini Ultra designed for highly complex tasks, Gemini Pro for a wide range of tasks, and Gemini Nano for efficient on-device tasks.
Screenshot from Google, December 2023
Google Gemini Performance: Text Benchmarks
The model’s performance is exceptional, surpassing human experts in Massive Multitask Language Understanding (MMLU) with a score of 90.0%.
Additionally, Gemini Ultra outperforms existing models in 30 of the 32 widely used academic benchmarks in large language model research.
Screenshot from DeepMind, December 2023
Google Gemini Multimodal Capabilities And Performance
Gemini’s innovative approach to multimodality sets it apart from previous models.
Traditional multimodal models are often limited by their design, which involves training separate components for different modalities and then stitching them together.
In contrast, Gemini was built from the ground up to be natively multimodal, enabling it to understand and reason across various inputs far more effectively.
Screenshot from DeepMind, December 2023
This capability positions Gemini as a powerful tool in fields ranging from science to finance, where it can uncover insights from vast amounts of data and provide advanced reasoning in complex subjects like math and physics.
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Examples from the Google DeepMind report on Google Gemin showcase Gemini’s multimodal capabilities, such as image generation.
Screenshot from Google, December 2023
In this video, Google tests Gemini with its Emoji Kitchen.
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It also can handle text, image, and audio, as shown below.
Screenshot from Google, December 2023
This video from Google offers more insight into Gemini’s ability to process raw audio.
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Gemini Benchmarks Against External Competitors
How does Google Gemini stack up to the top AI models from OpenAI, Inflection, Anthropic, Meta, and xAI? The following shows Gemini Ultra and Pro performance on text benchmarks against its competition.
Screenshot from Google, December 2023
Gemini Excels At Coding
In addition to its multimodal capabilities, Gemini excels in coding tasks. Its ability to understand, explain, and generate high-quality code in multiple programming languages positions it as a leading model for coding.
Screenshot from Google, December 2023
It also forms the basis for more advanced coding systems, like AlphaCode 2, significantly improving competitive programming problems.
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The model’s efficiency and scalability are bolstered by Google’s in-house designed Tensor Processing Units (TPUs) v4 and v5e, making it the most reliable and scalable model to train and serve.
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