View all AI news articles

Google Gemini: A Detailed Insight into the Latest AI Marvel

February 27, 2024


Google has recently introduced Gemini, an advanced generative AI platform developed by its AI research labs, DeepMind and Google Research. This platform encompasses three distinct models: Gemini Ultra, Gemini Pro, and Gemini Nano, each catering to different needs and computational capabilities​​. Google’s CEO Sundar Pichai and Demis Hassabis, CEO of Google DeepMind, have emphasized Gemini's potential to significantly impact scientific discovery, human progress, and everyday lives​​.

Gemini Ultra: The Flagship Model

Gemini Ultra, the foundation model of the Gemini family, is designed for highly complex tasks. However, its full capabilities have yet to be publicly released. Preliminary demonstrations suggest it can assist in academic tasks like physics homework, identifying relevant scientific papers, and even updating charts with recent data. Unlike other AI models that use intermediary steps for image generation, Gemini Ultra is capable of natively producing images​​.

Gemini Pro: The Publicly Available Model

Gemini Pro, currently available to the public, showcases enhanced reasoning and understanding capabilities. Although it excels in certain areas, it struggles with complex mathematical problems and factual accuracy. It is available through Bard, Google’s text-based search chatbot, and can be accessed via an API in Vertex AI, Google’s AI developer platform​​.

Gemini Nano: For On-Device Tasks

Gemini Nano is optimized for mobile devices like the Pixel 8 Pro. It powers features such as Summarize in the Recorder app and Smart Reply in Gboard, providing users with AI capabilities directly on their phones without the need for a server connection​​.

Performance and Comparison to GPT-4

Google claims that Gemini exceeds the performance of OpenAI’s GPT-4 in 30 out of 32 standard performance measures. However, the differences are marginal, and it's unclear how much of an improvement Gemini represents over existing models like GPT-4. Experts suggest that while Gemini is sophisticated, its capabilities might not be substantially greater than those of GPT-4, particularly in handling images and video​​​​​​.

Cost and Accessibility

Currently, Gemini Pro is free to use in Bard and on AI Studio and Vertex AI, with future pricing plans announced for Vertex AI. The cost is based on the amount of text processed and images generated​​.

User Reviews and Industry Perspectives

The introduction of Gemini has been met with mixed reactions. While some applaud its capabilities, others point out its limitations in factual accuracy, translation, and coding suggestions. There is also skepticism about the real-world applicability of the incremental improvements it offers over competing models like GPT-4​​​​​​​​.

Challenges and Future Prospects

Despite its advanced capabilities, Gemini faces challenges common to large language models, such as biases, security vulnerabilities, and environmental concerns. Google's approach to address these issues involves tools for double-checking chatbot answers against search results​​​​.


Gemini represents Google's ambitious stride in the generative AI arena, aiming to rival OpenAI’s GPT-4. Its three models cater to various computational needs, from high-end tasks to on-device applications. While its performance is commendable, the real-world impact and advantages over existing models like GPT-4 remain a topic of ongoing debate. The incremental improvements it offers may be more about convenience and brand recognition than groundbreaking technological advancement​​​​.


  • Google Gemini is a multimodal AI platform comprising Gemini Ultra, Gemini Pro, and Gemini Nano.
  • Gemini Ultra, the most advanced model, is yet to be fully released to the public.
  • Gemini Pro, available publicly, shows enhanced reasoning but struggles with complex tasks and accuracy.
  • Gemini Nano is designed for mobile devices, offering AI capabilities directly on phones.
  • The platform claims to surpass OpenAI's GPT-4 in certain benchmarks, but the actual performance improvement is marginal.
  • Gemini faces challenges similar to other large language models, including biases and environmental impact.

Recent articles

View all articles