Just eight months after its debut, Google’s Gemini chatbot has quietly been reshaped into a markedly more capable system. The service, which took over from the Bard brand in February 2024, now runs on a suite of large‑language models that can handle text, code, images, audio and video all at the same time – a capability Google describes as “native multimodality.” Most generative‑AI products are built around a single data type. They either read text or analyse pictures, and any cross‑modal work requires a conversion step.
Gemini’s architecture, however, was designed from the start to process multiple modalities in a single reasoning pass. The model does not turn an image into text before interpreting it; it treats the visual input as an image and the textual input as text simultaneously.
This design choice expands the range of tasks the chatbot can perform. With the release of the 1.5 and 3 model generations, Google also enlarged the context windows, allowing the system to retain far more information in a single prompt. Users can now upload an entire codebase and ask Gemini to locate bugs, or drop a two‑hour lecture video into the chat and request a concise summary.
The model keeps all of that data in its working memory, making such extensive analyses feasible. Google offers Gemini in three distinct tiers.
The smallest, called Nano, runs locally on a device and does not require an internet connection. The mid‑range Pro tier covers everyday tasks, while the top‑level Ultra tier is aimed at complex reasoning scenarios. This tiered approach lets customers select the version that matches their needs, spanning from on‑device processing to data‑center‑grade performance – a breadth that is uncommon among AI providers.
The shift from Bard to Gemini was more than a rebranding exercise. Bard, launched as Google’s answer to ChatGPT, was limited to text.
When the Gemini name was introduced in February 2024, the underlying models were rebuilt to support multimodal input, and the Bard label was retired. The current Gemini product is therefore a new offering rather than an incremental upgrade of its predecessor. Since its first public announcement in December 2023, Gemini has been continuously updated. Each new model iteration has extended the context window and improved the system’s ability to manage real‑world complexity.
Integration with Google’s broader ecosystem further strengthens Gemini’s appeal: the same model powers the chatbot in browsers, on smartphones and through workplace accounts, leveraging the company’s massive user base. Competitors in the AI space are still piecing together separate models for text, image, audio and video.
Gemini’s single‑system approach, combined with its large context windows and tiered deployment options, positions it as a more unified solution. While the full potential of native multimodality remains to be explored, the architecture and infrastructure are already in place, covering use cases from personal assistance to enterprise‑level analysis. As Google continues to expand Gemini’s capabilities, the industry will be watching to see whether the integration of all data types into one model can deliver the practical advantages that Google claims, and how it will influence the broader AI landscape.
