The video discusses Google’s release of the Gemini 2.5 Flash model, highlighting its affordable pricing and unique features like adjustable reasoning capabilities, which allow users to set limits on the model’s thinking before responding. While the model is cheaper than competitors, it generally lags behind in performance metrics, making its affordability the primary reason for consideration.
In the video, the presenter discusses Google’s recent release of the Gemini 2.5 Flash model, which is currently available in preview mode through the API and AI Studio. The AI Studio offers the model for free, while the API provides a limited number of free tokens for initial use. The Gemini 2.5 models are designed to enhance reasoning capabilities, allowing users to set limits on how much the model can “think” before responding. This feature distinguishes it from other models, such as ChatGPT, where users must switch models to access different functionalities.
The presenter clarifies the naming convention of the Gemini models, explaining that the 2.5 Flash version is a mini experimental model that has now transitioned to preview status. The Gemini 2.5 models are touted as Google’s response to the latest releases from competitors like ChatGPT. The ability to toggle the thinking mode on or off and set a thinking budget is highlighted as a significant advantage, allowing for more tailored interactions based on user needs.
In terms of pricing, the presenter compares Gemini 2.5 to other major models on the market, noting that it is significantly cheaper. Input tokens cost 15 cents per million, while output tokens are 60 cents without thinking and 350 cents with thinking. This pricing structure is presented as a competitive edge for Google, although the presenter expresses skepticism about the sustainability of such low prices, questioning whether they reflect actual production costs or are merely a strategic move to attract users.
Benchmark comparisons reveal that while Gemini 2.5 performs well in terms of pricing, it generally lags behind competitors like OpenAI in various metrics, including performance on exams and coding tasks. The presenter notes that although Gemini 2.5 is an improvement over its predecessor, it still falls short of the capabilities demonstrated by other leading models. The video emphasizes that the primary reason to consider Gemini 2.5 is its affordability rather than its performance.
Towards the end of the video, the presenter demonstrates the model’s capabilities by creating a simple 3D simulator game using prompts in AI Studio. While the results are impressive, the presenter expresses some disappointment with the speed of the model’s responses. The video concludes with an invitation for viewers to join the presenter’s community for further discussions and insights on AI tools, while also encouraging feedback on the Gemini 2.5 model and its place in the competitive landscape of AI technologies.