Google just released three new AI models, and each one matters for a different reason. Let’s start with what they made.
Gemini 2.0 Flash is the main model, ready for real production use. It handles text, images, multilingual audio, and has built-in tool usage. Most importantly, it’s twice as fast as Gemini 1.5 Pro while scoring better on benchmarks.
Then there’s Gemini 2.0 Flash-Lite Preview, a smaller model built for tasks that need high volume and quick responses. Google hasn’t shared much detail about this one yet.
Finally, there’s Gemini 2.0 Pro Experimental – their strongest Gemini model so far. It excels at complex reasoning but remains in testing. However, it has some limitations: it can’t reliably hit specified word counts, typically maxing out around 600 words. It also occasionally includes random Bengali words, suggesting temperature settings might be too high.
The interesting part? Google added quota tiering to their API, so developers can scale their usage based on what they need. They also added file input/output with code execution across all these models.
Gemini 2.0 Flash stands out because it can handle pretty much any type of input – images, video, audio – and output them too. It generates images mixed with text and can even create multilingual speech that you can control.
Google is putting these models to work in some interesting ways. They’re testing AI agents through projects like Astra, Mariner, and Jules (an AI coding assistant). While that’s cool, what matters more is that developers can now build serious applications with these models.
If you’re interested in the technical side of AI models and their real-world impact, check out my analysis of OpenAI vs Google’s research approaches here: https://adam.holter.com/openai-vs-google-deep-research-which-one-actually-makes-sense/
At the end of the day, Google is making AI more accessible and practical. These models aren’t just experiments – they’re tools ready for real work.
