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The generative AI race has hit hyperspeed. If we just look at how fast things are moving, even DeepSeek-RI coming out feels like ancient history now.
Speaking specifically about Google, the world’s go-to search engine has lagged far behind in this AI race, up until this point. But as of February 5, they appear to be back in the race with Gemini 2.0 - rumored to be incredibly powerful and incredibly cheap.
In this blog, we will briefly discuss the key details of Gemini 2.0 models, including recent updates on Flash, Flash-Lite, and Pro Experimental. We will also share some of the aspects in which these models stand out from the competition.
Google’s Gemini series of AI models didn’t have the smoothest start when Gemini 1.0 was launched in December 2023. There were some pretty noticeable issues, especially with how the model handled image generation, which led to some embarrassing mistakes.
A research paper was also published on arXiv.org that showed that Google Gemini at most tasks performed worse than GPT-3.5, the OpenAI’s free and less cutting-edge older model.
These early problems made it clear that Gemini, the long-rumored ChatGPT rival, still had a lot of room for improvement.
However, over the course of 2024, Gemini has steadily gotten better. Google has worked hard to fix the issues and make the models much more reliable. So, they went on to roll out two improved versions of Gemini, called Gemini Advanced and Gemini 1.5.
Now, with Gemini 2.0, their second-generation effort, the company seems determined to take things to a whole new level.
Google’s Gemini 2.0 is a portfolio of AI large language models (LLMs). Previous Gemini releases debuted with the base model. That base model was then followed by a lighter, optimized “Flash” variant. But unlike all the previous releases, the first preview of Google’s Gemini 2.0 came directly with the Gemini 2.0 ‘Flash’ variant.
In December 2024, Google rolled out Gemini 2.0 Flash Thinking and updated this in AI Studio last month (January 2025). It’s an experimental model that exhibits the speed of 2.0 Flash while offering enhanced reasoning capabilities.
Now, on 5th Feb 2025, Google has announced the following major updates to the Gemini 2.0 family:
Google does certain things differently than a lot of the other AI companies. They first release experimental versions of their models. So, Gemini 2.0 Flash, which was also launched as an experimental model, has now become generally accessible and is currently the default Gemini app model.
It’s the latest of all generally accessible models of the Gemini family. Google calls it their “workhorse” model which is highly popular among the developer community. The model is designed to offer low-latency responses and is capable of handling high-efficiency, high-volume tasks. Plus, it supports multimodal reasoning at large scales.
One of the key benefits Gemini 2.0 Flash has over its competition is its context window. Context window is simply the number of tokens you can type in as a prompt and get back in a single back-and-forth exchange with an LLM-based AI chatbot or API.
And the context window of the 2.0 Flash model is one million. If we compare this context window with other leading models, Gemini 2.0 Flash turns out to be on top here. Other models, including OpenAI’s latest o3-mini, only handle 200,000 tokens or fewer (which is roughly the length of a 400 to 500-page novel).
With 2.0 Flash offering support for 1 million tokens, now you as a user have an AI solution that helps you process large amounts of information and complete high-efficiency tasks at scale.
Gemini 2.0 Flash-Lite has come to lower costs like never before. It’s a brand-new AI model developed to be highly cost-effective without losing quality.
According to Google DeepMind, Flash-Lite beats its full-size predecessor, Gemini 1.5 Flash, on third-party benchmarks like Bird SQL programming (57.4% vs. 45.6%) and MMLU Pro (77.6% vs. 67.3%) while keeping the same price and same speed.
Just like the Gemini 2.0 Flash model, Flash-Lite also has a one million-token context window and accepts multimodal inputs (i.e., text, images, video, audio).
This model is currently in the public preview which means it’s publicly available but in a reduced capacity. Google recommends not using the public preview features in production code since the functionality and support level can change without any prior notice for such features.
Gemini 2.0 Flash-Lite costs $0.075 per million tokens (input) and $0.30 per million tokens (output), as shown in the table mentioned below. It’s positioned as a cost-effective option for developers and performs better than Gemini 1.5 Flash on most tests while maintaining the same price.
Gemini 2.0 Flash-Lite offers the best deal when compared to other top models like Anthropic Claude ($0.8/$4 per million in/out), OpenAI 4o-mini ($0.15/$0.6 per million tokens in/out), and DeepSeek’s traditional LLM V3 ($0.14/$0.28).
2.0 Pro has arrived in experimental availability. A successor to the Gemini 1.5 Pro model, Gemini 2.0 Experimental is built to offer “better accuracy” and “improved performance” for coding and mathematics-related prompts.
Google says it’s their strongest model yet. It features the largest context window of 2 million tokens and offers enhanced understanding and better reasoning of world knowledge compared to any other model they have released so far.
Just like other Gemini 2.0 models, Gemini 2.0 Pro has illustrated improved capabilities over 1.5 models across various performance benchmarks. The following image also shows Gemini 2.0 Pro outperforming Flash and Flash-Lite in multilingual understanding, long-context processing, reasoning, etc.
Gemini 2.0 Flash delivers speed with super-long context windows. Flash-Lite offers the same performance as 2.0 Flash but at a much more affordable price. Talking about 2.0 Pro (experimental), it comes with the largest context windows and is the strongest of all Gemini models so far, especially when it comes to complex tasks like coding and math.
With all these Gemini 2.0 models, Google leans into its multimodal strengths. If we look at tools like OpenAI’s latest o3-mini and even DeepSeek-R1, none of them accept multimodal inputs (such as images, file uploads, etc.). On the other hand, every model in the Gemini 2.0 series comes with multimodal capabilities.
All in all, we can say that Google’s second-generation line-up feels like a comprehensive upgrade to its predecessors. And if we compare them with other models in the market, they position Google as a serious contender in the generative AI space now.
This is Naveed Anjum, a skilled content writer with almost 7 years of experience. Currently part of Arbisoft's growth team, he focuses on creating content that moves the needle and supports growth objectives of the company. When not writing, he's exploring new ideas to improve his skills and keep things fresh.
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