Google Officially Launches Flagship AI Model Gemini 2.0 Pro Experimental and Gemini 2.0 Flash Thinking Model to Strengthen Its Competitiveness in the AI Field.
In response to the low-cost and high-efficiency AI model competition brought about by Chinese AI startup DeepSeek, Google is trying to improve its market share by integrating the Gemini 2.0 Flash Thinking model into its Gemini application.
Gemini 2.0 Pro, as the flagship model of the Gemini series, performs well in encoding and processing complex prompts, and has stronger world knowledge understanding and reasoning capabilities. Its 2 million tokens context window allows it to handle a large amount of textual content.
Google Introduces Gemini 2.0 Series
In response to the low-cost and high-efficiency trend sparked by DeepSeek, Google officially launched the flagship AI model Gemini 2.0 Pro Experimental on Wednesday, along with the Gemini 2.0 Flash Thinking model. This is seen as an important move by Google to actively respond to competition in the AI field and consolidate its market position.
Gemini 2.0 Pro: Upgraded Encoding Capability and Expanded Context Window
Gemini 2.0 Pro is the successor to Gemini 1.5 Pro, which was launched by Google in February last year. Google claims that it is now the flagship model in the Gemini AI model series. The model excels in encoding and processing complex prompts and has “better world knowledge understanding and reasoning capabilities” than any previous models.
According to Tech Church, Gemini 2.0 Pro can even invoke Google Search and execute code on behalf of users.
It is worth mentioning that the context window of Gemini 2.0 Pro has reached 2 million tokens, which means it can process approximately 1.5 million words (English vocabulary) in a single prompt. This capacity is enough for it to read all seven books in the “Harry Potter” series in a single prompt, with about 400,000 words of space remaining.
Gemini 2.0 series models have been officially launched.
Image/Google
Facing DeepSeek! Gemini 2.0 Flash Thinking Joins the Battle
Both Google and DeepSeek released AI inference models in December last year, but DeepSeek’s R1 received more attention. DeepSeek’s model is comparable to and even surpasses the leading AI models offered by American tech companies in terms of performance, and companies can use these models at a relatively low cost through their API.
In order to deal with the competition from DeepSeek, Google is trying to make more people aware of the Gemini 2.0 Flash Thinking model through the Gemini application, hoping to maintain its leading position in the fiercely competitive AI market with the launch of Gemini 2.0 Pro and Gemini 2.0 Flash Thinking.
Comparison of Gemini 2.0 Series Models
Gemini 2.0 Flash
The main model in the Gemini series, suitable for daily tasks. Compared to 1.5 Flash, it has significantly improved quality. Compared to 1.5 Pro, it has lower latency while slightly improving quality, making it closer to real-time response.
Key features:
It has a multimodal real-time API that supports low-latency bidirectional voice and video interactions. In most quality benchmark tests, it outperforms Gemini 1.5 Pro. It has improved in multimodal understanding, encoding, complex instruction following, and function calling, supporting a better user experience. It also adds built-in image generation and controllable text-to-speech capabilities, enabling image editing, localized artwork creation, and expressive storytelling.
Application scenarios:
Suitable for daily applications that require quick response and high-quality output, such as real-time translation and video recognition.
Gemini 2.0 Flash-Lite
It is the fastest and most cost-effective version of the Flash model, suitable for scenarios that require both speed and cost considerations.
Key features:
With the same price and speed, it has better quality than 1.5 Flash. It has multimodal input and text output capabilities, equipped with a 1M tokens input context window and an 8k tokens output context window. However, it does not include the multimodal output generation, multimodal real-time API integration, thinking mode, and built-in tool usage features of Gemini 2.0 Flash.
Application scenarios:
Suitable for large-scale text output applications, such as generating titles for a large number of photos.
Gemini 2.0 Pro
The model in the Gemini series with the strongest encoding capability and world knowledge, with a 2M long context window, suitable for scenarios that require processing a large amount of information and complex encoding tasks.
Key features:
It performs well in encoding and processing complex prompts, and has stronger understanding and reasoning capabilities for world knowledge. It has a super large context window of 2 million tokens, which enables comprehensive analysis and understanding of a large amount of information. It has tool invocation capabilities, such as Google Search.
Application scenarios:
Suitable for scenarios that require powerful encoding capability and handling complex problems, such as converting Python code to Java code. Researchers can also use Gemini 2.0 Pro to quickly read and understand a large amount of academic literature and automatically generate literature reviews, saving a lot of time and effort.
For more information: In addition to DeepSeek, there are countless other potential AI competitors. A look at the top 5 Chinese AI companies
Source: TechChurch, Google
This article was initially written by an AI and edited by Li Xiantai.