Gemini 2.0 – Everything You Need to Know About Google’s Latest LLMs

The race for the most advanced reasoning LLM is heating up, and the competition is fiercer than ever.  DeepSeek kicked it off with DeepSeek R1, followed by OpenAI’s o3-mini, and now Google has entered with a powerhouse lineup: Gemini 2.0 Flash, Flash Lite, Pro, and two experimental models—Flash 2.0 Thinking and Thinking with Apps.  While Flash models are already making their way into public testing, the experimental ones could redefine reasoning and app integration, challenging o3-mini and DeepSeek-R1. In this blog, we’ll dive into these new models, their unique features, and their competitive edge. Let’s dive in!

What is Gemini 2.0?

Gemini 2.0 is the umbrella of the latest multimodal models by Google. These models have been developed by Google, keeping in sight the demands of the agentic era for highly efficient workhorse models with low latency and enhanced performance. In the Gemini 2.0 series, the following models have been released so far:

1. Gemini 2.0 Flash
2. Gemini 2.0 Flash Lite
3. Gemini 2.0 Pro

Along with these powerful models, Google has also secretly released two other models which are currently in their “experimental” phase. The two models are:

4. Gemini 2.0 Flash Thinking Experimental
5. Gemini 2.0 Flash Thinking Experimental with Apps

These experimental models are by far the most exciting models by any AI company. Not only do they offer complex reasoning and logical thinking, but they also work with Google’s most used apps like YouTube, Maps, and Search.

So, let’s explore each of these latest releases by Google, one by one.

Gemini 2.0 Flash

The Flash models are designed for high-volume, high-frequency tasks, prioritizing speed and efficiency. Gemini 2.0 Flash is now openly available for everyone, making it suitable for production applications. Here are the key features of this model:

• It can handle heavy tasks and perform multimodal reasoning with a huge context window of 1 million tokens.
• It is accessible in the Gemini app and through the Gemini API in Google AI Studio and Vertex AI.
• The model is comparable to OpenAI’s GPT-4o, DeepSeek’s V3, and Qwen-2.5 with its speed and efficiency in handling tasks.

Availability: This model is currently available only to Gemini Advanced subscribers on the Gemini app, while in the Google AI Studio, it is available to all for free. So if you do not have a paid Gemini account (which comes with a free one-month trial), you can try it in Google AI Studio.

Now, let’s test it out on the Gemini app.

Prompt: “Read the article at https://www.analyticsvidhya.com/blog/2024/07/building-agentic-rag-systems-with-langgraph/ to understand the process of creating a vector database for Wikipedia data. Then, provide a concise summary of the key steps.”

Response:

Review:

The model is great at reading through the web links. It generates a clear summary and then lists down the broad steps covered in the blog. Thus, Gemini Flash 2.0 proves to be a fast and efficient model that is quick with accessing the internet for solving queries. It’s great for day-to-day content-related tasks as well as for image analysis and generation.

Gemini 2.0 Flash Lite

The Flash Lite model is designed with cost-effectiveness in mind. It builds upon its predecessor, 1.5 Flash, offering a noticeable improvement in quality while maintaining the same impressive speed and affordability. Here are some of its highlights:

• 2.0 Flash Lite is an excellent choice for developers looking for a balance between performance and budget.
• The model boasts a 1 million token context window and supports multimodal input, allowing it to handle a wide range of tasks.
• It is currently in public preview, accessible through the Gemini API in Google AI Studio and Vertex AI. This allows developers to experiment and integrate Flash Lite into their workflows.

Availability: Gemini 2.0 Flash Lite, is available for free in Google AI Studio. It is also available in Vertex AI, but not yet available in the Gemini app.

How to Access Gemini 2.0 Flash Lite

To use Gemini 2.0 Flash Lite in Google AI Studio, you will have to:

1. Sign up on the web page.
2. Once done, click on “Create prompt” in the left side panel.
3. From the right side panel, under the “Model” section, select “Gemini 2.0 Flash Lite 02-05”.

Let’s test it.

Prompt: “Explain the following image to me in simple words.”

Response:

“This image shows an AI agent interacting with the “World” and users. Here’s a breakdown:

• World: This represents the environment the AI operates in, including the internet, operating systems, API servers, hardware, and the physical world.
• AI Agent: The central box represents the AI agent, which receives input from users and interacts with the world. It is comprised of multiple parts:
  1. Users: People interacting with the AI. They provide input to the AI.
  2. LLM (Large Language Model): The AI’s brain. It receives information, processes it, and makes decisions.
  3. Tools: This part contains various tools the AI can use to interact with the world (website APIs, applications, shell, database, etc.)
  4. Memory: The AI’s memory, where it stores and retrieves information (like data in RAM, SQL databases, and VectorDBs).
  5. Action: The AI’s action-taking module, makes the AI execute the instructions generated by the LLM.
• How it works:
  1. Users input to the AI.
  2. The LLM processes this input.
  3. The LLM uses Memory and Tools to generate an Action.
  4. Tools act on the outside world, then the LLM gets feedback and this will update the Memory.
  5. This process repeats.”

Review:

The response starts with a small introduction about the image. It then describes each part of the image and then it breaks down all individual elements. Finally, it briefly explains how all components of the image work. This model works fast! It’s quick to analyze and break the image into simple explanations. For tasks that require speed, like building chatbots for customer query resolution or Q/A sessions, and interview preparation; Gemini 2.0 Flash Lite would be ideal.

Gemini 2.0 Pro

Gemini 2.0 Pro represents the pinnacle of the Gemini family regarding capability. It’s engineered for tackling the most complex tasks, particularly those involving coding. Here are some points to note about Gemini 2.0 Pro:

• This latest model has a massive 2 million token context window, enabling it to process and understand vast amounts of information.
• It has the unique ability to call tools like Google Search and execute code directly, significantly expanding its problem-solving potential.
• Currently, in the experimental phase, Gemini 2.0 Pro is being refined and tested before wider release.

Availability: This model too is available only to paid users of Gemini Advanced on the Gemini app. Meanwhile users can access it for free in the Google AI Studio and Vertex AI. So if you do not have a paid Gemini account (which offers a free one-month trial), you can try it in Google AI Studio.

Learn More: Google Gemini 2.0 Pro Experimental Better Than OpenAI o3-mini?

Let’s have a look at how this model performs.

Prompt: “Solve this puzzle and give me the table consisting of the solution.”

This puzzle has been sourced from the following website.

Response:

Placing these values on the website:

Review:

The model explains its solution and follows it up with a solution table, as prompted. It generated the correct responses based on the information given, although in a couple of places, it did assume incorrect values of color and currency. However, its final result remains unaffected because those values did not matter in the overall response. Needless to say, Gemini 2.0 Pro does combine powerful reasoning with speed and accuracy.

Gemini 2.0 Flash Thinking Experimental

The Flash Thinking Experimental model is an exciting development that combines the speed and efficiency of Gemini 2.0 Flash with enhanced reasoning abilities. This allows it to not just process information quickly but also apply logical thinking to complex problems.

• Currently, this model is available to users of the Gemini app, offering only some of the users early access to its advanced capabilities.
• As an experimental model, it’s still under development, but it points towards the future of LLMs that can reason and solve problems more effectively.

Availability: The Gemini 2.0 Flash model is only available in the paid version – Gemini Advanced, on the Gemini app. However, in Google AI Studio, it is available for free. So if you do not have a paid Gemini account (which offers a one-month free trial), you can try it in Google AI Studio.

Now let’s see what this model can do.

Prompt: “You walk into a room and see a bed. On the bed, there are two dogs, four cats, a giraffe, five cows, and a duck. There are also three chairs and a table. How many legs are on the floor?”

Response:

“Let’s break this down:

• Things ON the bed: Dogs, cats, giraffe, cows, duck. These animals are on the bed, so their legs are not on the floor.
• Things IN the room (and likely on the floor unless stated otherwise):
  • Chairs: Chairs have legs, and they are typically on the floor. Each chair has 4 legs, and there are 3 chairs.
  • Table: Tables have legs, and they are typically on the floor. Tables usually have 4 legs, and there is 1 table.
  • You: You walked into the room. Presumably, you are standing or walking, and your legs are on the floor. You have 2 legs.

Let’s count the legs on the floor:

• Chairs: 3 chairs * 4 legs/chair = 12 legs
• Table: 1 table * 4 legs/table = 4 legs
• You: 1 person * 2 legs/person = 2 legs

Total legs on the floor: 12 + 4 + 2 = 18 legs

Therefore, there are 18 legs on the floor.“

Review:

The correct answer to this question is 22 which includes 12 legs of 3 chairs, 4 legs of the table, 4 legs of the bed, and 2 legs of the person. Gemini 2.0 Flash experimental thinking model showcases brilliant thinking, accounting for the individual legs (something that most LLMs miss). Yet, it forgot to count in the legs of the bed. Hence, even though great, the model can be better at logical reasoning.

Gemini 2.0 Flash Thinking Experimental with Apps

Building upon the Flash Thinking Experimental model, the Flash Thinking Experimental with Apps version adds a crucial layer of interactivity: the ability to interact with external applications. Here’s what makes it a powerful model:

• Its integration with external apps makes the model capable of not just reason, but also taking actions within different apps. This opens up a world of possibilities for automation of complex workflows.
• The model showcases the potential for LLMs to become deeply integrated into our digital lives, seamlessly interacting with the tools we use daily.

Availability: Gemini 2.0 Flash is only available in the paid version – Gemini Advanced. Meanwhile, in the Google AI Studio, it is available for free. So if you do not have a paid Gemini account (which comes with a free one-month trial), you can try it in Google AI Studio.

So, let’s test it out.

Prompt: “Tell me about all possible road routes that I can take from my place to New Delhi Railway station. I’m currently here: https://maps.app.goo.gl/xciqXPUgASxTXYTk8”

Response: 

“Here are the possible road routes to New Delhi Railway Station from your current location at K-3 Road, DLF Phase 2, Sector 25, Gurugram, Haryana 122022, India.

Here are the possible road routes to New Delhi Railway Station from your location at K-3 Road, DLF Phase 2, Sector 25, Gurugram, Haryana 122022, India:

Route 1: Via NH 48

• Distance: 16.4 miles
• Duration: Approximately 49 minutes
• Summary: This route primarily follows NH 48.
• Map URL

This is currently the only route provided. If you would like to explore alternative routes, you could specify criteria such as avoiding highways or tolls, and I can check for those options.”

Review:

In just a few seconds, the model thinks through the prompt, accesses the location I shared through the map, and suggests the best possible path for me. It gives me the details of the route and shares a map link for my reference too. Such a feature can be a great addition when looking for places in a new city or for locating addresses quickly. This LLM is also great for transcribing or summarizing long YouTube videos. Although it runs into errors while working with YouTube shorts.

This model combines the power of reasoning, speed, and external app accessibility – all in one. Such capabilities indicate a strong presence of agentic nature that we will soon get to see with these thinking models.

Gemini 2.0 Models: Benchmark Comparison

Now that we have worked with all of Google’s latest models, let’s quickly look at how these models perform at different benchmarks. The details for Thinking Experimental models are not yet available hence we will only be working with the rest three models: Gemini 2.0 Flash, Flash Lite, and Pro.

Source: Google DeepMind Blog

Gemini 2.0 Models: Features Comparison

Each new model has its own unique set of features. In the following table, I have listed down the features and applications of all the models that we have explored in this blog.

Conclusion

Google’s Gemini 2.0 line-up marks a big step in generative AI model capabilities, offering various models tailored for speed, efficiency, and advanced reasoning. While Gemini 2.0 Flash and Flash Lite cater to high-throughput and cost-effective use cases, Gemini 2.0 Pro looks promising for long-context understanding and tool integration. The experimental models, particularly Flash Thinking and Flash Thinking with Apps, introduce possibilities for logical reasoning and seamless app interactions.

With Gemini 2.0, Google is setting the stage for GenAI models that are more context-aware, multimodal, and deeply integrated into our digital ecosystems. As these models evolve, their impact on AI-driven workflows, content generation, and real-time decision-making will only grow.

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Anu Madan

Anu Madan has 5+ years of experience in content creation and management. Having worked as a content creator, reviewer, and manager, she has created several courses and blogs. Currently, she working on creating and strategizing the content curation and design around Generative AI and other upcoming technology.