Choosing the Right Gemini AI Model for You: From Flash to Pro
Originally published on Premier Cloud Blog
Google has released multiple Gemini models that have revolutionised the landscape of AI-powered tools, making generative AI more accessible and powerful for individuals, developers, and enterprises alike. Developed by DeepMind, Google’s Gemini models represent the cutting edge in generative AI, offering advanced capabilities that range from natural language processing (NLP) to vision and multimodal AI solutions.
With multiple models tailored to specific use cases, Gemini is designed to meet the needs of individuals, small businesses, and large enterprises, providing AI-driven tools that enhance productivity and innovation. Whether you’re looking for a fast, lightweight model for simple tasks or a more advanced model capable of tackling complex projects, Gemini has something for everyone.
In this article, we will explore the various Gemini models, their use cases, and performance characteristics to help you determine the best fit for your needs. We will take a look at pricing options for each of these models to help you choose a model not just for its performance but also on price.
Overview of Gemini Models
The Gemini family provides a range of models that cater to different use cases and requirements. The list of models and all information listed in this article is per the date of this article/page and can change as newer and more powerful models and released.
Here’s a quick breakdown of the models and what they are designed for:
- Gemini 1.5 Flash (models/gemini-1.5-flash)
The Gemini 1.5 Flash model is designed for fast, low-latency AI tasks. It’s highly optimised for real-time applications that require rapid processing and output, without compromising too much on the quality of the response.
- Token Count: The model can process 1,048,576 input tokens and generate upto 8,192 tokens as output. This is useful when dealing with moderately sized tasks requiring a quick turnaround.
- Core Features: The Flash model supports key capabilities such as system instructions, JSON mode, functional calling and customisable safety settings.
- Rate Limits: Under the free plan, the model allows 15 requests per minute (RPM) and 1 million tokens per minute (TPM), while the pay-as-you-go option significantly increases this to 2,000 RPM and 4 million TPM. This means it scales well for larger, high-traffic applications.
Common Use Cases:
Gemini 1.5 Flash excels in applications that require real-time, interactive responses. It’s well-suited for:
- Customer Service Automation: Providing quick, pre-trained responses to common customer queries, ensuring minimal delay.
- Chatbots: Engaging users in fast-paced conversations without lag.
- Real-Time Analysis: Processing data and delivering immediate feedback, such as in dashboard applications where speed is crucial.
2. Gemini 1.5 Flash-8B (models/gemini-1.5-flash-8b)
The Gemini 1.5 Flash-8B is a variant of the Flash model but significantly more powerful, designed to handle more complex and resource intensive tasks. Its optimised with 8 billion parameters, enhancing its ability to process larger data sets while still maintaining a low-latency experience.
- Token Count: Similar to the 1.5 Flash, the 1.5 Flash-8B supports up to 1,048,576 input tokens and 8,192 output tokens, but with improved processing for more intricate tasks.
- Core Features: It retains the same features as the Flash model, including function calling, JSON mode, and tunable safety settings, but with the added power of more parameters to support higher-order reasoning tasks.
- Rate Limits: The model maintains the same rate limits as the standard Flash model ensuring scalability for high-demand environments.
Common Use Cases:
The 1.5 Flash-8B variant strikes a balance between speed and complexity. It’s best used for:
- Advanced Chatbots: Providing richer interactions by considering more complex user inputs and delivering personalised responses.
- Multimodal interactions: Ideal for applications that handle both text and multimedia data such as voice assistants or image recognition tasks with text outputs.
- Interactive Applications: Great for real-time user interfaces that need fast but contextually deeper responses, such as digital personal assistants.
3. Gemini 1.5 Pro (models/gemini-1.5-pro)
The Gemini 1.5 Pro is designed for enterprise-level applications that require both high accuracy and extensive input/output handling. This model is optimised for processing large-scale data and generating detailed, high-quality outputs.
- Token Count: The model can process up to 32,768 tokens, making it ideal for tasks that require a deeper understanding of long-context interactions or large inputs.
- Core Features: Beyond the standard features seen in the Flash models, Gemini 1.5 Pro places a heavier emphasis on contextual depth and accuracy, making it perfect for generating nuanced responses in mission-critical applications.
- Rate Limits: Under the free plan, the model allows 15 requests per minute (RPM) and 1 million tokens per minute (TPM) and 1500 requests per day (RPD) , while the pay-as-you-go option gives you 2,000 RPM and 4 million TPM.
Common Use Cases:
The 1.5 Pro is ideal for businesses that require high performance and contextual accuracy. It’s best suited for:
- Enterprise AI Applications: Powering large-scale applications such as knowledge management systems, legal analysis tools, and customer platforms that require high levels of accuracy and detail.
- Healthcare: Analysing large medical datasets and producing detailed reports or patient summaries.
- Financial Modelling: Offering insights into market trends, analysing large datasets for economic forecasting, or generating detailed financial reports.
Evaluating Gemini Model Performance
In this section, we will take a look at how each of the Gemini models — 1.5 Flash, 1.5 Flash-8B and 1.5 Pro — performs within Google AI Studio.
What is Google AI Studio ?
Google AI Studio is a browser-based IDE for prototyping with generative models. Google AI Studio lets you quickly try out models and experiment with different prompts.
When you’ve built something you’re happy with, you can export it to code in your preferred programming language and and integrate it in your application.
Testing Environment and Metrics
To provide a comprehensive comparison, we’ll consider the following aspects:
- Response Time: Speed of generating output after receiving input.
- Accuracy: How well the generated content matches the prompt’s intent. (subjective)
- Token Utilisation: How efficiently the model uses input tokens and handles context.
- Multimodal Integration: Performance in tasks requiring both text and audio/image inputs.
Once you launch Google AI Studio, a new prompt will open up and you should see something like this.
On the left side of the screen you can see your current library of prompts, create a new fine tuned model, view the prompt gallery for prompt inspirations and event get your own Gemini API key.
On the right side of the screen you can select the model you want to use, view the token count and use some Advanced settings too.
Execute your first prompt on AI Studio
Before we begin comparing the models, lets go ahead and execute our first prompt on Google AI Studio. For this prompt, I am going to be using the Gemini 1.5 Flash model.
You can change the model by clicking on Model, that will take you to a variety of models provided by Google AI Studio.
Once you are done selecting your model and adjusting the settings, go ahead and enter a new prompt in the dialogue box given below. For this example, I’m going to be generating a small poem about a blue bird.
Generate a poem about a blue bird sitting on a Banyan tree.
Once you are done designing your prompt, go ahead and click on Run.
Here you can see, on successful execution, it has generated a poem about a blue bird. Now if you wanted the code for this you can click on the Get code button on the top that will give you the code in multiple programming languages.
You can choose the language you are comfortable with and either copy the code or even open it in Google Colab.
Now that we have executed our first prompt on Google AI Studio, we’ll take a look at how these models perform. To compare models, you can click on the Compare button given above which will open up a window next to the previous prompt.
On clicking compare, two chat windows will open up with an option to choose a model for each. For this run, I am going to be testing the Gemini 1.5 Flash and 1.5 Pro model.
On successful execution, both the models have generated a response for the given prompt. Lets evaluate the responses of the models.
- Response Time: Gemini 1.5 Flash took 0.9s to generate a response, whereas Gemini 1.5 Pro took 6.2s.
- Accuracy: As mentioned before, this metric is subjective and there is a difference visible between both of the responses, both correct.
- Token Utilisation: The Gemini 1.5 Flash model utilised 51 tokens.
Whereas, the Gemini 1.5 Pro model utilised 305 tokens. A thing to note would be that the Gemini 1.5 Pro model has a token count of 2M.
To evaluate Multimodal Integration we are going to pass the same image to both the models and asking them to describe the image. I am going to be passing an image of a busy India market to test the models capabilities.
You can compare both the responses of the models.
Gemini 1.5 Flash response
A bustling street market in India is captured in this photograph. The scene is packed with people, vendors, and goods, creating a chaotic and colourful atmosphere.
The street itself is narrow and lined with shops and stalls on both sides. Overhead, a tangled web of electrical wires crisscrosses the sky, adding to the sense of urban sprawl. The buildings along the street are a mix of old and new, with some showing signs of wear and tear while others are more modern.
The crowd is diverse and dynamic. There are men, women, and children, some dressed in traditional Indian clothing while others wear more modern attire. Vendors hawk their wares from makeshift stalls, displaying a variety of goods including fruits, vegetables, clothing, and household items.
In the foreground, a collection of auto-rickshaws, known locally as “tuk tuks,” are parked and loaded with passengers. These colorful vehicles are a ubiquitous sight in Indian cities and are a popular mode of transportation.
The photograph captures the energy and vibrancy of this bustling street market, showcasing the daily life of people in a rapidly developing urban environment. The composition is balanced, drawing the viewer’s eye towards the center of the scene, while the use of color and light enhances the visual impact.
Gemini 1.5 Pro Response
The image captures the bustling chaos of a crowded street market in India, likely in Old Delhi, known for its vibrant atmosphere and dense urban fabric.
The scene is a sensory overload, with people, vehicles, and buildings crammed into the frame. The most striking element is the chaotic web of electrical wires crisscrossing above the street, forming a dense, almost impenetrable canopy. These wires speak to the ad-hoc nature of infrastructure in some parts of the city.
The street itself is a whirlwind of activity. People throng the narrow passageway, a mix of pedestrians, vendors, and those navigating the crowd on foot or rickshaws. The multitude of brightly colored clothes worn by the people adds to the vibrancy of the scene. Interspersed among the people are numerous auto-rickshaws, their bright yellow and green paint contrasting with the more muted tones of the buildings. These rickshaws, a common mode of transport in India, add to the sense of movement and congestion.
Adding to the visual cacophony are the buildings lining the street. They are a jumble of mismatched architectural styles, their facades plastered with a multitude of signs in various languages. The buildings are in varying states of repair, with some showcasing balconies and intricate details while others appear worn and faded. The signs, a mix of hand-painted advertisements and more modern billboards, compete for attention, advertising everything from guest houses to local businesses.
The image offers a glimpse into the raw, unfiltered energy of daily life in a densely populated Indian city. It’s a scene of vibrant chaos, highlighting the challenges and beauty of urban life in India.
In terms of tokens, Gemini 1.5 Flash utilised 526 tokens, whereas Gemini 1.5 Pro utilised 601 tokens.
Conclusion
Each Gemini model provides distinct advantages depending on the needs of your application. The Gemini 1.5 Flash is perfect for real-time, fast-response applications, while the 1.5 Flash-8B offers a balance of speed and complexity for moderately demanding tasks. For enterprises looking to deploy large-scale AI applications with high accuracy, Gemini 1.5 Pro is the optimal solution.
By selecting the right model variant for your project, you can leverage the power of Gemini to enhance your workflows, improve user interactions, and deliver intelligent solutions at scale.
Feel free to reach out if you have any issues/feedback at aryanirani123@gmail.com.
