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January 10, 2024

Google Gemini API vs. Open AI API: Main Differences


Artur Haponik

CEO & Co-Founder

Reading time:

11 minutes

The field of generative AI has seen significant strides since the development of the Eliza chatbot in the 1960s. Back then, it was seen as the epitome of AI development, although all it could do was mimic the conversational pattern of its users. [1]

Flash forward a few decades and OpenAI released the Chat GPT model. Besides possessing unmatched natural language understanding, the model can perform a wide variety of tasks including text generation, summarization, question answering, and much more.

However, OpenAI didn’t just stop at Chat GPT – they developed an API that incorporates various other generative models including DALL.E, Whisper, GPT 3.5, GPT 4, and many more; all engineered to perform specific tasks. Google, one of the leading companies in tech, has also recently come forward with its own API – Google Gemini. Like OpenAI API, the Gemini API offers incredible capabilities, with some developers taunting that it may rival the OpenAI API.

This guide will pit the Gemini AI API against its more popular OpenAI rival, evaluating every aspect of their operation including how they perform on various tasks.


Google Gemini overview

Google started off in the generative AI field with its release of Google Bard. Unfortunately, the model wasn’t quite well received after it gave a wrong answer, despite being taunted as the next generation in generative AI [2]

In just a few months, Google, with hopes of a comeback after a rather disappointing ordeal, released the Gemini AI model. Since its release, Gemini has been making waves in the AI community with its advanced capabilities.

What makes it stand out is the multimodal capabilities that enable it to understand text, images, and videos, giving it unmatched versatility, especially when it comes to handling complex tasks, especially in physics, mathematics, and other technical fields.

Like the OpenAI API, Google Gemini has several models suited to specific applications. These models include the:

  • Ultra Model: The Gemini Ultra model is taunted to be the most advanced of the bunch due to its full multimodal capabilities. The model is engineered for scalability and performance, giving it vast potential in real-life applications.
  • Google Gemini Pro Model: Google Gemini Pro Model was the first release in the Gemini series. It’s already incorporated into Google Bard, giving it significant advantages over OpenAI’s GPT 3.5, which doesn’t have up-to-date information.
  • Nano Versions: As the name suggests, the nano versions of this model are smaller versions that are optimized for on-device applications.

Unfortunately, besides the Gemini AI Ultra Model, which is already available to developers and other tech-based enterprises, the other models are still on the shelf but slated for release in the coming months.

Read more about Google Gemini AI

What makes Google Gemini AI stand out?

Like all other generative models, the suitability of the Gemini AI in various use cases comes down to its capabilities. In that regard, Google didn’t hold back when it came to developing the model. Here’s what makes it unique.

Multimodal training

Unlike OpenAI API which focuses primarily on natural language processing and understanding to generate text-based responses (with the exception of DALL.E, which can generate images), Google Gemini doesn’t just focus on text-based applications. It is trained in various modalities, enabling it to handle text, image, and video applications.


The performance, accuracy, and reliability of any generative model come down to the size and versatility of its dataset. Google didn’t disappoint in that regard. The Gemini model has a massive dataset encompassing everything from web documents, code, books, and other scripts.

What’s even more impressive is Gemini’s advantages over similar models don’t just end at its massive dataset. The Nano models in development, for instance, have huge parameters. The Nano-1 version has 1.8 billion parameters while the Nano-2 version has 3.2 billion parameters. [3]


Gemini’s architecture is built on decoder-only transformers, which give it incredible creative abilities. Unlike encoder-only transformers, which focus primarily on understanding context, decoder-only transformers can take it up a notch by accurately predicting the next token in a sequence, thereby facilitating coherent and contextually sound outputs.

The architecture is also enhanced for optimized inference and large-scale training, enabling seamless scalability and customization.

Context length

The Gemini models have a context length of more than 32,000 tokens. This gives them impressive multi-query attention, which comes in handy in extended, task-intensive applications.

How to use Google Gemini AI API

Shortly after the release announcement, Google provided free API access for its complete Gemini models. This means that you can access the Gemini API key for free, without first having to set up cloud billing. This offer is extended to the text-only and text-and-vision models, including Google Bard integrations.

Here’s how to access and use the Gemini API:

  1. Open the Gemini API website and create an account
  2. Your account comes with a free API key that you need to keep private
  3. Install the Gemini client library for your preferred programming language
  4. Import the Gemini API client library into your code and execute it using your Gemini API key
  5. Through the code, you can ask the Gemini API to perform various functions including text generation, question answering, and language generation
  6. The same principle applies to the text-and-image model, which can ask questions related to your image.
  7. You can also use the Gemini API key in chat format. Company recently released a code on GitHub that enables you to chat with the Gemini Pro model using an API key, right on the terminal window. [4] What’s great about the code is you don’t have to change the question in the code and run it again like with the text-only and text-and-image models.

Comparison of Google Gemini vs. ChatGPT

Gemini has emerged as a strong contender for OpenAI’s ChatGPT model. However, the true test for generative AI accuracy, reliability, and efficiency doesn’t lie in controlled benchmark tests but in real-life applications. In that regard, here’s a detailed get-4 vs. Gemini comparison.

GPT-4 vs. Gemini: Overview

The main difference between Google’s Gemini and OpenAI’s ChatGPT is that the latter excels in text-only applications like creative writing, informative dialogue, text translation, and question answering. Google’s Gemini, on the other hand, has multimodal capabilities, which means it can seamlessly handle text, audio, and video-related tasks.

Google’s Gemini has also outperformed ChatGPT on academic test benchmarks. For instance, Gemini scored 90% on tasks related to physics, math, and law, while ChatGPT scored 86.4%, which, despite the meager margin, is quite impressive. [5]

GPT-4 vs. Gemini: Comparison

Here’s a more detailed comparison of various instances Google Gemini vs. ChatGPT:


Besides academic-related benchmarks, Google’s Gemini outperforms ChatGPT on various other benchmarks including general reasoning in MMLU (90% to 86.4%), Big-Bench Hard (83.6% to 83.1%), DROP (82.4% to 80.3%), GSM8K (94.4% to 90.0%), MATH (53.2% to 52.9%), HumanEval on Python code generation (74.4% to 67.0%), and Natural2Code (74.9% to 73.9%).

google gemini vs gpt4 capabilities

That said, ChatGPT outperforms Gemini AI in the HellaSwag test that tests common sense reasoning in everyday tasks with a 95.3% to 87.8% score. [6]


Openai’s ChatGPT is an unimodal model. This means that it focuses solely on text-based applications. However, its specialization in handling Natural Language Processing (NLP) tasks makes it quite versatile in text-based applications.

Google’s Gemini, on the other hand, has multimodal capabilities. It integrates both text and image processing capabilities, facilitating more dynamic interactions and a broader range of applications.


ChatGPT is limited to its training data. For instance, the GTP 3.5 model can only answer questions on events that occurred before 2021, when it was trained. Conversely, Gemini can retrieve real-time content, enabling it to transcend the limitations of its training data, and facilitating more imaginative and innovative responses.


ChatGT is pretty fast. It can provide fast and relatively accurate responses. The responses are also coherent and contextually relevant. Similarly, Gemini, although most of its models are still under development, is taunted to provide faster and more accurate responses than its counterpart. This may significantly improve user experience.


ChatGPT leverages deep learning techniques for text processing. This makes it incredibly effective in natural language tasks, including text generation, translation, and question answering.

Gemini, on the other hand, utilizes AlphaGo-inspired techniques for problem-solving. [7] This allows for more advanced planning and reasoning in complex tasks, making it more versatile than ChatGPT.


ChatGPT has been around for quite some time now. In that time, it has undergone several iterations, with each iteration providing enhanced capabilities over the former version.

As part of the DeepMind project, Google’s Gemini is still under development. The only model available for public use is the Google Gemini Pro model, which promises advanced capabilities both in terms of performance and accuracy in various applications.


ChatGPT offers a text-based dialogue-like UI that makes it pretty easy to interact with. You don’t need any technical expertise to interact with the model, making it incredibly user-friendly.

Gemini’s Pro model, on the other hand, focuses primarily on code-based interactions, offering more interactive capabilities. It can also integrate both visual and textual responses, making it suitable for a wide variety of applications.

Data Handling

Both models can handle data pretty well. They can process user input and generate pretty accurate responses. The major difference emanates from the cut-off date of their respective datasets. For instance, ChatGPT only has data up to a certain date in 2021, significantly limiting the information it can deliver.

Google’s Gemini, on the other hand, is trained on real-time data. This facilitates up-to-date insights and responses, making it more suitable for tasks that require knowledge of current events.


ChatGPT offers some level of customization, particularly in how it provides responses based on user input. Similarly, Google Gemini offers more advanced customization options. Its code-based interaction mechanism facilitates broader learning and data integration capabilities, making it more versatile.

Safety and Alignment

OpenAI has incorporated numerous safety safeguards into its ChatGPT model. The GPT 3.4 and GPT-4 models have various safety enhancements that reduce the likelihood of responding to disallowed content requests. The company also constantly refines the model based on user feedback and real-world applications to make it safer for all users and reduce bias.

Likewise, Gemini undergoes rigorous safety evaluations. These evaluations are focused primarily on bias and toxicity analysis and are carried out through collaboration with external experts to help mitigate potential risks effectively.


The GPT 3.5 model and all its predecessors are multimodal, which means they can only process text-based inputs. However, OpenAI introduced visual comprehension to its GPT-4 model, allowing it to process visual information and generate an accurate response based on the input.

Google’s Gemini takes this up a notch by integrating various data types including code, text, images, audio, and video.

Read more about Multimodal AI Models: Understanding Their Complexity

Reasoning Abilities

Both the GPT-4 and Google Gemini AI have multimodal reasoning capabilities, although the latter is more adept. For instance, Gemini can extract information from both written and visual data, giving it more context when performing complex tasks. Similarly, OpenAI’s GPT-4 model significantly outperforms the GPT-3.5 model due to its ability to process both visual and textual inputs.


Google and OpenAI take different approaches when it comes to pricing strategies for their generative models. For instance, Google offers a character-based pricing model, while OpenAI utilizes a token-based approach.

These pricing models can be both advantageous and disadvantageous for certain language speakers. For example, OpenAI’s token-based model favors English speakers while leaving other languages that require significantly more tokens to analyze at a disadvantage.

Wrapping up

Google and OpenAI are the leading companies in generative AI. Their respective models demonstrate a significant stride in generative AI, particularly around multimodality. Similarly, their APIs give you access to various models, each suited to a wide variety of tasks, making them suitable for numerous real-world applications.

Taking this GPT-4 vs. Gemini comparison into consideration, it’s clear to see how similar the two APIs are. They can perform relatively similar tasks, albeit with a few differences, especially when it comes to performance and pricing strategies.

When choosing a preferable model between Gemini and GPT-4, it is important to not only consider their respective pricing models but also your specific requirements and tasks at hand. It’s also important to consider that GPT-4 focuses primarily on alignment, safety, and creative problem-solving, while its counterpart, Gemini, focuses on performance and multimodality.


[1] Eliza. URL: Accessed on January 8, 2024
[2] Google AI chatbot Bard sends shares plummeting after it gives the wrong answer. URL: Accessed on January 8, 2024
[3] Google Gemini AI. URL: Accessed on January 9, 2024
[4] Gemini Testing. URL:, Accessed on January 9, 2024
[5] Storage. Gemini Report. URL: , Accessed on January 9, 2024
[6] Deepmind. Google, Gemini Capabilities. URL: , Accessed on January 9, 2024
[7] How AI Thinks and Learns. URL:, Accessed on January 9, 2024


Artificial Intelligence