Home Google Gambit vs. Open AI Chat GPT: What’s the Difference?

Google Gambit vs. Open AI Chat GPT: What’s the Difference?

Recent years have seen a remarkable advancement in natural language processing (NLP). It’s due to the creation of complex language models like Google Gambit and Open AI Chat GPT. These language models have made it possible to create conversational agents and chatbots that can comprehend and react to human language. it has expanded the field of human-computer interaction.

Google Gambit

A language model called Google Gambit was created to play text-based games and respond to queries about the rules of the games. It can comprehend natural language inquiries regarding game mechanics. It was explicitly trained on a sizable dataset of text-based games. Google Gambit can play games for consumers and explain game rules using natural language. This is to its conversational agent capabilities.

Open AI Chat GPT

Open AI Chat GPT, on the other hand, is a general-purpose language model that was created by Open AI. It was trained using a vast dataset of human language, comprising texts from books, journals, and other sources. Open AI Chat can coherently address a wide range of topics and inquiries. Google Gambit is built for general language understanding and natural language processing.

Businesses and developers who want to include natural language processing in their goods and services must comprehend the distinctive characteristics of these language models. We will examine the key differences between Google Gambit and Open AI Chat GPT and the applications in this area.

Google Gambit VS Open AI Chat GPT

Training Data

Google Gambit is trained on a large dataset of text-based games, including games. The dataset consists of game logs, walkthroughs, and other game-related content that allows the model to understand game mechanics. It also provides explanations to users. The training data is specific to text-based games and is curated to provide the model with a deep understanding of game mechanics and terminology.

While Open AI Chat GPT is trained on a massive dataset of human language, it includes books, articles, and other sources of human language. The training data is much broader in scope and covers a wide range of topics and styles of language. The dataset used to train Open AI Chat GPT is much larger than the training data used for Google Gambit and allows the model to understand the nuances of language, such as grammar, syntax, and word usage.


Google Gambit is designed specifically to play text-based games and answer questions about game mechanics. It has been trained on a large dataset of text-based games and can understand natural language queries related to game mechanics. Google Gambit can provide users with explanations, hints, and tips as they play the game. It can also play the game itself, making it a valuable tool for game app developers to test and improve their games.

At the same time, Open AI Chat GPT is a general-purpose language model that can perform a wide range of natural language processing tasks. It can generate coherent responses to various questions and topics, such as answering factual questions, providing product recommendations, and even generating creative writing. It can also perform language translation, summarization, and sentiment analysis.

In terms of conversational abilities, both models are designed to generate responses that sound human-like. However, Open AI Chat GPT is generally more conversational than Google Gambit, as it is designed for general language understanding and can respond to a broader range of questions and topics.

Learning techniques

Google Gambit uses a reinforcement learning approach. This involves training the model to maximize a reward signal based on its performance in the game. The model is trained to play the game, and as it makes moves and receives feedback, it adjusts its strategy to improve its performance. This approach allows the model to learn from its mistakes and gradually improve its gameplay over time.

Open AI Chat GPT, on the other hand, uses a deep learning approach known as a transformer network. The transformer network is trained on a massive dataset of human language and can generate coherent responses. The model uses unsupervised learning, which means it does not rely on explicit feedback signals to adjust its behavior. Instead, it learns from the patterns and structures of the training data to generate coherent responses.

Ownership and accessibility

Google Gambit is owned and maintained by Google and is accessible through Google’s platform. This means that users can only access Google Gambit through Google’s game-playing platform and do not have direct access.

Open AI Chat GPT, on the other hand, is an open-source language model that anyone can access and use. This means that users have direct access to the model’s code and data. Moreover, Open AI Chat GPT has been trained on a big dataset of human language, which contains a wide variety of themes and settings. This makes it a versatile and flexible language model.


Google Gambit is designed to run on Google’s platform. This interface offers a simpler user experience, and its design is special for playing text-based games. However, this also means that users are limited to using the model within the context of Google’s platform. Open AI Chat GPT, on the other hand, is more flexible in terms of deployment. The model can be deployed on a variety of platforms, including cloud services, on-premises servers, and mobile devices. This flexibility makes Open AI Chat GPT a versatile language model that can be integrated into a variety of applications.


Google Gambit is a proprietary technology developed by Google, so Google controls its development. This means that users do not have access to the underlying code. They also can’t contribute to the development of the model.

While Open AI Chat GPT is an open-source project, which means that the underlying code is publicly available, thus, users can contribute to its development. This has led to a community of developers and researchers working together to improve the model and build new applications.

Additionally, Open AI has a mission to advance artificial intelligence safely and beneficially, and their work on Open AI Chat GPT is a part of that mission. They have made efforts to ensure that the model is transparent, explainable, and ethical, and have released resources and guidelines to help users build AIs. Google Gambit has been specifically designed for playing text-based games such as trivia, puzzle games, and escape room games. Its primary application is within the entertainment industry, where it can provide an engaging and interactive experience for users.


Both Google Gambit and Open AI Chat GPT have a wide range of applications in the field of natural language processing. But there are some differences in their primary use cases. Open AI Chat GPT, on the other hand, is a more general-purpose language model that can be used for a wide range of applications. This includes chatbots, customer service, content generation, language translation, and more. Its versatility makes it popular for businesses and organizations looking to incorporate natural language processing into their products or services.

Additionally, Open AI has released several variations of the Chat GPT model, including GPT-2 and GPT-3, which have progressively larger and more powerful neural networks. These models have demonstrated impressive capabilities in generating human-like language and have been used for a wide range of applications. This includes creative writing, chatbots, and language translation. Overall, while both Google Gambit and Open AI Chat GPT are powerful language models, their primary use cases differ.

Model size

Model size refers to the number of parameters or ” weights ” used to train a neural network. The larger the number of parameters, the more complex the model, and the better it is at processing and understanding natural language. There is a significant difference in model size in the case of Google Gambit and Open AI Chat GPT. Google Gambit has around 2.6 billion parameters, which is relatively small compared to the largest version of Open AI Chat GPT, GPT-3, which has over 175 billion parameters.

The larger number of parameters in GPT-3 allows it to perform more complex language processing tasks, such as generating coherent and natural-sounding text and understanding the nuances of language better. However, it also requires more computational resources to train and run, making it less accessible for some developers and organizations. On the other hand, Google Gambit has a smaller model size, making it less resource-intensive to train and run. This makes it a good choice for applications that require real-time processing, such as games.

Training time and resources

Google Gambit requires significant training time and resources due to the complexity of text-based games. Open AI Chat GPT, on the other hand, can be trained relatively quickly and efficiently due to its use of unsupervised learning and the availability of large-scale pre-trained models.


In conclusion, model size is a significant factor in determining the capabilities and performance of a language model. Overall, both models have their own unique strengths and limitations. The choice between them will depend on the specific requirements of the application in question.

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The ReadWrite Editorial policy involves closely monitoring the tech industry for major developments, new product launches, AI breakthroughs, video game releases and other newsworthy events. Editors assign relevant stories to staff writers or freelance contributors with expertise in each particular topic area. Before publication, articles go through a rigorous round of editing for accuracy, clarity, and to ensure adherence to ReadWrite's style guidelines.

Deanna Ritchie
Former Editor

Deanna was an editor at ReadWrite until early 2024. Previously she worked as the Editor in Chief for Startup Grind, Editor in Chief for Calendar, editor at Entrepreneur media, and has over 20+ years of experience in content management and content development.

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