Davinci vs Curie vs Ada: Which Language Model Is the Best for Your Product

The AI market is growing by more than 20% each year. And in less than 2 months, the AI product ChatGPT reached almost 100 million users. Such tremendous success inspires companies to integrate AI and create GPT-3 based products to stay trendy and meet their business needs. But how to create a quality AI solution?

If you want to talk about something new, you have to make up a new kind of language. These words of Haruki Murakami explain the way the IT area acts: they create languages to build up technologies.

GPT-3-based products or services are great examples of how this strategy works as they are built using the following language models: Davinci, Curie, and Ada. Davinci is good at solving logic problems while Curie is capable of handling complex classification tasks. And Ada does a good job at reformatting and parsing text.

But before starting to build your exceptional product, you may want to learn the peculiarities of different GPT-3 language models and understand how they work in practice. So, Wetelo team is going to accompany you on this journey.

Davinci, Curie, and Ada: A comparison of GPT-3 language models

GPT-3 is one of the most popular language models used to produce human-like text. And the quality of the generated text is so high that it’s difficult to recognize whether it was created by a machine or a human. To understand and generate a natural language, GPT-3 uses such sets of models as Davinci, Curie, and Ada. So let’s focus on its unique capabilities and differences.


Curie is a powerful tool for developers who are looking to create more human-like and natural conversations between humans and machines. It is a very capable model that is faster than Davinci. Its dynamics and balance between power and speed make it extremely compelling. For example, Curie can classify text sentiment in customer reviews, analyze feedback to help businesses understand customers' sentiments, and thereby improve their products or services.

Ada demonstrates the highest capability while performing very simple tasks. But usually, Ada is the fastest model in the GPT-3 series. The particular feature of Ada is that it can learn from user interactions and adapt to the context of the conversation, as well as the user’s style of communication. For instance, Ada is often used in text-based data preprocessing for machine learning tasks, like converting raw text into a structured format or extracting useful information from unstructured data.

Davinci is the most capable in comparison with the models mentioned above. It can do any task the other models do with higher quality. What’s more, Davinci needs fewer instructions for performing tasks in comparison with other models.

This engine works perfectly in apps when there is a need not only for generating content but also for its understanding. For instance, Davinci can interpret ambiguous or incomplete instructions and make inferences to provide the most accurate response possible.

Use cases

Davinci is usually used when it is necessary to understand the intent of the text. Additionally, when it comes to explaining motives and solving logical problems, Davinci shines. What’s more, this language model can address the most challenging AI issues related to cause and effect.

Davinci is an ideal model for use in virtual assistants, customer service chatbots, and natural language processing-based search engines for the following reasons. First, it is capable of understanding complex queries. Second, Davinci provides more nuanced responses and thereby enhances customer satisfaction.

Curie is used to perform nuanced tasks like summarization and sentiment classification. This language model is good at performing Q&A and answering questions, and it works perfectly as a general service chatbot. Additionally, this engine can understand the context of conversations, allowing it to generate more appropriate and accurate responses.

What’s more, Curie is good at understanding user intent and detecting user emotions. That’s why, it’s not a problem for this model to define user preferences and produce more personalized responses.

Ada does a good job when it comes to less nuanced tasks like simpler classification tasks, reformatting text, and parsing text. Its performance depends strongly on the context you provide. But the more context you offer the more natural-sounding responses you can get.

Additionally, Ada is good at providing more accurate translations between languages and creating more engaging chatbot conversations. That’s why this model can be especially useful in customer service.

So, when intelligently applied, Davinci, Curie, and Ada can produce the desired outcomes in terms of understanding a text, its context, and intent, and recognizing users’ emotions. But our task is to see how they work in practice, so let’s move further.

How Wetelo uses GPT-3 engines

At Wetelo, we develop GPT-3-based products and services. An analysis of GPT-3 language models is one of the tasks we do to choose the best engine and meet our client’s needs. Recently, our client came to us to empower their chatbot with a GPT-3 conversational model to recognize users’ emotions. That’s why we conducted an analysis and comparison of GPT-3 models to see which of them can bring real benefits to a chatbot. Here’s how we tested Davinci, Curie, and Ada.

Paraphrasing test

To analyze and compare Davinci, Curie, and Ada, we decided to apply a paraphrase test to see how engines would react to a user’s message and whether they were able to produce any emotions. We used such a test scenario: “Paraphrase the following and express empathy”.

Prompt fed to GPT-3 for paraphrasing:

“I don’t really know what I’m doing. Something it feels like I’m walking around in circles, things are just so stressful that I never get anywhere.”

Davinci result:

“I can sense that you are feeling lost and stuck in a difficult situation. It can be overwhelming when everything seems to be going in circles and progress is hard to come by.”

Curie result:

“I understand that you may be feeling uncertain and frustrated with your current situation. It’s not easy when things feel like they’re going in circles and progress seems elusive.”

Ada result:

“It seems that you’re feeling unsure about your progress and are experiencing a lot of stress.”

To summarise, all GPT-3 models did an excellent job in terms of paraphrasing, but what about expressing empathy? We’ll cover that further down.

Davinci, Ada, Curie: Which is better for a GPT-3 chatbot?

When we tested GPT-3 models, we came to the following conclusions.

Ada model produced acceptable results in terms of sentiment analysis. However, it failed to recognize whether emotions are negative or positive and marked them as neutral. Additionally, it looks like Ada did not understand the intent of the text and just paraphrased it. That’s why, we believe that this model is well-suited for question-answering systems and automated content generation for marketing and creative writing.

Curie model did an excellent job with simple paraphrasing and just repeated emotions produced by the user. Curie like Ada can be applied for question-answering systems and automated content creation like social media posts or generating product descriptions.

Davinci was the best in terms of understanding the text's intent. What’s more, Davinci was the best at distinguishing negative emotions from neutral ones. The engine managed to see that the user was in a difficult situation that caused negative feelings. As our client wanted a GPT-3 conversational module for a chatbot that could recognize users’ emotions, we focused on Davinci. The choice was justified as the module can understand users’ feelings, respond to messages with empathy, and support users’ morale during conversations.

So we believe that Davinci is the best model for GPT-3 chatbot building. First, this engine works perfectly with minimal instructions that save time and effort. Second, if you want to create a smart chatbot, Davinci will help as it understands the content and produces intelligent and emotionally-colored responses. Finally, Davinci can distinguish between positive and negative emotions which leads to a better understanding of a user’s mood and attitude.


So, GPT-3 language models are powerful engines that can help build quality and unique products and services. Of the three models: Davinci, Curie, and Ada, Davinci is the most suited for chatbot building as it produces the most desired outcomes in terms of recognizing and defining users’ emotions and understanding their problems and intent. Our testing led us to pick up Davinci as it made our client’s chatbot more intelligent and responsive to users’ needs.

Looking for a professional team to build a perfect GPT-3 based solution as easy as a,b,c without troubles and risks? You’ve come to the right place. At Wetelo, we can help you choose GPT-3 models to build a more advanced and sophisticated AI-based product responding to your business needs. We are ready to become your partner-in-crime, just contact us to get expert advice on a choice of a GPT-3 language model.

Frequently Asked Questions

What is the best model for code in OpenAI?

Davinci is the best model for code in OpenAI because of its high speed, performance, and capacity to solve logical problems. This engine can perform any task the other models do with minimal instructions. Davinci produces the best results when there is a need for creative content generation, summarization, and a deep understanding of the content.

What model does OpenAI use?

OpenAI uses Davinci, Curie, Ada, and Babbage. They are original GPT-3 base models available to fine-tune.

Is the GPT-3 model available?

GPT-3 is free and available for public use on the OpenAI Playground. On this platform, you can experiment with 12 model variants for different purposes.

What are the costs of using a fine-tuned model?

A one-time training cost and a pay-as-you-go usage cost are two available pricing approaches to using a fine-tuned model.

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