OpenAI API Pricing in Words per Dollar
Pricing Breakdown for Your Favorite AI’s Favorite API
The Difference Between ChatGPT and GPT-3
ChatGPT is eating the world by attracting 1,000,000 users within 5 days.
ChatGPT and GPT-3 are both language models developed by OpenAI, but they have some key differences. Let’s take a closer look at these differences to help you understand how ChatGPT differs from GPT-3.
First, let’s take a look at GPT-3. GPT-3 is a state-of-the-art language model that has been trained on a massive amount of data, making it capable of understanding and generating human-like text. It can be used for a wide range of natural language processing tasks such as:
- language translation
- summarization
- text generation
GPT-3 is also available with different engines like Davinci, Curie, Babbage and Ada which are specialized in certain tasks.
ChatGPT, on the other hand, is a variant of GPT-3 that is specifically designed for conversational language understanding and generation. It has been fine-tuned on a large dataset of conversational interactions and is able to understand the context and flow of a conversation. ChatGPT can be used for tasks such as:
- chatbot development
- customer service
- dialogue generation
In terms of capabilities, GPT-3 is more versatile and can be used for a wider range of tasks, while ChatGPT is specifically designed for conversational language understanding and generation. GPT-3 is also larger than ChatGPT and thus has a more extensive knowledge base, but ChatGPT is more efficient in understanding the context and flow of a conversation.
Another key difference is that GPT-3 is available via OpenAI’s API, while ChatGPT is only available as a pre-trained model that can be fine-tuned on specific tasks and datasets.
Engine Comparison (Davinci/Curie/Babbage/Ada)
One of the most impressive features of GPT-3 is the availability of four different engines to choose from: Davinci, Curie, Babbage, and Ada. Each engine has its own unique capabilities and is suited for different types of tasks.
Davinci
First, we have the Davinci engine. This engine is designed for more complex tasks such as:
- writing creative fiction
- poetry
- screenplays
It is capable of generating highly creative and nuanced responses, making it ideal for tasks that require a more artistic touch.
If you’re looking to write a short story, compose a poem, or even write a screenplay, the Davinci engine is the perfect choice.
Curie
Next, let’s take a look at the Curie engine. This engine is designed for simple and direct tasks such as:
- answering questions
- providing customer service
- providing summaries and translations
It excels at providing simple, direct, and accurate responses to queries.
This makes it the perfect choice for tasks such as customer service, where customers expect quick and accurate answers to their questions. Additionally, Curie can be used for tasks such as summarizing long articles or documents, and even providing translations.
Babbage
The Babbage engine, on the other hand, is designed for tasks related to:
- data analysis
- business intelligence
It can be used to analyze data sets, create visualizations, and provide insights and recommendations.
This engine is perfect for businesses and organizations that need help with data analysis and business intelligence, or for researchers and analysts who need assistance with their data.
Ada
Finally, we have the Ada engine. The engine is designed for tasks related to:
- programming
- software development
It can be used to write code, troubleshoot errors, and provide explanations of programming concepts.
This engine is perfect for developers and programmers who need assistance with their code, or for students learning programming who need help understanding a particular concept.
All in all, GPT-3 offers four different engines to choose from, each with its own unique capabilities. The Davinci engine is ideal for more complex tasks that require a creative and nuanced response. The Curie engine is perfect for simple and direct tasks such as answering questions and providing customer service. The Ada engine is perfect for tasks related to programming and software development, while the Babbage engine is ideal for tasks related to data analysis and business intelligence. Understanding the capabilities of each engine and when to use them will help you get the most out of GPT-3.
OpenAI API
OpenAI API is a cloud-based platform that allows developers to access GPT-3 and other models developed by OpenAI. The API provides a simple, easy-to-use interface for interacting with the model, which means that developers do not need to have extensive knowledge of natural language processing to use it. The API also allows developers to fine-tune GPT-3 on specific tasks and datasets, and to access pre-trained models that are tailored to specific use cases.
The OpenAI API can be used for a wide range of natural language processing (NLP) tasks. Some of the most common uses of the API include:
- Language Translation: Translate text from one language to another, making it easy to communicate with people who speak different languages.
- Text Generation: Generate new text based on a given prompt or input. This can be used to generate headlines, summaries, and even entire articles.
- Text Summarization: Summarize long documents or articles into shorter, more concise versions. This can save time and make it easier to read and understand important information.
- Chatbot Development: Develop chatbots that can understand and respond to natural language input. This can be used for customer service, virtual assistants, and other applications.
- Question Answering: Answer questions with high accuracy and fluency, this feature is available with all GPT-3 engines.
- Language Understanding: Understand the meaning of text, this can be used to analyze customer feedback, analyze customer sentiment, and more.
- Text Completion: Complete text based on a given prompt, this can be used for autocompleting forms, writing emails, and more.
- Text Classification: Classify text into different categories, such as spam or not spam, positive or negative sentiment, etc.
These are just a few examples of the many things that the OpenAI API can be used for. The API is highly flexible and can be used for a wide range of NLP tasks, making it a powerful tool for developers and organizations.
Cost Comparison of OpenAI API Engines
When it comes to language processing, one of the most important considerations is cost. This is especially true when it comes to GPT-3 models, which have quickly become a staple in the field of natural language processing. But with multiple models available, how do their costs compare?
It’s worth noting that cost should not be the only factor considered when choosing a GPT-3 model. It’s important to carefully evaluate the costs and capabilities of each model before making a decision.
Let’s take a look at the four main GPT-3 models: Davinci, Curie, Babbage, and Ada. Each model is priced based on the number of tokens processed — with 1,000 tokens equating to approximately 750 words.
Starting with the lowest tier, 1,000 tokens (or ~750 words) can be processed for:
- $0.02 with Davinci
- $0.002 with Curie
- $0.0005 with Babbage
- $0.0004 with Ada
As the number of AI words delivered increases, so do the costs.
For example, processing 2,500 tokens (or 1,875 words) would cost:
- $0.05 for Davinci
- $0.005 for Curie
- $0.0013 for Babbage
- $0.0010 for Ada
~1.5 million words can be delivered for $40 with Davinci, $4 with Curie, $1 with Babbage and $0.80 with Ada.
ChatGPT Pro has been initially priced at $42, which is the equivalent of the OpenAI API (davinci) processing ~1,575,000 words.
Word Counts in Literature
When it comes to writing, word count can be a crucial factor in determining the success and impact of a piece of literature. But what exactly does a certain word count signify, and how does it compare to other works in the literary world?
Let’s take a look at some examples. An “ideal” blog post, according to some experts, should be around 1,600 words. This allows for a good balance of information and readability, without overwhelming the reader. On the other hand, Stephen King is known for his daily word count goal of 2,000 words, which has helped him become a bestselling author.
Moving on to longer works, F. Scott Fitzgerald’s classic novel “The Great Gatsby” clocks in at 47,094 words. This is considered a shorter novel, but still packs a punch with its memorable characters and themes. In contrast, the average 300-page book has a word count of around 90,000.
Stephen King also wrote the novel “IT”, which has a word count of 445,134. This is a much longer work than “The Great Gatsby”, and demonstrates the breadth and depth that King is able to achieve in his writing. For even greater relative perspective, The King James Bible has a word count of 783,137.
Shakespeare’s works, including all of his plays and sonnets, have a total word count of 884,421. This is a testament to his enduring impact and influence in literature. Lastly, J.K. Rowling’s Harry Potter series, which includes all seven books, has a combined word count of 1,084,170. This is a clear indication of the epic scale and imagination of the series.
These examples serve to show the wide range of word counts in literature, and how they can convey different levels of depth, complexity, and impact. It’s important to remember that word count is just one aspect of writing, and shouldn’t be the sole focus. However, it is an interesting way to compare and contrast different works, and appreciate the power of words in literature.
OpenAI API currently prices out these example word counts to:
Conclusion
As of January 2023, AI words are budget-friendly and should continue to entice independent creators — not to mention that ChatGPT is currently free.
OpenAI’s API will unlock a wave of independent creators and imaginative startups looking to displace incumbents mired with fossilized thought, legacy baggage, gatekeeping hierarchies, and a fear of artificial intelligence.
