OpenAI API Pricing 2024: Understanding GPT-3 Pricing In-Depth

The release of ChatGPT has created an enormous buzz around the technology that powers it. And it's not hard to see why. With its high accuracy in performing natural language processing tasks, ChatGPT has the potential to enhance customer experience, automate various language-related tasks, optimize marketing strategies, increase engagement levels, and many more.

 

If you're reading this article, you've likely already had the chance to experiment with ChatGPT, or you may have seen it in action, and now you are considering taking your projects to the next level by harnessing the power of this technology.

 

Some questions may likely come to mind including "How much does it cost, and how does the cost structure work?"

 

Luckily for you, in this post we explain what determines the cost of using GPT-3 and how to calculate the approximate cost of building a GPT-3 based app. Please keep reading.

 

Official pricing for the use of GPT models

The cost of building a GPT-3-based app can vary significantly depending on several factors, such as the project's scope, the complexity of the features, and the team's expertise. However, the most significant cost of building a GPT-3 based app lies in the usage fees charged by OpenAI – the company behind the technology.

 

OpenAI offers to choose from four language models: Ada, Babbage, Curie, and Davinci. Although Davinci (text-davinci-003) is often considered the most powerful OpenAI model, other models excel in specific tasks and can even outperform Davinci in certain cases, particularly in terms of speed.

 

Below you can see the pricing for OpenAI base language models.

OpenAI's pricing is based on a token-based model, with the price calculated for every 1K tokens used. For instance, a token is considered a textual unit (such as a character, a space, or a punctuation mark) processed by an OpenAI model. 

 

For the Davinci model, a cost of about $1 for every 50K tokens used. Whether this is deemed expensive or not depends on the specific use case. For example, a developer running a small-scale project or experimenting with GPT-3 may find the prices reasonable. However, a large organization processing massive amounts of text data might find the costs adding up quickly.

 

It's worth mentioning that OpenAI provides a free tool called Tokenizer that gives you an idea of how many tokens your text will add up.

 

 

 

Factors affecting the cost of GPT-3

GPT-3 is one of the most advanced natural language processing models available. It is being used for various commercial applications, from customer service to text summarization and beyond. But how much does it cost to use GPT-3? Here’s a look at some key factors that can impact the cost of using GPT-3:

 

Model tier

Different model tiers have different capabilities and pricing models. The more powerful models like Davinci are generally more expensive than the standard models.

 

Usage volume

OpenAI's pricing model is based on usage volume, with charges per 1K tokens processed. The more tokens you process, the higher the cost.

 

Amount of computing power

The amount of computing power needed for a project can also impact the overall cost. As GPT-3 is a large model, it requires disproportionately high computing resources and storage capacity compared to simpler models like GPT-2.

 

Consequently, if your project uses large amounts of data or complex computations, you can expect increased costs associated with usage fees and additional overhead expenses related to computational resources.

 

Use case

Specific applications may require more or less processing power which impacts the cost. For example, suppose a user requires GPT-3 to perform complex natural language processing tasks that involve a large amount of data processing. In that case, the cost may be higher than other use cases.

 

API calls

The more API calls you make, the more your usage volume and associated costs will increase. This can occur if your application requires frequent requests to the GPT-3 API.

 

Concurrency

If the application requires a high concurrency level, with multiple requests being processed simultaneously, this can also impact the cost.

 

Considering these factors, you can make informed decisions about optimizing usage and managing costs when using GPT-3. If you need help with it, feel free to contact us.

 

 

Comparison of cost for GPT models: GPT-4 vs GPT-3

The cost of using GPT-4 is usually higher than GPT-3. This is because GPT-4 has more advanced features, a more extensive library of available data, and it has been trained on a much larger set of data.

 

The average cost for GPT-3 models starts at around $200 per month, while the price tag for GPT-4 can range anywhere from $400 - $1000 per month. But above all, the cost to use GPT-3 and GPT-4 models can vary significantly depending on the specific requirements and needs.

When choosing a GPT model, consider how much computing power you need to run the application and what type of data you'll use to determine which one best suits your needs. For example, GPT-3 may require more computing power than GPT-4, but it also has specialized models better suited for text-processing tasks.

 

On the other hand, GPT-4 is better suited for processing audio and video data, so you may need more computing power if that’s your primary goal.

 

 

How to estimate the cost of using GPT-3

Measuring and estimating the cost is crucial while considering the usage of GPT-3. Here are some critical steps that help to calculate the price of using GPT-3:

 

1. First, you should identify the specific task or use case to which you want to apply GPT-3 to. When defining this task, you should be as specific as possible, as it can directly impact cost estimation.

 

2. Next, you should determine the number of tokens required for the task. The number of tokens required can be estimated using mock tests or by carefully analyzing and preparing the data you plan to feed into the model.

 

3. With the number of tokens in mind, you can use OpenAI's cost calculator to determine how much the task will cost. For every 1K tokens processed, the cost is $0.006. The calculator will estimate the total cost for the task and the associated processing time.

 

4. If your estimate is higher than expected, you can adjust the parameters, such as the amount of data or the level of processing power required, to arrive at a more manageable cost estimate.

 

Сost of using GPT-3: example

The cost of using the GPT-3.5 model is $0.002 per 1K tokens. Let's assume the project anticipates up to 200K user requests monthly. Each request can contain between 20 and 1000 tokens.

 

Assuming the average value within this range represents typical usage, the average number of tokens per request is approximately 510 ((20 + 1000) / 2).

 

Considering this, 200K monthly requests amount to approximately 102 million tokens (200K requests * 510 tokens/request).

 

With the cost of $0.002 per 1K tokens, the usage cost of the GPT-3.5 model for such a project is $204 per month (102 million tokens * $0.002 / 1K tokens).

 

Note: This is only an approximate example of cost. Prices are calculated individually depending on your specific project requirements and goals. 

 

If you want to calculate GPT cost, share your app idea with us, and we'll be in touch shortly.

 

 

Onix experience

Onix has vast experience in providing GPT development services that allow organizations to build and deploy their own custom-built artificial intelligence (AI) systems. We create custom AI models with the help of specific parameters and algorithms.

 

The СhatGPT API types we've worked with:

 

• Chat – is the base type for communicating with ChatGpt in a chat format.
• Completions – create a completion for the supplied hint and options.
• Edits – after receiving a hint and instruction, the model will return an edited version of the hint.
• Images – given a hint and/or an input image, the model will create a new image.
• Embeddings – get a vector representation of the given input data that models and machine learning algorithms can easily use.
• Audio – convert sound to text.
• Fine-tunes – tune and adapt the selected model to specific data.
• Moderations – analyze text for OpenAI policy violations.

 

Recently, our company has developed a chatbot that partially solves the tasks of an HR manager. Such a chatbot can act as an assistant that generates greetings for any holiday. We have specified { role: 'system', content: 'I am HR-manager it-company' } in the system message so the bot understands who it is talking to. 

Request

Response

Result

 

Our services leverage the power of machine learning and AI to uncover insights, improve decision-making, and create breakthrough results. 

 

Read also: How AI Will Transform Your Business: 8 Applications

 

Conclusion

Generally speaking, the cost of building an app with a GPT-3 model depends on three main factors: the size and complexity of the project, the resources required to complete it, and the amount of time needed. The larger and more complex a project is, the higher its costs will be.

 

Additionally, if more resources are required to complete the project, such as extra hardware or human resources, that can add to the cost. Finally, depending on how long it takes to finish a project also affects its overall cost.

 

Using OpenAI's GPT-3 model requires careful planning and understanding of the costs of building a GPT-3 based application. Before proceeding with development, you and your development team should research and factor in all the expenses related to building a GPT-3 based app. This can help prevent any unexpected financial surprises down the line. 

 

If you're ready to use GPT-3 models, feel free to contact us. Our specialists will estimate the costs associated with your project to ensure you will not spend more than necessary while still producing the desired results.

 

 

FAQ

 

Can I use GPT-3 for commercial use?

Yes, GPT-3 can be used for commercial use. OpenAI offers a paid tier for businesses with more than 400K monthly tokens. The cost is calculated on a pay-as-you-go basis based on usage volume (with each 1K token costing $0.006). Companies can also purchase exclusive access to the model and additional features such as higher computing power and access to extra data sets.

 

Before using GPT-3 for commercial purposes, it is crucial to understand the associated costs and how they might impact your project's budget. It is also vital to understand what type of applications or use cases the model is best suited for before investing in its development and deployment.

 

How expensive is it to use GPT-3?

GPT-3 cost depends on several factors, such as model tier, usage volume, and use case. The cost of GPT-3 can range from a few dollars to thousands per month, depending on usage.

 

It's worth noting that OpenAI provides a monthly free-tier usage limit, allowing users to process up to 400K tokens for free. After that, the cost is charged based on the number of tokens processed. OpenAI also offers a cost calculator on their website, which can help estimate and monitor usage and costs.

 

Which model is more cost-effective, GPT-3 or GPT-4?

GPT-3 offers a free tier for developers and businesses with up to 400K tokens per month processed for free. After that, the cost is calculated on a pay-as-you-go basis based on usage volume (with each 1K token costing $0.006).

 

In comparison, GPT-4 is offered in three tiers – standard, large, and XLarge. Each model tier provides different capabilities, with the costs ranging from $25/hour for standard to $300/hour for XLarge. Depending on usage requirements and budget, GPT-3 may be more cost-effective than GPT-4 in certain cases.

 

Why is it important to understand the cost of using GPT models?

Using powerful models like OpenAI's GPT-3 and GPT-4 can be a great way to achieve advanced artificial intelligence (AI) capabilities, but it also comes with a cost. Knowing the cost of using such models is vital to ensure developers and businesses can decide which model they should use.

 

Without understanding the costs associated, there could be surprises down the road, such as large bills or restrictions due to usage limits.

 

Moreover, since different models come with their own unique features and capabilities, budget plays an essential role in determining which model fits best for a given project. Understanding the cost can help narrow down the list of viable options, ensuring developers select the model features that give them the best return on investment while still achieving their desired AI outcomes.