ChatGPT Word to Prompt Cost Calculator
How much does this prompt cost per OpenAI model?
What is all this?
GPT-4 is the latest addition to OpenAI’s lineup of powerful language models. It is a large multimodal model that can accept text inputs and generate text outputs, with the ability to process image inputs in the future. GPT-4 surpasses its predecessors, including GPT-3.5 models, in terms of capability and accuracy for solving complex problems. It leverages its extensive general knowledge and advanced reasoning capabilities to provide more accurate and comprehensive responses. While optimized for chat-based applications, GPT-4 is also highly effective for traditional completion tasks. It offers a maximum token limit of 8,192 and is based on training data up to September 2021. OpenAI regularly updates GPT-4, with the latest model iteration becoming available two weeks after its release.
On the other hand, GPT-3.5 models, particularly gpt-3.5-turbo, provide an excellent balance between capability and cost-effectiveness. GPT-3.5-turbo, optimized specifically for chat applications, offers similar performance to other GPT-3.5 models but at only 1/10th the cost of text-davinci-003. It supports a maximum token limit of 4,096 and is trained on data up to September 2021. OpenAI releases regular updates for GPT-3.5-turbo, with the latest iteration becoming available two weeks after its release. However, it’s important to note that GPT-3.5-turbo-0613 and gpt-3.5-turbo-16k-0613 are temporary snapshots from June 13th, 2023, and will not receive further updates. They will be deprecated three months after the introduction of new versions. Overall, GPT-3.5-turbo is recommended for its cost-effectiveness and reliable performance across a range of language tasks.
How does OpenAI calculate the cost?
The cost of using OpenAI’s language models is determined based on the number of tokens processed. Tokens can be considered as pieces of words, where approximately 1,000 tokens correspond to 750 words. The pricing is calculated per 1,000 tokens.
For GPT-4, which offers broad general knowledge and advanced reasoning capabilities, there are different pricing tiers based on the context length. The 8K context option costs $0.03 per 1,000 tokens for input and $0.06 per 1,000 tokens for output. On the other hand, the 32K context option is priced at $0.06 per 1,000 tokens for input and $0.12 per 1,000 tokens for output. These prices reflect the model’s ability to understand complex instructions in natural language and accurately solve difficult problems.
Regarding chat-based applications, OpenAI provides the gpt-3.5-turbo model, which is specifically optimized for dialogue. The performance of gpt-3.5-turbo is comparable to the Instruct Davinci model. Similar to GPT-4, the pricing for gpt-3.5-turbo is determined based on the context length. The 4K context option costs $0.0015 per 1,000 tokens for input and $0.002 per 1,000 tokens for output. For a larger context length of 16K, the pricing is $0.003 per 1,000 tokens for input and $0.004 per 1,000 tokens for output.
By considering the token count, developers can accurately calculate the cost of using OpenAI’s language models, allowing them to manage their usage and budget effectively.
Here are a few simple formulas
For GPT-4:
8K context:
- Input Cost = (Number of Input Tokens / 1000) * $0.03
- Output Cost = (Number of Output Tokens / 1000) * $0.06
32K context:
- Input Cost = (Number of Input Tokens / 1000) * $0.06
- Output Cost = (Number of Output Tokens / 1000) * $0.12
For gpt-3.5-turbo:
4K context:
- Input Cost = (Number of Input Tokens / 1000) * $0.0015
- Output Cost = (Number of Output Tokens / 1000) * $0.002
16K context:
- Input Cost = (Number of Input Tokens / 1000) * $0.003
- Output Cost = (Number of Output Tokens / 1000) * $0.004
These formulas allow you to calculate the cost based on the number of tokens in your input and output. Substitute the appropriate values into the formulas, where the “Number of Input Tokens” represents the token count in the input text and the “Number of Output Tokens” means the token count in the generated output. The resulting cost will give you an estimation of the charges associated with using the respective models.