As the development community continues to leverage OpenAI’s advanced models, the need for more transparent and accessible budgeting tools has become apparent. The introduction of a programmatic way to estimate token usage and associated costs would mark a significant step forward in this regard, particularly with the utilization of cutting-edge models like GPT-4.
This proposal outlines the introduction of a dedicated API endpoint for token count and cost estimation and suggests enhancing model objects with pricing information to aid developers in making more informed decisions.
The Proposal
- API Endpoint for Token Count and Cost Estimation (
count_tokens
): This endpoint aims to provide developers with an efficient tool for estimating the number of tokens generated by their text inputs, alongside the expected cost, for a specific model, namely GPT-4. - Incorporate Pricing Information into Model Endpoints: To further aid decision-making processes, it is proposed that model detail endpoints be updated to include essential pricing information.
Benefits
- Accurate Cost Management: Enables developers to accurately manage and forecast their expenditures on the OpenAI platform.
- Seamless Developer Workflow: Integrates directly into development workflows, allowing for real-time cost estimations without manual intervention.
- Transparent Pricing: Offers clear visibility into pricing structures, promoting trust and reliability in the OpenAI ecosystem.
Suggested Implementation
Here is how the count_tokens
endpoint could be implemented, using GPT-4 as an example:
POST /v1/count_tokens
Content-Type: application/json
{
"model": "gpt-4",
"text": "Your sample text goes here."
}
Expected Response:
{
"tokens": 150,
"cost": 0.08
}
To include model-specific pricing details transparently:
GET /v1/models
Sample Response:
{
"models": [
{
"id": "gpt-4",
"object": "model",
"token_cost_per_thousand": "0.08"
},
// More model objects...
]
}
The introduction of the count_tokens
endpoint, particularly with support for GPT-4, represents a critical enhancement to the OpenAI API, streamlining development processes and facilitating better budget management. Community feedback on this proposal is invaluable for refining and implementing these suggestions effectively.