OpenAI opens up ChatGPT and Whisper APIs from $0.002

OpenAI has made the ChatGPT API available to developers today, powered by the gpt-3.5-turbo model, along with an API for Whisper, its speech recognition system. The move turns two tools that have so far had a highly visible public presence into components other companies can integrate directly into their products.

The news matters because of both the price and the simplicity of access. Instead of training its own model or maintaining complex infrastructure, an application can send a request to OpenAI’s servers and receive a conversational response or an audio transcription.

A ChatGPT model for third-party products

The gpt-3.5-turbo API costs $0.002 per 1,000 tokens, OpenAI has announced. A token is a unit of text: in English, it is roughly equivalent to four characters or three-quarters of a word, although the ratio varies by language. The company puts the price at one-tenth the cost of its previous GPT-3.5 models.

This does not mean embedding the ChatGPT website inside an application. Developers access the model through an application programming interface, or API: a standardized channel that lets one program request a task from another service. They can define the assistant’s role, provide a conversation history and send the user’s question from their own interface.

The model supports up to 4,096 tokens across the prompt and response. That limit allows for short conversations and common text tasks, but it requires developers to manage how much context is retained during longer exchanges. A company looking to summarize lengthy documents, for example, will have to split them into chunks or select the relevant information in advance.

The API’s arrival is accelerating use cases that were already being tested with language models: customer-service assistants, writing tools, search interfaces for internal documents, programming support and conversational tutors. The change does not guarantee that these products will be reliable. GPT-3.5-turbo can produce convincing but incorrect answers, so services using it in sensitive areas will need review processes, clear limits and mechanisms for escalating difficult cases to a person.

Whisper puts voice on the same bill

OpenAI has also launched an API for Whisper, its automatic speech recognition model, which it released as open-source software in September 2022. The service costs $0.006 per minute of audio and can transcribe recordings and translate audio from other languages into English.

Until now, integrating Whisper meant downloading and running the model on a company’s own servers—an option useful for organizations that need technical control or process large volumes, but less accessible to small teams. The API removes that installation step: an interview platform, meeting tool or news outlet can send an audio file and receive text ready for processing.

Speech recognition is not the same as understanding a conversation. Proper names, background noise, multiple speakers and unusual pronunciation remain sources of errors. For publishing subtitles, legal transcripts or medical records, automatic transcription should still be treated as a first draft, not a final document.

Low price, high dependence

The lower cost could change which projects are viable. At $0.002 per 1,000 tokens, many conversational features stop being an expensive experiment and acquire a low marginal cost. That benefits small businesses and independent developers, but it also concentrates an essential part of their product in OpenAI’s infrastructure and commercial terms.

The company has said it will retain data sent to the API for 30 days to monitor potential abuse and will not use it to improve its models unless the customer gives explicit consent. That is an important condition for organizations looking to analyze internal information, although it does not eliminate the need to assess what data can be sent to an external provider.

The next challenge will be distinguishing between a superficial integration and a useful product. The API makes it easier to add a chat box or automatic transcription; designing safeguards, protecting data and taking responsibility for model failures will remain the job of whoever builds the service.