Navigating the pricing for powerful AI models like GPT-3.5 Turbo can feel a bit like trying to decipher a complex map. You've got the core technology, and then you have the different versions and how they're billed. It's a common point of confusion, and I've seen firsthand how folks grapple with it.
When we look at Azure OpenAI Service, for instance, the GPT-3.5 Turbo models have seen updates, and with those updates come pricing adjustments. The key thing to understand is that different model versions can have different pricing structures. For example, the newer 0613 versions of gpt-35-turbo and gpt-35-turbo-16k have a split pricing model. This means you're charged separately for the tokens used in the prompt (what you send to the model) and the tokens generated in the completion (what the model sends back).
This is a shift from some earlier models, like the 0301 version of gpt-35-turbo. As of my last check, this older version was often priced at a flat rate per 1,000 total tokens. The distinction between prompt and completion pricing in the newer versions is important because it can significantly impact your costs depending on how you use the model. If your prompts are very long but the completions are short, you'll be billed differently than if the reverse is true.
There was a period where there was some discussion and internal checking within Azure about whether the new pricing model applied to older versions, or if the prompt/completion pricing might have been inadvertently switched on the pricing page compared to OpenAI's own platform. It’s always a good idea to double-check the official Azure OpenAI Service pricing details for the most current and accurate figures, as these things can evolve.
For those deploying models, like someone recently asking about gpt-35-turbo with version 0613 and a capacity of 34K TPM, understanding these nuances is crucial. While documentation might primarily highlight the 4k and 16k context windows, the underlying pricing per token for prompt and completion remains the core factor. It’s this granular detail that helps in forecasting and managing your AI service expenditure effectively.
