Why Are Token Costs Spiking for My GPT-3.5 Turbo Job Categorization?

Below is some guidance on understanding unexpected token cost increases while using GPT‑3.5‑turbo:

Review Both Input and Output Tokens

Remember that both input and output tokens contribute to the total token count. Even if you’re tracking context tokens, any change in the output length (or formatting) could significantly inflate costs.

Check Your Prompt Structure

A small change in your prompt could inadvertently add extra tokens. For example, additional system instructions or modifications in formatting may be included automatically. Verify that the payload you send hasn’t changed in a way that increases the token count.

Use the Tokenizer Tool

I recommend experimenting with our Tokenizer tool to inspect exactly how both your inputs and any expected outputs are being tokenized. This will help you identify discrepancies between your manual estimates and the actual counts.

Monitor API Response Metadata

Your API responses include token usage data (both input and output) that can serve as a baseline for debugging. By monitoring this metadata, you might pinpoint whether the increased cost is coming from unexpected output tokens or a change in how tokens are counted.

Consider Other Factors

• Cumulative effects: Over many calls, even a small additional token per response can add up.
• Recent API behaviour: There’s no widespread announcement about changes in token counting for GPT‑3.5‑turbo. However, subtle updates or adjustments could affect tokenization patterns.

If you continue to observe discrepancies, it might be worth cross-checking your API call details and reaching out with specifics through our support channels. Additional context from your API usage logs (like differences between the expected and actual token counts reported) can help diagnose the root cause.

By reviewing these points, you may identify why the token count—and therefore the cost—has increased in your recent calls.