OpenAI billed more tokens than your logs show: reasoning, image, and cache-write tokens

TL;DR: Your request logs say 2k tokens but the bill says 9k. The gap is tokens you never see: hidden reasoning tokens, image tokens, and cache writes. Confirm the cause Check the usage breakdown on a real response: 1. On reasoning models, usage.outputtokensdetails.reasoning_tokens counts internal thinking: billed as output, never returned in the response.

The fix

TL;DR: Your request logs say 2k tokens but the bill says 9k. The gap is tokens you never see: hidden reasoning tokens, image tokens, and cache writes. Confirm the cause Check the usage breakdown on a real response: 1. On reasoning models, usage.outputtokensdetails.reasoning_tokens counts internal thinking: billed as output, never returned in the response.

The symptom

Billed tokens consistently exceed what your prompt plus visible response suggest. The usage object has fields your logging ignores.

Confirm the cause

Check the usage breakdown on a real response:

1. Hidden reasoning tokens. On reasoning models, usage.output_tokens_details.reasoning_tokens counts internal thinking: billed as output, never returned in the response. A "short" answer can carry thousands of reasoning tokens. This is the biggest surprise on o-series and GPT-5.
2. Image tokens. Vision inputs are tokenized by size and detail level and land in input tokens. A few high-detail images can outweigh a long text prompt.
3. Tool and schema tokens. Tool definitions and structured output schemas count as input tokens on every call.
4. Cache write tokens. On GPT-5.6 and later, prompt_tokens_details.cache_write_tokens reports prompt tokens written to cache, billed at 1.25x the uncached input rate. Cache writes are not free on newer models.
5. Retries and streams. Every retried request rebills its prompt tokens. Every retried stream rebills them too.

The fix

• Log the full usage object, not just prompt and completion totals. Break down reasoningtokens, cachedtokens, and cachewritetokens per call.
• Budget reasoning models with reasoning in mind: lower reasoning_effort when deep thinking is not needed, and size max_completion_tokens for reasoning plus visible output.
• Right-size image detail. low instead of high when the task allows it.
• Fix retry storms and loops first; they multiply every hidden cost above.

Verify the fix

Compare your logged token totals against the billed totals for a day. After the fix they should reconcile within a few percent. Any remaining gap has a named field in the usage object; find it before it finds your budget.

When to use this

• This covers exactly what the title says: OpenAI billed more tokens than your logs show.
• You are setting this up for the first time, or auditing an existing setup.
• You want the key gotchas in one place before you start.

When not to use this

• You are doing a different workflow with OpenAI; these steps are specific to the title above.
• You need the full reference docs; this is the short path, not the manual.

Compatibility

• Mentions 5.6 in the original; behavior may differ on much older or newer releases.

OpenAI's reasoning guide documents reasoningtokens under usage outputtokensdetails: internal reasoning billed as output but never returned. The prompt caching guide documents cachewrite_tokens on GPT-5.6 and later, billed at 1.25x the uncached input rate, and confirms images, tools, and schemas count as input tokens (https://platform.openai.com/docs/guides/reasoning, https://platform.openai.com/docs/guides/prompt-caching).