GPT-5, GPT-5 mini, nano and pro retire on 11 Dec 2026.

What's retiring, and what replaces it

Dates and replacements are OpenAI's, from https://developers.openai.com/api/docs/deprecations. Plan to have moved before the date; check the source for any change.

Why a swap can quietly change your outputs

A replacement may score higher on benchmarks, but that doesn't mean it does your task the same way. Usually nothing errors. The answers change, and your users may notice before you do.

Format

The new model may wrap JSON in a code fence, add Markdown, change headings or drop a field your code reads.

Tone

Warmer, blunter, more formal or more chatty. It reads as a different writer.

Refusals

It may refuse or hedge on inputs the old model handled, or answer ones it used to decline.

Length

Answers often get longer or shorter. That can break a limit on screen, in an SMS or in a feed.

Instructions

Edge-case rules in your prompt (never mention X, always end with Y) are where models differ most.

Cost and speed

Price per token, tokens per answer and thinking time all change, so the cost of each answer can move even at a similar list price.

Re-test before you switch

1. Find every place your code, config and environment variables name the old model ID.
2. Collect 10 to 20 real inputs, including the awkward ones that went wrong before.
3. Write down what a good answer does: its format, length, what it must say and must not.
4. Run the old and the new model on the same prompt and the same inputs, side by side.
5. Read the answers that changed, not just the pass rate.
6. Compare cost and time per answer, then adjust the prompt if you need to and test again.
7. Switch before the date, and keep the cases as a regression test for the next change.

We can run both sides of these swaps for you: GPT-5 against GPT-5.6 Sol; GPT-5 mini against GPT-5.6 Terra; GPT-5 nano against GPT-5.6 Luna; GPT-5 pro against GPT-5.6 Sol.