Most teams rebuild GPT-5.6's reasoning from scratch on every turn of a conversation. The model can persist and reuse reasoning items across turns instead, which raises multi turn quality and lifts cache hit rates at the same time.
The default behaviour of a multi turn conversation with a reasoning model is quietly wasteful. On each new turn the model receives the visible message history, reconstructs whatever internal reasoning it needs, and discards it again when the turn ends. You pay for that reconstruction every time, and because the reconstruction is not deterministic, the model can reach a different conclusion on turn four than it reached on turn two from the same underlying facts.
GPT-5.6 addresses this directly with persisted reasoning. Rather than starting cold, the model can reuse reasoning items that are already available from earlier turns in the same conversation. The behaviour is selected through the reasoning.context setting on the request. This is not a prompt technique and it is not something you can approximate by pasting a summary of the previous turn into the context window, because the reasoning items being carried forward are the model's own working state rather than a natural language recap of it.
Two things improve at once, which is unusual. Quality improves because the model is no longer re deriving a chain of reasoning it already completed, so multi turn conversations hold their position instead of drifting. Cost improves because carrying stable reasoning items forward means a much larger share of each request matches what was sent before, which is exactly the condition prompt caching rewards. Teams running long agentic sessions or extended analyst style conversations tend to see the cache effect before they notice the quality effect, because it shows up on the invoice first.
The reason this matters right now is that it stacks with the July pricing change. OpenAI cut the API price of the lighter GPT-5.6 tier by roughly eighty percent and the heavier tier by roughly twenty percent. A cheaper per token rate multiplied by a higher cache hit rate compounds, and the combination changes which workloads are economically viable. Conversations that were previously trimmed aggressively to control token growth can now run at full fidelity.
One caution before you turn it on everywhere. Persisted reasoning is the right default for a conversation that is genuinely continuous, where later turns build on earlier ones. It is the wrong default for a shared endpoint where consecutive requests come from unrelated users or unrelated tasks, because carrying reasoning forward across an unrelated boundary is both a correctness problem and a data isolation problem. Scope the setting to the conversation, not to the service.
Try it today
- Identify which of your GPT-5.6 endpoints serve genuinely continuous conversations rather than independent one shot requests. Only the continuous ones are candidates.
- Set reasoning.context on those requests so reasoning items persist across turns, and confirm your client library is passing the field through rather than silently dropping an unrecognised parameter.
- Verify that your conversation identifier maps to exactly one user and one task. If a single session object is reused across unrelated users, fix that before enabling persistence.
- Instrument cached input tokens as a separate metric from total input tokens, then compare a week of traffic before and after. The cache hit rate is where the cost benefit appears.
- Re run your multi turn evaluation set with persistence on and off, scoring specifically for position drift across turns four and later, which is where the quality difference is easiest to detect.
- Pair the change with a reasoning_effort review. Persisted reasoning reduces how much fresh reasoning each turn requires, so an effort level you tuned months ago is probably now set higher than the work actually needs.
