GPT-5.6 Sol's Fast mode makes deployment settings part of the model comparison

OpenAI's July 30 update says Fast mode replaces Priority Processing for GPT-5.6 Sol in the API. The company describes Fast as delivering up to 2.5 times the speed of Standard processing at twice the price, with no change in intelligence, and says requests previously tagged `priority` will continue to work. The same announcement lowers Terra and Luna prices while leaving Sol pricing unchanged. For teams already using Sol, the new decision is therefore not simply which model tier to select: it is whether a latency-sensitive step in an existing workflow is worth a separately priced processing mode.

The timing matters because OpenAI published a harness analysis one day earlier that reported markedly different ARC-AGI-3 results for the same Sol configuration. OpenAI says its run using the benchmark's official harness scored 13.3% on the public set, while a Responses API harness retaining reasoning and using compaction scored 38.3% and used fewer output tokens. ARC Prize's published results independently show a 13.33% public score for Sol at maximum reasoning effort. The higher figure is OpenAI's own modified-harness result, not an independently reported replacement leaderboard score, so it is better read as evidence that the evaluation setup materially affects the observed outcome.

That distinction is practical for developers migrating from GPT-5.5 or GPT-5.4. OpenAI's current model guidance recommends keeping the existing reasoning-effort setting as a baseline, then testing one level lower on representative tasks; it also recommends the Responses API for reasoning, tool-calling, and multi-turn work. Its documentation explains that continuing with `previous_response_id` can make earlier reasoning available, while compaction and context settings change how a long-running workflow carries history. A direct benchmark comparison that omits those choices can be useful, but it does not by itself predict the latency, token use, or task success of a production agent.

AWS's GPT-5.6 guidance supplies a second implementation constraint for Sol users on Amazon Bedrock: prompt caching is not merely automatic background behavior. The documentation describes explicit cache breakpoints, a minimum reusable prefix of 1,024 tokens, up to four checkpoints per request, and a 90% discount on cached-input reads, while cache writes cost 1.25 times the ordinary input rate. It also cautions that cache hits are not guaranteed and directs developers to measure `cached_tokens` and `cache_write_tokens`. Together with Fast mode, that makes workflow instrumentation central: teams should evaluate task quality, end-to-end latency, cache-hit rate, and total cost on their own stable prompts before treating a headline speed or benchmark number as a deployment decision.