AWS's GPT-5.6 guide makes the deployment trade-offs more concrete

Amazon's July 24 implementation guide describes an AWS-specific route for calling GPT-5.6 Sol, Terra, and Luna: the OpenAI Responses API served from the `bedrock-mantle` endpoint. It identifies Sol as the tier for autonomous coding, security research, scientific analysis, and deeper multi-step reasoning; Terra as the balanced production option; and Luna as the high-volume, latency-sensitive option. This is a deployment and operations update rather than a new model-release determination: it documents how a catalogued model family is exposed through Amazon Bedrock.

The timing matters because AWS had already posted a July 13 availability notice for the three GPT-5.6 tiers. The later guide adds the information an implementation team needs to assess that availability: Sol is listed for US East (N. Virginia) and US East (Ohio), while Terra and Luna also include US West (Oregon). AWS says all three accept text and images, return text, use a 272K-token context window, and expose the same Responses API plus reasoning-effort controls from `none` through `max`.

For an application already written against the OpenAI SDK, AWS's prescribed migration is narrow but not entirely invisible: change the base URL to the regional `bedrock-mantle` endpoint, substitute the Bedrock model identifier, and authenticate with AWS credentials or a Bedrock bearer token. That preserves the Responses API shape and tool-calling workflow, so model selection can be separated from a wholesale client rewrite. The practical comparison inside the family is therefore less about incompatible APIs than about workload placement: Sol is constrained to two listed regions, whereas Terra and Luna have a third, and AWS positions them for different capability, cost, and latency targets.

The operational value of this path is control over the inference environment, not a blanket privacy guarantee. AWS says calls run under IAM policies, can use VPC and CloudTrail, and can remain in the selected region. It also says classifier-flagged traffic may be retained for up to 30 days for automated abuse detection, with retained inputs and outputs processed by AWS and not shared with the model provider unless the customer opts in. Teams with data-residency or audit requirements should treat those details as configuration and policy work to verify, rather than assuming that an OpenAI-compatible endpoint alone settles them.

Prompt caching is the clearest economic lever in the guide, but it has measurable limits. AWS documents implicit caching as the default and explicit cache breakpoints for stable prefixes such as system instructions, tool definitions, and reference files; it says cached input is billed at a 90% discount, cache writes at 1.25 times the uncached input rate, and a cacheable prefix must contain at least 1,024 tokens. Cache hits are not guaranteed even for identical prefixes, so engineers should instrument `cached_tokens` and `cache_write_tokens` from each response rather than estimate savings from prompt structure alone. That makes the most defensible use case a repeated-context agent loop whose cache behavior is observed in production.