Prompt caching across the GPT-6 family can be optimized by keeping shared prefixes stable, setting explicit cache breakpoints, managing tool definitions, adjusting reasoning effort across turns, warming the cache, and diagnosing cache behavior. When migrating to GPT-6 Sol, validate these practices against actual workloads.
Suitable tasks: Multi-turn API Agent workflows that repeatedly send long instructions, tool definitions, or reference context.
Unsuitable tasks: Workflows with little shared prefix across requests, or with frequently changing inputs that cannot be reused consistently.
Applicable model versions: The source applies to the GPT-6 family. This article is filed under GPT-6 Sol, but it should not be treated as a Sol-specific test or a guarantee of gains.
Applicable clients, agents, or APIs: Implementation requires OpenAI API prompt caching, the Responses API, and the corresponding caching guide; specific capabilities depend on the API and account in use.
Recommended reasoning effort and parameters: Set them according to the task. The source specifies no fixed effort level; it only says reasoning effort can be updated between subsequent GPT-6 responses.
Establish a baseline: In the Prompt Caching Dashboard, record the cache hit rate and the breakdown of cached and uncached input tokens. Also record latency and cost.
Organize the stable prefix: Put instructions, tool definitions, and reference materials that remain unchanged across requests in the shared prefix. Keep frequently changing content toward the end.
Set cache breakpoints: Use the official prompt caching guide to choose explicit cache breakpoints and determine which prefix range to reuse. Configure the breakpoint's exact position and request format according to the API documentation.
Keep tool definitions stable: Keep tool definitions, schemas, and their order as consistent as possible. Use allowed_tools to limit the tools currently available. When no tools are available, set tool_choice: "none" if supported by the interface, rather than removing tool definitions for this reason.
Append new instructions: When behavior needs to change, append new instructions in a later developer message so they override earlier instructions while preserving the existing prefix.
Adjust reasoning across turns: To change reasoning effort, append configuration_update in a subsequent response. Keep request-level reasoning effort unchanged, and check the exact structure in the API documentation.
Warm the cache in advance: Submit known shared instructions, tool definitions, or reference materials at application startup or before a user request arrives to reduce processing time while the user waits.
Diagnose and retest: If the hit rate unexpectedly drops, use Prompt Caching diagnostics to compare recent requests and responses, and check for changes to the model, tools, settings, or inputs. After making adjustments, record the hit rate, latency, and cost again.
OpenAI says eligible shared prefixes that are reused within a 30-minute window receive a cache discount of up to 90% on cached input tokens. Actual cache hits and savings depend on the requests and workload.
The source describes these caching capabilities and configuration recommendations as GPT-6 family capabilities; it reports no separate GPT-6 Sol experiment. Hit rate, latency, and cost on Sol need to be measured separately.
The diagnostic example shows that a change to tools may cause a cache miss. Changes to other models, settings, or inputs may also affect prefix reuse.
The source does not provide full request bodies for cache breakpoints, configuration_update, or cache warming. It also does not specify breakpoint placement, field values, or compatibility across all interfaces. Consult the relevant API documentation during implementation; do not infer payloads that are not shown here.
Cache warming and cache controls are optional optimizations. Adopt them only when Dashboard and diagnostic data show they help the target workload.
GPT-6 Sol
