What this error means
prompt_caching not activated on new model families (Opus 4.5/Haiku 4.5) — Cache control header ignored, costing more tokens than expected is a Anthropic API failure pattern reported for developers trying to fix prompt caching not being applied on claude opus 4.5 and haiku 4.5 models despite cache_control parameter set. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.
Why this happens
GitHub issue anthropics/anthropic-sdk-python#1085 — 7 comments, open since Nov 2025. Affects billing cost directly because uncached prompts consume full token count. New model-specific regression. Not in covered-errors.md. Category: Anthropic API — cache behavior bug affecting production costs.
Common causes
- GitHub issue anthropics/anthropic-sdk-python#1085 — 7 comments, open since Nov 2025. Affects billing cost directly because uncached prompts consume full token count. New model-specific regression. Not in covered-errors.md. Category: Anthropic API — cache behavior bug affecting production costs.
Quick fixes
- Confirm the exact error signature matches
prompt_caching not activated on new model families (Opus 4.5/Haiku 4.5) — Cache control header ignored, costing more tokens than expected. - Check the Anthropic API account, local tool state, and provider configuration involved in the failing workflow.
- Compare the failing environment with a known working setup, then change one configuration value at a time.
Platform/tool-specific checks
- Verify the command, editor, extension, or API client that produced the error.
- Compare local settings with CI, deployment, or editor-level settings when the error appears in only one environment.
- Avoid deleting credentials, local model data, or project settings until the failing scope is clear.
Step-by-step troubleshooting
- Capture the exact error message and the command, editor action, or request that triggered it.
- Check whether the failure is account/auth, quota/rate, model/provider, local runtime, or deployment configuration.
- Review the source evidence below and compare it with your environment.
- Apply one change at a time and rerun the smallest failing action.
- Keep the working fix documented for the team or deployment environment.
How to prevent it
- Keep provider/tool configuration documented.
- Record non-secret diagnostics such as tool version, provider name, model name, and command path.
- Add a lightweight check before CI or production workflows depend on the tool.