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Verify the target model and find every caller
Start with the exact model identifier in the request, not a shorthand used in a ticket, dashboard, or internal configuration. The Anthropic model-status documentation surfaced for this migration identifies Sonnet 5 as claude-sonnet-5 and describes migration from Sonnet 4.5 or earlier. Model names and availability can change, so confirm the identifier in the current official model-status documentation when you plan the change.
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Next, inventory production and non-production usage so the test covers all callers, not just the main application. Anthropic’s guidance points developers to the Claude Console Usage page: export usage and review the CSV breakdown by API key and model. Use the results to locate services, scheduled jobs, and environments that still call the model you plan to replace. Map each API key to an owner and deployment path in your own records; the export alone does not establish which service owns a key.
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- Record the service or job, environment, API key owner, current model identifier, and SDK/API version.
- Include background jobs, staging deployments, and less frequently used workflows, not only interactive user requests.
- For each caller, note the request parameters and whether it parses, stores, or forwards structured response content.
Review the thinking configuration before changing models
Anthropic’s migration guidance says that moving from Sonnet 4.5 or earlier to Sonnet 5 involves an effort-default change and removing manual extended thinking configured with budget_tokens. Review every request builder and shared configuration layer for that setting; do not assume a model-name substitution is the whole migration.
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Confirm the intended request configuration against the documentation for the exact target model and the SDK/API version you use. Keep this review separate from assumptions about failure modes: the cited guidance does not establish that a particular thinking-block signature, parser error, or response shape will fail. If your application depends on such details, verify them with actual responses from your target configuration.
Configuration checks to make
- Find explicit
budget_tokenssettings, including defaults inherited from shared libraries or environment configuration. - Review how effort is selected and whether the application relies on a previous default.
- Record the old and intended new configuration for each caller so test results can be traced to a specific change.
- Check that test and production deployments use the same intended model identifier and request-building code.
Turn the migration into an application-specific regression test
Anthropic recommends testing applications with newer models well before the current model’s retirement date. It does not prescribe a ready-made harness, fixtures, pass thresholds, or particular thinking-block assertions. The following is an engineering approach for translating that general recommendation into checks for your own application.
Run representative requests through the old and intended new configurations in a controlled environment. Choose cases that exercise the behaviors your software actually relies on, then judge results against application requirements rather than expecting identical wording. Capture the request configuration and the returned content your application receives, subject to your own data-handling rules, so a failure can be reproduced.
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| Check | What to exercise | What to record |
|---|---|---|
| Target and request configuration | Each caller with the intended model identifier, effort setting, and revised thinking configuration. | Model identifier, SDK/API version, relevant parameters, and whether legacy budget_tokens configuration remains. |
| Task success | Representative routine requests plus business-critical and known difficult cases. | Whether each case meets its application-specific acceptance criteria; record the criteria before running tests. |
| Content handling | Paths that parse, validate, persist, or forward returned structured content. | Whether parsing and downstream handling succeed, and the response or error details needed to reproduce a failure. |
| Operational behavior | The same workload under the old and new configurations in your environment. | Observed latency and cost using your own measurements; the cited sources publish no comparative results for your workload. |
| Recovery | The deployment and rollback procedure for each caller. | The person or system that can revert the change, the trigger for rollback, and confirmation that the old configuration remains deployable. |
Make content-block checks match your code
If code assumes that a response always contains a particular content-block arrangement, test that assumption at the boundary where the response enters your application. Assert the behavior you need—such as successful parsing, validation, or a controlled error when content is unexpected—rather than asserting an undocumented signature or treating one observed response as a universal format guarantee.
For business-critical outputs, use stable fixtures where a deterministic check is appropriate and human review where correctness depends on judgment. Decide acceptance criteria before comparing results. There is no universal threshold in the cited guidance, and a passing parser test does not by itself establish that the model’s answer is fit for the task.
Roll out in stages and define a rollback trigger
Keep the old model path available while you validate the new one. First run the regression set outside production; then move a limited, observable set of traffic or callers, if your architecture permits it. Expand only when the checks relevant to that caller pass. This staged rollout is an engineering recommendation, not a deployment sequence specified by Anthropic.
- Baseline: Save the old request configuration and run the representative test set so you have a comparison for task success, parsing, latency, and cost in your environment.
- Update: Change the target identifier and thinking-related configuration, then rerun the same tests against the intended SDK/API version.
- Canary: Route a limited caller or workload to the new configuration and monitor application-level failures and acceptance checks.
- Expand or revert: Expand only after the pre-agreed criteria pass. Revert the caller if a critical task, content-handling path, or operational limit fails; investigate and rerun before trying again.
- Retire: Remove the old path only after the usage inventory and deployment records show that the callers in scope have migrated and the rollback window is no longer needed.
What this test plan can and cannot establish
A successful migration test can show that the specific callers, configurations, fixtures, and workload you exercised behaved acceptably at the time of the test. It cannot establish that every response will have identical content, that an untested caller is safe, or that a named thinking-block failure is a documented general defect. Attribute any failure to the configuration and reproducible case you observed, and avoid generalizing beyond it.
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