recommending-model-tier

To choose the right Claude model tier for RAG synthesis and classification, evaluate each task's reasoning depth, latency, cost, and safety constraints to assign Haiku, Sonnet, or Opus accordingly. This ensures optimal task routing without overpaying or downgrading necessary reasoning.

The best way to optimize Claude API costs across a pipeline is to implement tier routing, assigning cheaper models like Haiku to extraction and classification while reserving Opus for complex planning or code generation. This approach includes cost projections and escalation rules to keep tier changes defensible.

Setting up model escalation rules for Claude tier routing involves defining safety and reasoning thresholds that trigger a jump from Haiku or Sonnet to Opus. You can establish these rules alongside latency estimates and eval guidance to validate when deeper reasoning is necessary.

Yes, you can use different Claude model tiers for code generation and judge workflows by evaluating the specific reasoning depth required for each. Assigning Sonnet or Opus to code generation while using a capable tier for judging ensures both tasks meet their safety and latency constraints efficiently.

You should not use Claude Opus for workflow tasks like simple extraction or classification where latency and cost constraints are tight and reasoning depth is minimal. Downgrading to Haiku or Sonnet for these specific tasks prevents unnecessary spending while maintaining output quality.

Yes, Claude tier routing supports same-family cache guidance to optimize latency. By keeping tasks within the same model family when possible, you can leverage caching mechanisms to reduce response times across repeated pipeline stages like rewrites and summarizations.