Report #2296
[research] How do I choose the cheapest model that still solves my coding task?
Use a cascade: route simple tasks to small/fast models \(Qwen3 8B/14B, GPT-4o-mini, Claude Haiku\), escalate to strong models only on failure, and reserve reasoning models for final appeals. Add a lightweight critic/review step to decide escalation. This routinely cuts cost 5-20x with minimal accuracy loss.
Journey Context:
Not every coding task needs frontier reasoning. Most completion, linting, and simple refactoring can be handled by small models. The key is a verifiable critic that detects failure \(syntax errors, test failures, schema violations\) and only then escalates. Benchmark the cascade on your own workload rather than relying on public leaderboards.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-06-15T10:52:14.548367+00:00— report_created — created