I benchmarked 8 AI coding agents on the same project. Results: one production-ready out of four, total cost $1.94.

I needed to build a VPS management toolkit. Instead of writing it myself, I turned it into a reproducible benchmark: same functional brief, 8 tool/model combinations, two phases (architecture then code), blind external code review. Key findings: None of the 8 models asked clarifying questions before producing a plan. Every single one generated first, clarified second — the reverse of how an experienced engineer works. The tool wrapper (Claude Code, Copilot CLI, OpenCode) had no measurable impact on planning quality. Same model = same output regardless of tool. Planning phase cost: $0.06. Code phase cost: $1.67. Factor of 28 — that ratio explains most of the real economics of AI coding. One out of four implementations was judged production-ready by an independent blind review. Total cost: $1.94. Estimated equivalent on Copilot + Sonnet 4.6: ~$25. What discriminated the models: The winning implementation self-tested during the session, caught two Pydantic v2 validation bugs, fixed a sed substitution issue, and delivered 37/37 tests passing. The others delivered and stopped. That behavior — taking responsibility for the output — is what separated production-ready from not. Full methodology, scoring grid, and all 4 implementations are in the public repo. Reproducible if you want to challenge the results.