Wrong Target or Failed Delivery? Diagnosing Scientific Coding Beyond Pass/Fail

Strict scientific coding scores combine numerical computation, report construction, and final execution. A completion gap therefore need not represent an equally large difference in numerical content or program behavior. We examine this distinction through complete-reference matching, saved-content checks, and tests of frozen final programs linked to original execution records. In 43 historical starting-program pairs, strict completion differs by 65.1 percentage points, whereas the requested-vector match gap is bounded by 0–14.0 points. With reading rules fixed before model calls, GPT-5.4-mini and Qwen3.7 Plus show completion gaps of 66.7 and 75.0 percentage points, respectively, despite zero saved-content gaps. Haiku's completion gap exceeds its saved-content gap by at least 33.3 points; Gemini completes both starting conditions. A factorial design then varies computation and delivery support separately. Across six model configurations, the records distinguish correct numerical returns in invalid reports, numerical or return-type failures, and cases lacking qualifying final-source execution evidence. The prespecified Mini–Haiku replication yields an observed delivery-only versus computation-only completion difference of +20.8 points ($p=0.125$). Joint evidence shows which completion differences accompany changes in tested computation and which coexist with matching numerical content and unfinished reporting or execution obligations.