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Report · answer generation

Phase 7 — Broad RAG versus Evidence Layer

Same question and same model per pair. Arm A passes the five best lexical passages; arm B passes three selected passages (JEV, or lexical only in the Haiku no-JEV run).

Two Anthropic variants. The historical Haiku run used cached JEV scores (estimated cost). The Haiku EL + lexical, no JEV run measures the same setup without any JEV scoring.

OpenAI and DeepSeek (historical)

ProviderTotal tokens A → BLLM cost A → BLatency A → BQuality
OpenAI gpt-6.1-sol507.75 → 386.10
−23.96%
$0.069660 → $0.0588802,264 → 3,061 msCorrectness unchanged; completeness lower; no general improvement established.
DeepSeek V4 Pro728.95 → 570.52
−21.73%
$0.032920 → $0.0286224,974 → 5,094 msSmall metric-dependent changes; no general improvement established.

Anthropic — with estimated JEV (historical) and without JEV (new)

Model · variantTokens A → BMeasured LLM cost A → BSystem cost A → BQuality readout
Claude Haiku 5.5 · EL + lexical, no JEV912.25 → 701.00
−23.16%
$0.007876 → $0.006698$0.007876 → $0.006698
−14.96%
Correctness 2.000 → 1.975; completeness 1.950 → 1.725 (CI −0.40, −0.075); groundedness 1.975 → 2.000. No quality gain established.
Claude Haiku 5.5 · EL + estimated JEV906.15 → 710.43
−21.60%
$0.007754 → $0.006765$0.007754 → $0.009965
+$0.002210
No quality gain established.
Claude Sonnet 5.5 · EL + estimated JEV822.75 → 631.68
−23.22%
$0.121724 → $0.103798$0.121724 → $0.106998No quality gain established.
Claude Opus 5.5 · EL + estimated JEV915.75 → 723.00
−21.05%
$0.317848 → $0.280656$0.317848 → $0.283856No quality gain established.

JEV cost on the “estimated JEV” rows is a list-price estimate, not an invoice. The “no JEV” row made no JEV call, so its system cost equals measured LLM cost.

Limitations