# Finding 2: Cheap model + narrow lens > expensive model + broad review (one data point)

**Date:** 2026-04-26
**Task:** Check 12 rewritten hypotheses for directional bias
**How we used them:**
- Sonnet & GPT-5: full PR review context (diff, file content, issue, AC).
  Broad mandate: "review this PR." Rich context but unfocused task.
- GPT-4.1 Mini: given ONLY the 12 hypothesis texts + one focused question:
  "Do any of these hypotheses lead toward a predetermined conclusion?"
  Minimal context, laser-focused task. No diff, no project docs, no issue.

- Both Sonnet and GPT-5 approved the hypotheses as reviewers
- GPT-4.1 Mini found ALL 12 pushed toward predetermined conclusions
- Words like "requires," "necessary," "must be" were flagged as directional
- **Takeaway:** Task framing mattered more than model size. Rich context +
  broad mandate = missed the forest for the trees. Minimal context + precise
  question = found exactly what mattered. This needs more testing — was it
  the narrow framing, the lack of surrounding context, or both?