Agreement Inversion Is Not a Mid-Tier Property: Precision of Agreed LLM Answers Follows Pair Accuracy

When two flagship models, asked separately, give the same answer to one of the hardest SimpleQA questions, that answer is usually wrong. On the 106 SimpleQA questions where July mid-tier panels failed, 45 flagship pairs without the client-served GPT arm, where web search cannot be excluded, are right on 96 of 691 agreements (13.9%). Answering only when a second model agrees needs no labels or parameters, and earlier work attributed the failures of this rule to mid-tier models. On both tiers I measured, the precision of agreed answers follows the accuracy of the pair, so inversion is not a mid-tier property. In the hardest quintile, defined the same way for each tier, precision is 17.3% in July and 15.5% in August; across all five quintiles, at the precision expected from each pair's two accuracies, flagship pairs differ from mid-tier pairs by -0.2 points [-3.1, +2.7]. The data are SimpleQA, fresh two-hop questions and product facts, collected in July, August and October 2026 with flagship models from eleven laboratories. A partner from a second laboratory catches more errors but opens the gate less often: for one released model, a same-laboratory partner catches 2 of 29 errors and a partner from another laboratory 20 of 29 (-62.1 points [-79.3, -43.8]), with the gate open on 96% and 61% of questions. When the wider gate is thinned by stated confidence to equal coverage, this advantage holds only where the partner's model is far more accurate. A small model fed measured accuracies predicts out-of-sample precision with mean absolute error 0.037 against 0.099 for the best single model, level with an independent-errors baseline at 0.032, and runs high on product facts and public data. On the same 100 questions the identifier gpt-5.6-sol scores 92.0% in its client-served wave and 46.3% through the API at another effort setting, while eight other models move by at most 4 points. SSRN: https://ssrn.com/abstract=7682960. Data and code: https://doi.org/10.5281/zenodo.24090584. Full text also at https://polyhelper.ai/research/agreement-inversion/ and https://github.com/vadimchernets/papers/tree/main/agreement-inversion.