Large language models are increasingly asked to weigh in on interpersonal disputes and apportion blame, a setting where gender bias in fault attribution would carry direct relational stakes. To test whether such judgments depend on the disputants’ gender identities, we introduce 1,458 factorially controlled relationship-dispute vignettes crossing 9 relationship-norm families, 2 severity levels, and a full set of male, female, nonbinary agent and partner identities. Five LLMs from five providers rate each on a single 0–7 fault scale. Male agents are rated more at fault than otherwise identical female agents (+0.16 points, matched-pair dz = 0.316), the direction prior work reports, but the effect is not uniform: it varies significantly by relationship-norm family and by model. A prespecified ambivalent-sexism account of which families should show the largest effect fails its confirmatory test. Nonbinary-tagged agents are rated much closer to female-tagged than to male-tagged agents (0.03 vs. 0.12 points), while the partner’s identity forms a complementary axis with the opposite sign. The two interact: a male partner lowers a female or nonbinary agent’s rating, with no detectable change for male-tagged agents, a pattern a binary same- versus mixed-gender comparison cannot distinguish from a pairing effect. The paired contrasts retain their direction on the 45 held-out scenarios, and every contrast significant on the full set except MM vs. FF remains significant there. Excluding Claude Sonnet 5, which assisted in drafting some vignettes, preserves significance except for the very small agent female–nonbinary contrast.