Same model, same prompt, two agent harnesses: 45/50 vs 43/50

Ran 50 tasks rebuilt from merged PRs through two open source coding agents, both on deepseek-v4-flash. Same model, same system prompt written into both configs by a sync script that fails on drift, same sealed container, no web access for either. Graded by each project's own held-out tests plus a 3-model judge panel. 45/50 against 43/50, judge average 88.6 against 85.6. Cost was a wash, $1.59 against $1.53 for the whole run. Two cases out of fifty is not a margin I'd defend on its own. The more interesting split was wall clock, and it taught me not to trust my own averages. On the mean ours looked 2 minutes a case slower. On the median it's 5.5 against 7.0, and case by case ours is faster in 31 of 50. The entire mean gap came from a single case, a react hydration bug both agents failed, where ours ran 271 minutes and 1322 steps before giving up and the other quit at 63. Our no-progress detection plainly didn't fire. That's a real bug rather than a measurement artifact, and it's the worst single result in the run. Near-identical money also bought very different shapes of work: 1.5M output tokens against 573K, and 1.0M reasoning against 1.3M. Disclosure: ours is octomind, so this is our benchmark and our bias. Repo link in the comments per sub rules. Posting mostly because I haven't seen many same-model harness comparisons, and I'd like to know if anyone has run one on a bench we don't own.