Mean absolute gap in percentile positions on shared benchmark settings; lower is closer. At least 60% overlap is required, with a minimum of three when available. Independent evaluations are used when there are at least three; otherwise published report comparisons are used. Repeated model observations within one setting are averaged before ranking. Writing-style traits and composite indices are excluded. This describes similarity, not an overall quality ranking.
POST-TRAINING / SOURCE DISCLOSURE
What the Kimi K3.1 Pro report says
K3.1 Pro starts from Kimi K3 and expands joint reinforcement learning across many downstream domains. The announcement ties the scale-up to long-context inference, sandbox infrastructure, rollout scheduling and environment checks.
Kimi K3 starting checkpoint
Joint reinforcement learning across downstream domains
The announcement does not specify the complete loss, training mixture or per-stage compute. The systems note is background; it does not establish that K3.1 Pro uses DAPO.
Every plotted observation links to its evaluation. Reported configurations, benchmark versions and task subsets can differ; similarity describes shared scores, not training lift.