Ornith-1.5 shipped 9B, 35B-A3B and 397B open weights, and the parity claim rests entirely on the 397B.

The 397B MoE scores 86.1 on Terminal-Bench 2.1, 86 on SWE-Bench Verified, 56 on DeepSWE, 92.8 on GPQA Diamond and 44.6 on HLE, which the team frames as comparable to Claude Opus 4.8. The method is a closed self-improvement loop where the model proposes its own tasks and scaffolds for RL rollouts, jointly optimized with GRPO instead of training on human-curated task sets. (Ornith) But a top r/LocalLLaMA commenter's side-by-side shows the 35B-A3B losing to Qwen3.8-27B on Terminal-Bench (68.5 vs 73.0) and DeepSWE (22.0 vs 42.2) while winning NL2Repo (46.2 vs 42.3). So the headline doesn't transfer to the sizes most people can actually run. Worth noting the tension with arXiv 2608.19072 above: self-generated training curricula at the model level, while agents doing model-level strategy selection can't revise their own approach.