Ornith-1.5: Self-Improving Models Match Claude Opus Performance
rohanpaul_ai · x · 2026-08-19
Ornith-1.5 introduces a major step toward foundation models built through end-to-end self-improvement, extending the self-scaffolding framework into a complete loop where the model proposes tasks, generates scaffolds, and produces reinforcement learning solutions.
Model Specs & Performance:
- 397B MoE: Scores 86.1 on Terminal-Bench 2.1 and 56.0 on DeepSWE, matching Claude Opus 4.8 and outperforming GLM-5.2 and DeepSeek-V4-Flash.
- 35B MoE: Activating only 3B parameters per token, it significantly outperforms Gemma 4-31B and Meta Muse Glimmer-30B on agentic coding tasks.
- 9B Dense: A mobile-quantized version runs on phones, substantially outperforming larger models like Gemma 4-31B and Qwen 3.6-35B.
Related event: Ornith-1.5 Release: 397B MoE Self-Improvement Targets Claude Opus(24 posts)→
More from Models
- GLM 5.5 rumored to drop soon, coding ability expected to match frontier models — bindureddy · 2026-10-16
- Five rounds of adversarial prompting make Opus 5.5 and Argon converge on identical output — LanceCharger · 2026-10-16
- DIY benchmark ranks 18 models on subjective judgments, GLM-5.3 tops consensus match — arkuto · 2026-10-16
- Bindu Reddy: Google's Argon excels at complex doc writing at half the cost of Opus — bindureddy · 2026-10-16
- MAI-2 is 2x bigger than V4-Pro while V4.1F is 3x smaller: Microsoft's scaling ladder questioned — teortaxesTex · 2026-10-16
- Step 5 Preview hits DeepInfra day-zero: 600B MoE, 1M context, $1/M input tokens — StepFun_ai · 2026-10-16