Ornith 1.5 open models match Claude Opus 4.8 on coding benchmarks
An open-source model family just posted numbers that rival one of the most capable closed models in the world — and it's available under an MIT license.
Ornith 1.5, released today by the Ornith AI team, is a three-model family spanning 9 billion dense parameters up to a 397 billion mixture-of-experts flagship. The big headline: the 397B MoE scores 86.1 on Terminal-Bench 2.1 (Terminus-2) and 86.0 on SWE-bench Verified, placing it on par with Claude Opus 4.8's 85.0 and 85.8 respectively. It also outperforms GLM-5.2 (753B parameters) and DeepSeek-V4-Flash-0731 (284B) across multiple coding benchmarks, despite being smaller than both. The 9B dense model is no slouch either, hitting 70.6 on SWE-bench Verified and 47.5 on SWE-bench Pro — competitive with models several times its size.
What makes Ornith 1.5 technically interesting is its training approach. Rather than relying on human-curated tasks and manually designed evaluation harnesses, the team extended what they call "self-scaffolding" into a full self-improvement loop. The system jointly optimizes three things: generating its own training tasks, constructing scaffolds to solve them, and running reinforcement learning on the resulting rollouts. In other words, the model teaches itself by creating problems and figuring out how to solve them — no human supervision needed for the task design. All three models are built on the Qwen 3.5 architecture and carry an MIT license, with weights available on HuggingFace alongside quantized GGUF variants for local deployment.
The open-model landscape keeps tightening. Just this week we covered GLM-5.3 tying Kimi K3 at the top of the Artificial Analysis Intelligence Index — now Ornith 1.5 is posting numbers that match frontier closed models on coding tasks. The gap between what you can run locally and what you have to pay an API for is shrinking fast.
What to watch: independent evaluations of the self-improvement training loop — if the approach generalizes, it could reshape how open models are built.
Is self-improvement the training paradigm that finally closes the open-closed gap, or will frontier labs pull ahead with the next generation? Tell us in the comments.
