In brief
DeepReinforce released Ornith-1.0 on June 25 under MIT license, purpose-built for AI coding agents working in real terminal and repository environments.
The 9B variant scores 69.4 on SWE-bench Verified, outperforming Google's Gemma 4-31B (52.0).
Ornith's own model card warns the models may underperform on non-coding tasks—they are wired for developer pipelines, not general-purpose AI conversations.
DeepReinforce, an AI research lab previously known for CUDA-L1 and the IterX code-agent optimization loop, released Ornith-1.0 late last week—a family of open-source coding models available on Hugging Face in four sizes based on the number of parameters: 9 billion, 31 billion, 35 billion mixture of experts, and a 397 billion mixture-of-experts flagship, all under MIT license with no regional restrictions.Parameters are basically the number of dials and configurations a model can handle on its training. The more parameters, the more capable a model is. A 9-billion-parameter model is considered small, good enough to run on a good smartphone, but not capable of doing any heavy reasoning task reliably. A 397 billion model is much more capable, but requires some heavy computing, the kind that is not available on consumer hardware.The lab describes it as "a self-improving family of open-source models specially for agentic coding tasks." That word—agentic—is doing a lot of work.
