Ornith-1.0: Self-scaffolding LLMs for agentic coding
The article introduces Ornith-1.0, a new family of open-source AI models designed for agentic coding tasks. These models utilize a self-improving training framework to generate both solutions and task-specific scaffolds, achieving performance levels that compete with top-tier proprietary models.
Why it matters
It represents a significant advancement in open-source AI capabilities, potentially lowering the barrier for high-performance coding automation.
Today, we are introducing Ornith-1.0 , a self-improving family of open-source models specially for agentic coding tasks. Ornith-1.0 spans the full spectrum, from compact 9B Dense models suitable for edge device deployment to 397B MoE frontier-scale models optimized for maximum performance, with variants including 9B Dense, 31B Dense, 35B MoE, and 397B MoE . Built on top of pretrained Gemma 4 and Qwen 3.5, it achieves state-of-the-art performance among open-source models of comparable size on coding benchmarks.
The content is a technical announcement regarding software development and AI research.
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