Ornith is a family of open-source models specialized for agentic coding.
Ornith achieves SOTA performance among open-source models of comparable size on a variety of coding benchmarks (Terminal-Bench 2.1, SWE, NL2Repo, OpenClaw, SWE Atlas, etc)
This is a rewrite of the Claw Code project aimed at significantly reducing token usage. In our initial experience, we observed up to 74% token savings, with around 30% on average, without sacrificing quality.
It was adapted from ultraworkers/claw-code.
It is still an early project and under active iteration.
The optimization work was achieved through a combination of Codex, Grok, and the IterX code optimization tool. Together, they were used to iterate on prompt reduction, tool-surface minimization, replay compaction, and measurement-driven refinement across the harness.
GrandCode is the first AI system that consistently beats all human participants in live contests of competitive programming: in the most recent three Codeforces live competitions, i.e., Round 1087 (Mar 21, 2026), Round 1088 (Mar 28, 2026), and Round 1089 (Mar 29, 2026), GrandCode placed first in all of them, beating all human participants, including legendary grandmasters. GrandCode shows that AI systems have reached a point where they surpass the strongest human programmers on the most competitive coding tasks.
IterX is the optimization engine designed for high-performance computing environments and beyond. With their latest update, user can now effortlessly integrate IterX with local code agents to drive superior system efficiency.
This capability helps user optimize complex code spanning Infrastructure, CUDA, Smart Contracts, databases, and AI/ML Ops. Do not need to code manually; instead, user simply utilize agents like Claude Code or Cursor to define reward signals and handle the integration logic. Performance just became automatic.
Ornith achieves SOTA performance among open-source models of comparable size on a variety of coding benchmarks (Terminal-Bench 2.1, SWE, NL2Repo, OpenClaw, SWE Atlas, etc)
details: https://deep-reinforce.com/ornith_1_0.html