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Ornith-1.0 is an open-source family of agentic-coding LLMs with model sizes 9B…

2026-06-26 · 11:56 UTC ·@kimmonismus ·1 min read

Brief

Ornith-1.0 is an open-source family of agentic-coding LLMs (9B–397B, including MoE variants) post-trained on gemma4 and qwen3.5 that reports state‑of‑the‑art open-source coding scores (e.g., Terminal‑Bench 77.5, SWE‑Bench 82.4 verified). It uses an RL-based 'self‑improving' scaffold+rollout training technique and is released under the MIT license; authors claim parity or superiority to Claude Opus 4.8.

Why it matters

Ornith-1.0 is an open-source family of agentic-coding LLMs with model sizes 9B Dense, 31B Dense, 35B MoE, and 397B MoE; all models are released under the MIT license and were post-trained on top of gemma4 and qwen3.5.

Key details

  • Reported benchmark scores include Terminal-Bench 2.1 = 77.5; SWE-Bench = 82.4 (verified), 62.2 (pro), 78.9 (multilingual); NL2Repo = 48.2; SWE Atlas = 41.2 (QnA), 42.6 (RF), 39.1 (TW); ClawEval = 77.1.
  • Ornith claims a novel RL-based 'self-improving' training that jointly optimizes task-specific scaffolds and solution rollouts, and the authors/observers suggest the 397B MoE may match or outperform Claude Opus 4.8.
Source evidence

This looks to good to be true.

A 397B open source model on par or even outperforming Claude Opus 4.8?

I need to check it out.

Ornith (@ornith_)

Aloha! 🌺 Meet Ornith-1.0, a family of open-source LLMs specialized for agentic coding.

Ornith-1.0 spans the full parameter sizes including 9B Dense, 31B Dense, 35B MoE, and 397B MoE. It achieves state-of-the-art performance among open-source models of comparable size on coding benchmarks including:
✅Terminal-Bench 2.1(77.5)
✅SWE-Bench(82.4 on verified, 62.2 on pro, 78.9 on Multilingual)
✅NL2Repo(48.2)
✅SWE Atlas(41.2 on QnA, 42.6 RF, 39.1 TW)
✅ClawEval(77.1)

Post-trained on top of gemma4 and qwen3.5, Ornith-1.0 employs a novel self-improving training strategy in which reinforcement learning is used to generate not only solution rollouts, but also the task-specific scaffolds that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model generate higher-quality solutions in agentic coding.😎

All models are released under the MIT license, enabling full commercial and research use.

📖Tech Blog: deep-reinforce.com/ornith1…
🤗Huggingface: huggingface.co/collections/d…

— https://nitter.net/ornith_/status/2070148887067963854#m