Ornith-1.0 is an open-source family of LLMs announced 2026-06-25 by @ornith_…

Brief

Ornith-1.0 is a family of MIT-licensed, open-source LLMs for agentic coding (9B, 31B, 35B MoE, 397B MoE) released 2026-06-25 by @ornith_. Post-trained on gemma4 and qwen3.5, it uses reinforcement learning to generate and jointly optimize task scaffolds and solution rollouts, claiming SOTA open-source results across multiple coding benchmarks.

Key details

Ornith-1.0 claims state-of-the-art open-source coding performance on benchmarks: 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), and ClawEval (77.1).

Models were post-trained on gemma4 and qwen3.5 and use a novel self-improving RL training strategy that jointly generates and optimizes task-specific scaffolds and solution rollouts to improve agentic coding quality.

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…

Brief

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