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Ornith-1.0 is an open-source family of agentic-coding LLMs released under the MIT…

2026-06-26 · 12:44 UTC ·@BrianRoemmele ·1 min read

Brief

Ornith-1.0 is an open-source family of agentic-coding LLMs (9B, 31B, 35B MoE, 397B MoE) released under the MIT license. Post-trained on gemma4 and qwen3.5, it uses a novel RL self-improving scaffold+rollout training and reports strong coding-benchmark scores (e.g., Terminal-Bench 77.5); Brian Roemmele praised it on 2026-06-26.

Why it matters

Ornith-1.0 is an open-source family of agentic-coding LLMs released under the MIT license with parameter sizes 9B Dense, 31B Dense, 35B MoE, and 397B MoE (announcement 2026-06-26).

Key details

  • Reported benchmark results: 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.
  • Technical provenance and method: Ornith-1.0 was post-trained on gemma4 and qwen3.5 and uses a novel RL self-improving training that jointly optimizes task-specific scaffolds and solution rollouts for higher-quality agentic coding; author Brian Roemmele tweeted it is “absolutely amazing” and “the last nail in the Anthropic Coffin.”
Source evidence

We are testing US open source Ornith 1.0 and it is absolutely amazing.

The last nail in the Anthropic Coffin!

Good work folks!

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