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@0xSero claims new "best in class" models targeted at three memory tiers

2026-06-25 · 18:06 UTC ·@0xSero ·1 min read

Brief

Ornith-1.0 is a family of open-source LLMs for agentic coding (9B/31B dense and 35B/397B MoE) post-trained on gemma4 and qwen3.5. It uses a novel RL self-improvement that jointly generates task scaffolds and rollouts, reports strong coding benchmark scores (e.g., Terminal-Bench 77.5, SWE-Bench 82.4 verified), and is released under the MIT license.

Why it matters

@0xSero claims new "best in class" models targeted at three memory tiers: 8–16 GB, 16–96 GB, and 256–512 GB and says they need to "improve my GLM-5.2 reap now" (2026-06-25).

Key details

  • Ornith-1.0 is an open-source agentic-coding LLM family (9B dense, 31B dense, 35B MoE, 397B MoE) post-trained on gemma4 and qwen3.5 and released under the MIT license.
  • Ornith-1.0 reports state-of-the-art open-source coding scores: Terminal-Bench 2.1 = 77.5; SWE-Bench = 82.4 (verified), 62.2 (pro), 78.9 (multilingual); NL2Repo = 48.2; SWE Atlas QnA = 41.2, RF = 42.6, TW = 39.1; ClawEval = 77.1. It uses an RL self-improving strategy that jointly optimizes task-specific scaffolds and solution rollouts and is available on Hugging Face and a tech blog.
Source evidence

New best in class models for:

  • 8-16gb memory
  • 16-96gb memory
  • 256-512gb memory

Gotta improve my GLM-5.2 reap now

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