Ornith-1.0：用于智能体编程的自我脚手架大语言模型Ornith-1.0: Self-Scaffolding LLMs for Agentic Coding
DeepReinforce 发布了其首个 MIT 许可证的开源模型系列 Ornith-1.0，专注于智能体编程任务。该系列包含 9B Dense、31B Dense、35B MoE 和 397B MoE 等多种参数变体。模型基于预训练的 Gemma 4 和 Qwen 3.5 构建，旨在为代码生成和自动化代理流程提供底层支持。在同等规模的开源模型中，该模型在多项编程基准测试上取得了最先进（SOTA）的性能表现。
Simon Willison
29th June 2026 - Link Blog
Ornith-1.0: Self-Scaffolding LLMs for Agentic Coding. This is an interesting new open weights (MIT licensed) model, the first model release from DeepReinforce.
[...] with variants including 9B Dense, 31B Dense, 35B MoE, and 397B MoE. Built on top of pretrained Gemma 4 and Qwen 3.5, it achieves state-of-the-art performance among open-source models of comparable size on coding benchmarks.
As far as I can tell the licenses of those underlying models is compatible with being used in this way - Gemma 4 is Apache 2.0 licensed (and not bound by the janky additional Gemma Terms of Use that afflicted the previous Gemma models) and Qwen 3.5 is Apache 2.0 licensed as well.
I've been running the model using LM Studio and the ornith-1.0-35b-Q4_K_M.gguf (20GB) GGUF, hooked up to Pi. Initial impressions are very good - it seems to be able to run the agent harness over many tool calls in a proficient way.
Here's a terminal session where I asked it to "find the code that decodes the actor cookie" and then "find the code that opens the insert dialog when thebutton is clicked" against a Datasette checkout, which it handled with ease.
I also had it draw this pelican, which came out at 103 tokens/second:
It's a little bit mangled but the pelican is clearly a pelican.
I couldn't find much information about DeepReinforce themselves. The earliest paper I could find from the was CUDA-L1: Improving CUDA Optimization via Contrastive Reinforcement Learning from June 2025.
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