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The tweet compares Qwen 3.8 27B and Ornith-1.5-35B models on a prompt for generating a bioluminescent abyssal temple animation, noting that Qwen performs better visually while Ornith is faster in build speed and completion.
User runs local benchmarks comparing Qwen3.6 27b, Gemma4 26B, and Ornith1.0 35B on an RTX 3090 using inspect-ai. Results show Qwen leading in knowledge and coding, while Ornith is competitive in grounding and recall.
Tweet highlighting top-performing AI models under 128B parameters on the SWE-bench_pro benchmark, noting Alibaba Qwen 3.6 27B and ornith 35B as leading contenders.
Ornith-9B demonstrates that RL training at 9B parameters primarily buys efficiency, achieving same answers with ~56% of the tokens and twice the speed of its base model, offering real cost savings for per-token payment.
Ornith-1.0 is a family of open-source LLMs specialized for agentic coding, available in sizes from 9B to 397B MoE, and can be run via Ollama for use with tools like Claude or Pi.
A side-by-side canvas test compares Qwen 3.5 35B A3B and Ornith 1.0 35B on three paper destruction tasks (slice, shredder, crumple), with Ornith decisively winning, demonstrating the value of post-training on Qwen 3.5 and Gemma 4.
China released an open source coding AI LLM called Ornith, with a 35B version that beats Qwen3.6 and a 397B version that benchmarks near Claude Opus 3.7.