Ornith 1.5 Brings a Lean 35B MoE to Open Weights

Ornith AI has published Ornith-1.5-35B-A3B, a mixture-of-experts language model that leans on sparsity to keep inference costs down. While the model holds 35 billion parameters in total, only about 3 billion are active for any given token, according to its Hugging Face repository.

The release is built on the Qwen3.5 MoE architecture and targets both general text generation and reasoning. It ships under an MIT license, one of the more permissive options for commercial and research use, which lowers the barrier for developers who want to fine-tune or deploy it without restrictive terms.

Why it matters

The appeal of a 35B/3B-active setup is efficiency: MoE models can offer the knowledge capacity of a larger network while running at the speed and memory footprint closer to a much smaller dense model. That tradeoff has become a popular recipe for teams building on top of Qwen's architecture.

• Total parameters: 35B, with roughly 3B active per token
• Architecture: Qwen3.5 MoE
• License: MIT
• Focus: text generation and reasoning

As a smaller, incremental release, Ornith 1.5 is less about headline benchmarks and more about giving the open-weights community another permissively licensed MoE to experiment with. Details such as context length and evaluation results were not specified in the release record, so prospective users will want to check the repository directly before building against it.