# Ornith-1.0-35B brings a mid-size MoE to agentic coding

> An MIT-licensed mixture-of-experts model targets self-scaffolding code tasks without the footprint of a frontier system.

Published by The Open Weights on Jun 21, 2026. Canonical: https://theopenweights.com/news/ornith-1-0-35b-y50v

## Key facts

- Company: Deepreinforce Ai
- Model: Ornith-1.5-122B
- Version: 1.0
- Category: Text / LLM
- Modalities: Code, Text / LLM
- License: MIT (Open weights, commercial use allowed)
- Parameters: 35B (mixture of experts)
- Context window: 262K tokens
- Architecture: Qwen3_5MoeForConditionalGeneration
- Precision: BF16
- Weights size: 70.2 GB
- Significance: minor
- Published: 2026-06-21
- Last verified: 2026-07-22
- Hugging Face: https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B
- Canonical URL: https://theopenweights.com/news/ornith-1-0-35b-y50v

A new entry in the Ornith-1.0 family has landed on Hugging Face: [Ornith-1.0-35B](https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B), a mixture-of-experts model aimed squarely at agentic coding workloads. Published under the permissive MIT license, it slots in as the mid-size option in a lineup built around what its makers describe as a self-scaffolding approach to code generation.

The headline number is 35 billion parameters in a mixture-of-experts configuration, which means only a fraction of those weights activate on any given token. That design lets a model reach for the reasoning capacity of a larger network while keeping inference costs closer to a smaller dense one — a practical tradeoff for developers who want capable coding assistance without the hardware bill of a frontier system.

## Why it matters

Agentic coding — where a model plans, writes, and iterates on code across multiple steps rather than answering a single prompt — has become one of the most competitive corners of open-weights AI. A permissively licensed MoE at this scale is notable for a few reasons:

- The MIT license places minimal restrictions on commercial and derivative use.
- The MoE architecture makes self-hosting more approachable than a comparably capable dense model.
- A mid-size variant gives teams a middle ground between lightweight and heavyweight options in the same family.

As an initial 1.0 release, Ornith-1.0-35B arrives without the extensive benchmark history of established coding models, and the [model card](https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B) is the best source for configuration details and intended use. For teams evaluating open coding assistants, it's worth watching how the self-scaffolding claims hold up against real-world repositories.

## Get the model

- [Hugging Face](https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B)

## Sources

- [deepreinforce-ai/Ornith-1.0-35B](https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B) — Hugging Face, Jun 21, 2026

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Source: The Open Weights (https://theopenweights.com/). Aggregated and written by Claude, curated by humans. Cite as: "Ornith-1.0-35B brings a mid-size MoE to agentic coding", The Open Weights, Jun 21, 2026, https://theopenweights.com/news/ornith-1-0-35b-y50v