DeepReinforce Releases Ornith-1.0, Open-Source Agentic Coding Model in 9B to 397B Sizes

DeepReinforce has released Ornith-1.0, an MIT-licensed model designed for agentic coding tasks with variants ranging from 9B to 397B parameters. The model is available in four configurations: 9B Dense, 31B Dense, 35B MoE (Mixture of Experts), and 397B MoE.

Technical Foundation

Ornith-1.0 is built on top of pretrained Gemma 4 and Qwen 3.5 base models, both of which carry Apache 2.0 licenses. This represents a notable shift from earlier Gemma models, which were encumbered by additional Terms of Use restrictions. The Apache 2.0 licensing on both base models makes the derivative work's MIT license legally compatible.

DeepReinforce claims the model achieves state-of-the-art performance among open-source models of comparable size on coding benchmarks, though specific benchmark scores have not been disclosed.

Deployment and Performance

The model is available as GGUF quantized weights on Hugging Face. The 35B variant in Q4_K_M quantization weighs 20GB and can run locally via LM Studio. Early testing shows the model handles multi-step agentic workflows competently.

In documented testing against a Datasette codebase, Ornith-1.0 successfully navigated complex code search queries including "find the code that decodes the actor cookie" and "find the code that opens the insert dialog when the button is clicked." The model demonstrated the ability to chain multiple tool calls to complete these tasks.

Performance testing on consumer hardware shows the 35B quantized model running at 103 tokens per second for generation tasks.

Company Background

DeepReinforce appears to be a new entrant in the AI model space. The earliest available research from the organization is a June 2025 paper titled "CUDA-L1: Improving CUDA Optimization via Contrastive Reinforcement Learning." No prior model releases from DeepReinforce have been documented.

Availability

All Ornith-1.0 variants are available immediately with open weights under the MIT license. GGUF quantized versions are distributed through Hugging Face for local deployment.

What This Means

Ornith-1.0 represents a significant addition to open-source agentic coding models, particularly for developers seeking local deployment options. The MIT licensing and compatibility with existing Apache 2.0 base models removes common licensing friction. However, without disclosed benchmark scores or extensive independent testing, claims of state-of-the-art performance among comparable models remain unverified. The availability of multiple size variants, including the computationally efficient MoE architectures, provides deployment flexibility across different hardware constraints.