DeepReinforce

2026DeepReinforceopen source

Ornith-1.0

Ornith-1.0 is a family of self-improving, open-source large language models (LLMs) designed for agentic coding tasks. Ranging from compact 9B Dense models suitable for edge devices to 397B MoE models optimized for maximum performance, Ornith-1.0 achieves state-of-the-art results on various coding benchmarks. Built upon pretrained Gemma 4 and Qwen 3.5, it introduces a self-improving training framework that enables the model to generate both solution rollouts and the task-specific scaffolds guiding those rollouts, leading to higher-quality solutions without relying on human-designed harnesses. This innovative approach allows Ornith-1.0 to outperform leading open-source models of similar size, demonstrating strong agentic coding capabilities even in resource-efficient deployments.

Self-improving training framework

Open-source models ranging from 9B Dense to 397B MoE

State-of-the-art performance on coding benchmarks

pricingFree

version1.0

2026DeepReinforceopen source

Ornith-1.5

v1.5

LLM

Open-Source

Ornith-1.5 is an open-source model family that advances foundation model development through an end-to-end self-improvement loop. The system autonomously proposes tasks, constructs task-specific scaffolds, and generates solution rollouts to continuously refine its reasoning, coding, and agentic capabilities.

End-to-end self-improvement loop for autonomous task and scaffold generation

Multi-scale model family: 397B MoE, 35B MoE, and 9B dense variants

State-of-the-art performance on agentic coding benchmarks like Terminal-Bench 2.1 and SWE-Bench

pricingOpen-weight

version1.5