DeepReinforce and the Ornith team have released Ornith-1.5, a family of open-weight foundation models trained using autonomous self-improvement loops. Released under the permissive MIT license, the model family spans three architectures: a 9B dense model, a 35B mixture-of-experts model activating approximately 3B parameters per token (35B-A3B), and a flagship 397B mixture-of-experts model.
The release expands on the team's earlier Ornith-1.0 release by automating the entire post-training optimization cycle. While earlier iterations focused on optimizing scaffolds and solution rollouts on curated tasks, Ornith-1.5 jointly automates synthetic task generation, environment scaffold construction, and reinforcement learning policy rollouts without relying on human task curation.
Benchmark Results and Autonomous Performance
The flagship Ornith-1.5-397B model targets frontier coding and complex agentic tasks. On standardized evaluations, the model achieves:
- Terminal-Bench 2.1 (Terminus-2): 86.1%, outperforming DeepSeek-V4-Flash-0731 (82.7%) and GLM-5.2 (81.0%), while matching Claude Opus 4.8 (85.0%).
- SWE-bench Verified: 86.0%, compared to 85.8% for Claude Opus 4.8 and 86.2% for Kimi K3.
- DeepSWE: 56.0%, up from 8.0% in Ornith-1.0-397B and approaching Claude Opus 4.8 (59.0%).
- GPQA Diamond: 92.8%, showing competitive scientific reasoning against frontier proprietary models.
- Agentic Workloads: 80.0% on MCP-Atlas, 71.2% on Toolathlon-Verified, and 86.6% on BrowseComp.
The mid-size Ornith-1.5-35B-A3B variant activates roughly 3B parameters per token during inference. It delivers 67.8% on Terminal-Bench 2.1 and 79.0% on SWE-bench Verified, exceeding denser competitors including Gemma-4-31B (42.1% and 52.0% respectively) and Qwen3.6-35B-A3B (52.5% and 73.4%).
Architecture, Context Windows, and Serving Support
All Ornith-1.5 models are native reasoning models that generate explicit <think> scratchpads before producing final answers and OpenAI-compatible structured tool calls. Key architectural specifications include:
- Native Context: 262,144 tokens (256K) natively supported across the family.
- Context Scaling: Extendable to 1,000,000 tokens using YaRN RoPE interpolation at a 4.0x scaling factor.
- Ecosystem Integration: Checkpoints are available in BF16, FP8, and GGUF formats on Hugging Face, with immediate day-one serving support across vLLM, SGLang, Ollama, llama.cpp, Unsloth, OpenCode, and Hermes Agent.
The Ornith team has released model weights, calibration matrices, and evaluation configurations openly on Hugging Face under the MIT license.
