Ornith-1.0
DeepReinforce · Released Jun 2026
A strong MIT-licensed open-source agentic coding model family that achieves frontier-competitive benchmarks — beating Claude Opus 4.7 on both Terminal-Bench 2.1 and SWE-Bench Verified — but requires self-hosting with significant GPU resources and comes from a new lab with limited track record.
Is it right for you?
Good for
- Agentic coding with multi-step tool calling — outperforms Claude Opus 4.7 on Terminal-Bench 2.1 (77.5 vs 70.3) and SWE-Bench Verified (82.4 vs 80.8)
- Self-hosted coding agents with full data privacy — MIT license, no API dependency, runs on your own infrastructure
- Edge deployment with the 9B variant — matches or exceeds Gemma 4-31B on SWE-Bench Verified (69.4 vs 52) while fitting on a single consumer GPU
- Self-scaffolding RL training produces per-task strategies that outperform fixed human-designed harnesses on agentic coding benchmarks
Not good for
- Teams without GPU infrastructure — no managed API from DeepReinforce; you must provision and maintain your own inference servers
- General-purpose chat or non-coding tasks — heavily specialized for agentic coding harnesses; not benchmarked or tuned for conversation, writing, or multimodal use
- Production deployment with reliability SLAs — from a new lab with no managed service, limited community adoption data, and unverified benchmark claims
How it performs by task
Agentic coding (multi-step tool use)
SOTA among open-source models; 397B beats Claude Opus 4.7 on both TB-2.1 and SWE-Bench Verified. Simon Willison independently verified proficient multi-tool-call behavior.
Code generation
Strong on SWE-Bench Verified (82.4) and NL2Repo (48.2). Competitive with frontier closed models but trails Claude Opus 4.8 and GLM-5.2 on raw generation benchmarks.
Code debugging and bug fixing
Solid SWE-Bench Pro score (62.2) and strong SWE Atlas results. Handles multi-file bug localization and test-driven patches well.
Multi-file refactoring
Designed for repository-scale tasks. 256K context window enables working across large codebases. Strong on SWE-Bench Multilingual (78.9).
Local/edge inference
The 9B variant (69.4 on SWE-Bench Verified) punches far above its weight class, matching 31B-class models. GGUF quants available for llama.cpp and Ollama.
Pricing
Input
Free (MIT license)
Output
Free (MIT license)
Context
256K tokens
Benchmarks
No verdict changes yet
The clock starts day one — changes land here as our verdict evolves.
Sources
- Simon Willison — Ornith-1.0: Self-Scaffolding LLMs for Agentic CodingJun 2026
- DeepReinforce Official AnnouncementJun 2026
- Hugging Face — Ornith-1.0-397B Model CardJun 2026
- Hugging Face — Ornith-1.0-9B Model CardJun 2026
- GitHub — Ornith-1 RepositoryJun 2026
- MarkTechPost — DeepReinforce Releases Ornith-1.0Jun 2026
- TechTimes — Open-Source Coding Model Ornith-1.0 Writes Its Own Training ScaffoldJun 2026
- LLM Reference — Ornith 1.0Jun 2026
Verification log
- Pricing— No changes
Automated agent
- Pricing— No changes
Automated agent
- Pricing— No changes
Automated agent
- Profile— No changes
Imported at launch
- Pricing— No changes
Imported at launch