DeepReinforce has launched Ornith-1.0, an open-source mannequin household constructed for agentic coding. The lineup spans 4 sizes, from a 9B dense mannequin to a 397B mixture-of-experts flagship. Each checkpoint ships beneath the MIT license on Hugging Face. The fashions are post-trained on prime of pretrained Gemma 4 and Qwen 3.5.
Most coding brokers pair a mannequin with a set, human-designed harness. Ornith-1.0 as a substitute learns to put in writing its personal. The DeepReinforce analysis staff experiences state-of-the-art outcomes amongst open fashions of comparable measurement.
TL;DR
- Ornith-1.0 ships in 9B, 31B, 35B-MoE, and 397B-MoE sizes beneath MIT, constructed on Gemma 4 and Qwen 3.5.
- The mannequin learns its personal scaffold throughout RL, collectively optimizing the harness and the answer.
- Ornith-1.0-397B tops Claude Opus 4.7 on each headline benchmarks, however not Opus 4.8 or the bigger GLM-5.2-744B.
- Three layers — fastened belief boundary, deterministic monitor, frozen LLM choose — guard in opposition to reward hacking.
What’s Ornith-1.0?
Ornith-1.0 is a set of reasoning fashions tuned for coding brokers. The variants are 9B Dense, 31B Dense, 35B MoE, and 397B MoE. The 35B mannequin is mixture-of-experts and prompts roughly 3B parameters per token. FP8 and GGUF builds are additionally printed for sooner native serving.
Every mannequin is a reasoning mannequin. Replies open with a reasoning_content discipline. The fashions additionally emit well-formed instrument requires agent loops.
Deployment is easy. The 9B mannequin is about 19GB in bf16 and serves on a single 80GB GPU. Serving recipes goal vLLM, SGLang, and Transformers. Every mannequin exposes an OpenAI-compatible endpoint. Commonplace agent frameworks due to this fact work with out code modifications.
Interactive Explainer
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The Self-Scaffolding Thought
Most coding brokers depend on a scaffold, additionally known as a harness. A scaffold wraps the mannequin with reminiscence, instruments, error dealing with, and orchestration logic. AI groups often hand-design one scaffold per process class.
Ornith-1.0 treats the scaffold as a learnable object as a substitute. Throughout reinforcement studying, the scaffold co-evolves with the mannequin’s coverage. Every RL step runs in two phases.
First, the mannequin reads the duty and its earlier scaffold. It then proposes a refined scaffold. Second, it makes use of that scaffold and the duty to generate an answer rollout. Reward from the rollout flows again to each phases.
So the mannequin is optimized to creator orchestration, not simply solutions. Over coaching, higher-reward scaffolds are mutated and chosen mechanically. Per-task methods emerge with out hand-engineered harness design.
Coaching additionally runs asynchronously, utilizing a pipeline-RL setup. A staleness weight downweights older, off-policy tokens and drops them previous a threshold. The optimization makes use of a token-level GRPO goal.
Guarding Towards Reward Hacking
Letting a mannequin write its personal scaffold invitations reward hacking. A scaffold may learn seen take a look at recordsdata and hardcode anticipated outputs. It may additionally copy an oracle answer sitting within the surroundings. DeepReinforce staff describes three protection layers.
- The outer belief boundary is fastened and immutable. The surroundings, instrument floor, and take a look at isolation keep exterior the mannequin’s attain. The mannequin evolves solely its internal coverage scaffold.
- A deterministic monitor flags banned actions. Studying withheld paths or enhancing verification scripts earns zero reward. These trajectories are excluded from the benefit computation.
- A frozen LLM choose acts as a veto. It sits on prime of the verifier, not as the first reward.
Benchmark
DeepReinforce experiences vendor numbers throughout a number of agentic coding benchmarks. At flagship scale, Ornith-1.0-397B posts 77.5 on Terminal-Bench 2.1 and 82.4 on SWE-Bench Verified. On SWE-Bench Verified, that 82.4 trails solely Claude Opus 4.8 (87.6) among the many listed fashions. On Terminal-Bench 2.1, the image is extra blended.
Ornith-1.0-397B beats Claude Opus 4.7 (70.3) on Terminal-Bench 2.1. However it trails Claude Opus 4.8 (85) and the bigger GLM-5.2-744B (81.0). So the ‘state-of-the-art’ declare is scoped to open fashions of comparable measurement.
The smaller fashions carry the effectivity case. The 35B mannequin scores 64.2 on Terminal-Bench 2.1, above Qwen 3.5-397B’s 53.5. The 9B mannequin reaches 43.1 on Terminal-Bench 2.1 and 69.4 on SWE-Bench Verified.
| Benchmark | Ornith-1.0-397B | Qwen3.5-397B | Qwen3.7-Max | GLM-5.2-744B | Minimax-M3-428B | DeepSeek-V4-Professional-1.6T | Claude Opus 4.7 | Claude Opus 4.8 |
|---|---|---|---|---|---|---|---|---|
| Terminal-Bench 2.1 | 77.5 | 53.5 | 73.5 | 81.0 | 64 | 64 | 70.3 | 85 |
| SWE-Bench Verified | 82.4 | 76.4 | 80.4 | – | – | 80.6 | 80.8 | 87.6 |
| SWE-Bench Professional | 62.2 | 51.6 | 60.6 | 62.1 | 59 | 55.4 | 64.3 | 69.2 |
| SWE-Bench Multilingual | 78.9 | 69.3 | 78.3 | – | – | 76.2 | – | – |
| NL2Repo | 48.2 | 36.8 | 47.2 | 48.9 | 42.1 | – | – | 69.7 |
| ClawEval Avg | 77.1 | 70.7 | 65.2 | – | – | 75.8 | 78.2 | – |
Use Instances and a Fast Begin
The fashions goal terminal-native coding brokers and repository-scale work. Sensible matches embody multi-file refactors, bug localization, and test-driven patches. The 9B mannequin fits edge or single-GPU setups the place latency and price matter. The 397B mannequin targets most accuracy on lengthy, multi-step duties.
For instance, a dev can run the 9B mannequin regionally to triage a failing take a look at suite. A platform staff can self-host the 397B mannequin for an inside coding agent.
Serving is a one-liner with vLLM:
vllm serve deepreinforce-ai/Ornith-1.0-9B
--served-model-name Ornith-1.0-9B
--max-model-len 262144
--enable-auto-tool-choice --tool-call-parser qwen3_xml
--reasoning-parser qwen3
--trust-remote-codeThen name it with any OpenAI consumer:
from openai import OpenAI
consumer = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
resp = consumer.chat.completions.create(
mannequin="Ornith-1.0-9B",
messages=[{"role": "user", "content": "Write a Python is_prime(n)."}],
temperature=0.6, top_p=0.95,
)
msg = resp.decisions[0].message
print(getattr(msg, "reasoning_content", None)) # the hint
print(msg.content material) # the ultimate reply The reasoning hint returns in reasoning_content, with the reply in content material. Really useful sampling is temperature=0.6, top_p=0.95, top_k=20. The mannequin additionally plugs into OpenHands, OpenClaw, and OpenCode.
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