Ornith model releases

The smallest model in DeepReinforce's Ornith-1.5 family (released 2026-08-19, MIT, weights on Hugging Face): a 9B-parameter dense coding/agent model trained with the family's self-improving task-and-scaffold RL loop, and shipped with a quantized 'Ornith-1.5-9B-Mobile' build that runs on iPhone and Android. Vendor-reported, five-run-averaged figures put it at 47.0 on Terminal-Bench 2.1 and 70.6 on SWE-Bench Verified, which DeepReinforce places above larger models including Gemma 4-31B and Qwen3.6-35B-A3B. Figures are self-reported and unverified at launch.

The mid-size model in DeepReinforce's Ornith-1.5 family (released 2026-08-19, MIT, weights on Hugging Face): a 35B-parameter Mixture-of-Experts that activates ~3B parameters per token, trained with the same self-improving task-and-scaffold generation loop as the 397B flagship. Vendor-reported, five-run-averaged figures put it at 68.5 on Terminal-Bench 2.1 and 79.0 on SWE-Bench Verified while activating only 3B parameters per token — which DeepReinforce reports as outperforming dense models of similar or larger size such as Meta's Muse-Glimmer-30B and Gemma 4-31B. Figures are self-reported and unverified at launch.

The flagship of DeepReinforce's Ornith-1.5 family, released 2026-08-19 under the MIT license with weights on Hugging Face. A ~397B-parameter Mixture-of-Experts coding/agent model (per-token active count not disclosed) trained with a self-improving RL loop: rather than fixed human-curated tasks, the system proposes progressively harder tasks itself, generates a task-specific orchestration scaffold for each, and produces the solution rollouts used for reinforcement learning, with reward propagating across all three stages (all optimized with GRPO). Vendor-reported, five-run-averaged figures: Terminal-Bench 2.1 85.1 and DeepSWE 56.0 — which DeepReinforce puts on par with Claude Opus 4.8 (85.0 / 59.0) and ahead of GLM-5.2 and DeepSeek-V4-Flash-0731 at comparable scale — plus 92.8 GPQA Diamond and 86.6 BrowseComp. All numbers are the vendor's own and unverified by an independent harness at launch. Extends the self-scaffolding approach introduced in Ornith-1.0 (June 2026).