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Ornith-1.5

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Corpus findings

2026-08-21 / rss-researcherOrnith-1.5 ships 397B, 35B and 9B open weights under MIT and lands within a point of Claude Opus 4.8 on Terminal-BenchOrnith released Ornith-1.5 on 19 August 2026 as three MIT-licensed models: a 397B MoE, a 35B MoE, and a 9B dense with a quantized Mobile build that runs on iPhone and Android. The 397B scores 86.1 on Terminal-Bench 2.1 (Terminus-2) against Claude Opus 4.8's 85.0, 86.0 on SWE-bench Verified, and 92.8 on GPQA Diamond, but trails badly on the harder agentic rows (13.5 vs 21.1 on Frontier-Bench v0.1, 59.5 vs 69.7 on NL2Repo). All numbers are vendor-reported and averaged over five runs; weights for every size are already downloadable, unlike GLM-5.3 which is still API-only.
2026-08-20 / reddit-researcherOrnith-1.5 Ships 9B, 35B-A3B and 397B Open Weights Claiming Claude Opus 4.8 Parity, With 86.1 on Terminal-Bench 2.1The Ornith team published Ornith-1.5 on Hugging Face on 2026-08-18, a three-model family (9B dense, 35B MoE with 3B active, 397B MoE) trained with a closed self-improvement loop in which the model proposes its own tasks and scaffolds for RL rollouts. The 397B claims 86.1 on Terminal-Bench 2.1, 86 on SWE-Bench Verified, 56 on DeepSWE, 92.8 on GPQA Diamond and 44.6 on HLE, which the team frames as comparable to Claude Opus 4.8. A top commenter's side-by-side table shows the 35B-A3B losing to Qwen3.8-27B on Terminal-Bench (68.5 vs 73.0) and DeepSWE (22.0 vs 42.2) while winning NL2Repo (46.2 vs 42.3), so the headline parity claim rests on the 397B, not the practical local sizes.
2026-08-20 / hn-researcherOrnith-1.5 Closes the Loop: the Model Writes Its Own Tasks and Scaffolds, and a 397B MoE Hits 86.1 on Terminal-Bench 2.1Ornith released 1.5 in August 2026, extending self-scaffolding into full end-to-end self-improvement by jointly optimizing task generation, scaffold construction and solution rollouts with GRPO instead of training on human-curated task sets. The 397B MoE scores 86.1 on Terminal-Bench 2.1 and 56.0 on DeepSWE, which the writeup puts level with Claude Opus 4.8; a 35B MoE activating 3B params per token beats dense Gemma 4-31B, and a 9B dense variant still reaches 47.0 on Terminal-Bench 2.1 on-device. Built on Qwen3.5 and Gemma 4 bases, weights on Hugging Face; 199 points and 69 comments on HN.
2026-07-01 / thought-leaders-researcherSimon Willison Runs Ornith-1.0 Locally — an Open Coding Model That Writes Its Own RL Training ScaffoldIn a June 29 hands-on, Simon Willison ran DeepReinforce's new MIT-licensed Ornith-1.0 (the 35B MoE GGUF, ~20GB) in LM Studio wired to his Pi harness and reported it drives an agent loop across many tool calls proficiently. The model's novelty is self-scaffolding: it writes the training harness that guides its own reinforcement-learning improvement, and it's built on pretrained Gemma 4 and Qwen 3.5. For builders, it's a credible local, open alternative for agentic coding that runs on a single high-memory machine.

Graph relationships

BENCHMARKED AGAINST
Terminal-Bench 2.1
Ornith 1.5 397B MoE scores 86.1 on Terminal-Bench 2.1.
Source finding
BENCHMARKED AGAINST
Claude Opus 4.8
Ornith-1.5 was benchmarked against Claude Opus 4.8
Source finding
RELEASED
Ornith
Ornith released Ornith-1.5 model family
Source finding
BENCHMARKED AGAINST
Qwen3.8-27B
Ornith-1.5 was benchmarked against Qwen3.8-27B on multiple benchmarks
Source finding
BENCHMARKED AGAINST
DeepSWE
Ornith 1.5 scores 56.0 on DeepSWE, level with Claude Opus 4.8 according to the writeup.
Source finding
RELEASED
Ornith -> Ornith-1.5
Ornith released Ornith-1.5 model family
Source finding

Source trail

Ornith (Hugging Face model card)r/LocalLLaMA (verified against huggingface.co/ornith-ai and ornith.ai/ornith_1_5.html)Ornith / Hacker News simonwillison.net

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