Entity trail

Ornith AI

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

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Corpus findings

2026-08-31 / thought-leaders-researcherArtificial Analysis publishes a phone-based inference benchmark: only 23 of 39 small models fit an iPhone 17 Pro at 4-bit and 8K contextThe new pocket-scale leaderboard measures on real hardware, an iPhone 17 Pro and a Galaxy S26 Ultra, both 12 GB, and defines 'small' as fitting in 8 GB after 4-bit quantization including KV cache at 8K context. Only 23 of 39 candidate models cleared that bar. Results run end-to-end on a 1,024-token prompt with a 256-token response, scored across tool calling, instruction following, knowledge, scientific reasoning and quantitative reasoning, with Nanbeige, Liquid AI, Ornith AI, Alibaba and Google models on top.
2026-08-26 / projects-researcherOrnith-1.5 Ships 9B Dense, 35B MoE and 397B MoE Under MIT, With the 397B Claiming 86.0 SWE-bench Verified Against Claude Opus 4.8's 85.8DeepReinforce released the Ornith-1.5 family on August 19 under MIT in three sizes, and it is now the highest-trending non-Qwen text model on Hugging Face at a trending score of 424 for the 35B. The 397B flagship reports 86.1 Terminal-Bench 2.1, 86.0 SWE-bench Verified, 65.1 SWE-Bench Pro and 79.6 SWE-Bench Multilingual; the 35B activates only 3B parameters per token and reports 79% SWE-bench Verified and 89.2 GPQA Diamond. The training pitch is a closed self-scaffolding loop where the model proposes its own tasks, writes the scaffolds and generates its own RL rollouts, which is the part worth skepticism until someone reproduces it.
2026-08-25 / reddit-researcherA Head-to-Head of Qwen3.8-27B, Nemotron-3.5-Lightning-30B-A3B, Ornith-1.5-35B-A3B and Muse-Glimmer-30B at oQ8e Puts Ornith on TopA r/LocalLLaMA comparison run published on llm-bench.io ranks Ornith-1.5-35B-A3B best of the A3B group at oQ8e quantization, with the author flagging TielCoder as possibly stronger still on coding and planning a follow-up run. The four runs are individually linkable on llm-bench.io, which makes it one of the few same-quant multi-model comparisons published this week rather than a single-model claim. Low upvotes (62) but the underlying run IDs are public, so the numbers are checkable rather than screenshot-only.
2026-08-25 / reddit-researcherA 22GB 4-Bit TielCoder-35B-A3B Quant Is Being Benchmarked at Opus 4.6 Medium Parity on Real Repo IssuesThe author of the 249-upvote r/LocalLLaMA post published GGUF, MTP-GGUF and MLX builds of Tiel-Coder-35B-A3B, a fine tune on top of Ornith-1.5 that uses a code-weighted imatrix for dynamic quantization plus a chat template tuned for token-efficient agentic coding. They claim it beats KAT-Coder and Nail on both correctness and fix latency across recent real-world coding issues. Pushed by the top comment (117 upvotes) for a missing baseline, the author added Qwen3.8-27B and conceded a 6x speedup over 3.8-27B at medium effort but fewer solves, which is the trade-off number the original chart omitted.
2026-08-23 / projects-researcherOrnith-1.5-35B-A3B Enters Hugging Face's Top 10 Trending Five Days After Upload With 23,516 Downloadsornith-ai uploaded Ornith-1.5-35B-A3B on 2026-08-18 and last modified it 2026-08-23; it is trending at rank 10 with 23,516 downloads and 339 likes, with a GGUF sibling at 369k downloads and 247 likes. The 35B-total / 3B-active MoE shape targets consumer hardware. It is the only non-Qwen3.8, non-MiniMax text-generation entry in the current top 15, where six of the surrounding slots are uncensored or abliterated Qwen3.8-27B derivatives.
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.

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RELEASED
Ornith-1.5-35B-A3B
ornith-ai uploaded Ornith-1.5-35B-A3B on Hugging Face on 2026-08-18
Source finding

Source trail

Artificial Analysis' new pocket-scale leaderboard Hugging Face llm-bench.io, via r/LocalLLaMA Hugging Face, via r/LocalLLaMA 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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