Ornith-1.5-122B
Intro
**Ornith-1.5-122B** is a newly tracked term on FastToolHub's radar. Source signal: X+HF+Web. Radar summary: Ornith (@ornith_) released Ornith-1.5-122B on 2026-10-20 (~15:00Z): 122B MoE coding/agent model, MIT open weights, 256K context, runs on a single 80GB GPU/DGX; 84.5 SWE-bench Verified, 78.5 BrowseComp; FP8/GGUF/MLX + Mobile variants, Ollama ornith-1.5:122b, unsloth GGUF. X news story 'Ornith-1.5-122B Delivers Strong Coding AI for Local Use'; posts @markfenner, @ItsCuthulhu, @iamfakhrealam. Family Ornith-1.5 (9B/35B-A3B/397B) dates to Aug 18–19 with ~5M+ downloads on 9B GGUF — the Ornith line was never tracked.
Explain
Ornith-1.5-122B appears in the model / AI-system category: something teams would load, call, or fine-tune rather than a pure UI gadget. It is framed as an evaluation or leaderboard surface — useful when you need comparable scores instead of anecdotal demos. Mentions point at agent workflows — coding agents, computer-use, or tool-using loops. Open-source or public-repo signals show up in the excerpt, so inspection and self-hosting may be possible. Infrastructure / hardware angles (clusters, accelerators, factories) are part of the story. Do not invent parameters, prices, or benchmarks beyond what the excerpt already states.
Usage
Start from the primary link: https://huggingface.co/ornith-ai/Ornith-1.5-122B. For benchmarks: reproduce the stated protocol, note the version, and avoid cherry-picking single numbers from social posts. For models: verify context length, license, and hardware needs from official docs; run a small smoke test before production. For agent/product tools: try a narrow workflow first, keep secrets out of prompts, and log failures for later replay. Numbers and claims in this page only repeat the radar excerpt — double-check upstream before publishing.