Richard Young · DeepNeuro (abliterated build of empero-ai's Qwythos, Qwen3.5 base)

Qwythos 9B

A Claude-style creative & reasoning 9B with a full 1M-token context — the local writer & thinker.

Context1,048,576 tokens (YaRN, Qwen3.5-9B base)
PricingFree · runs locally
Tasks tested0
Avg scorecurrently unranked
Medals🥇0 🥈0 🥉0
Release2026-06
Official vendor source
Qwythos 9B is built by Richard Young · DeepNeuro (abliterated build of empero-ai's Qwythos, Qwen3.5 base) — see the vendor's own product page, pricing, and docs at ollama.com/richardyoung/qwythos-9b-abliterated.
Visit ollama.com/richardyoung/qwythos-9b-abliterated →

What is Qwythos 9B?

Qwythos 9B is the Richard Young · DeepNeuro (abliterated build of empero-ai's Qwythos, Qwen3.5 base) frontier model with a 1,048,576 tokens (YaRN, Qwen3.5-9B base) context window, released 2026-06. Tagline: A Claude-style creative & reasoning 9B with a full 1M-token context — the local writer & thinker.. Official source: ollama.com/richardyoung/qwythos-9b-abliterated.

Pricing detail. Qwythos (model ID: richardyoung/qwythos-9b-abliterated) is an abliterated build of empero-ai's Qwythos-9B-Claude-Mythos — a Claude-style creative & reasoning model on a Qwen3.5-9B base, post-trained on Claude Mythos & Fable traces, with a 1M-token context. Thinking model (<think>), native function-calling. Refusals trimmed via the Heretic library (53/100, KL 0.0066). Q4_K_M is 5.6GB, runs 100% offline.

How I use it inside the Agent OS. Loaded in Ollama alongside Ornith as the long-context local option. Bench results pending.

What I built with Qwythos 9B

Every model on Goldie Bench gets the same fixed prompt set — one shot, single HTML file out — and I score the result 0–10 inside the Agent Operating System. Here's what Qwythos 9B shipped on the bench: 0 one-shot demos across 1,048,576 tokens (YaRN, Qwen3.5-9B base) of context. Of those, 0 are scored against the field with my honest 0–10 from the source guides at agentos.guide.

Strengths

  • Full 1M-token context locally — matches frontier-cloud models on context length, at $0
  • Fast — ~3 tokens for 'pong' in 38ms during the smoke test
  • Apache-2.0 license, runs entirely on-device via Ollama

Trade-offs

  • 9B ceiling — won't match frontier 100B+ on complex one-shot ambitious builds
  • Quality varies vs Ornith on the same prompts; bench scoring will land the honest number

Best for

  • Long-context local tasks (large file refactors, multi-file analysis) where 1M ctx matters
  • Cost-zero daily coding work on a consumer Mac
  • Workflows where you want an unfiltered local coder without API guardrails

Every demo by Qwythos 9B

0 live demos, sorted by category. Click any tile to play the actual one-shot result. Verdicts and 0–10 scores are pulled from the source guides where I posted them publicly.

every demo, in a grid · click any one to play
The same stack Julian uses

Run this stack yourself.

Every demo on this bench was built inside the Agent Operating System — one prompt, one shot, single HTML file out. The Agent OS, the prompts, the templates, the weekly walkthroughs and 3,600+ founders shipping with it every day all live inside the AI Profit Boardroom.

3,600+founders
258documented wins
38countries
$59/momonthly