Radar / AI models / Occamy-1.0

Occamy-1.0: open 35B MoE built for long-horizon co-work agents

Accio Lab released Occamy-1.0, a 35B-total / 3B-active MoE built on Qwen3.6-35B-A3B and post-trained for sustained agentic co-work: tool use, terminal coding, state tracking across context compaction. Apache 2.0, posted to r/LocalLLaMA this week (2,648 HF downloads, 235 likes).

new launchmodels · open-weightsadded 2026-10-17
Open Occamy-1.0 →View on the radar

Why it matters

Purpose-built co-work post-training: 82.2 Claw-Eval average vs 69.5 for its base checkpoint, top of its 35B class while sitting at the low-cost knee of the cost-performance Pareto frontier — long-horizon agent work without frontier bills.

What you could build with it

A startup building an AI support-ops product could run multi-hour ticket-resolution workflows on Occamy-1.0 instead of a frontier model, keeping state across tool calls and context-compaction events while cutting per-episode inference spend.

Does it hold up?

Too early to judge — only the model card's own vLLM/llama.cpp compatibility checks and HF Space presence so far; independent runtime reports and GGUF quant quality are still pending.

Built with Occamy-1.0

Learn more

technical deep dive →

First spotted on hf: source.

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