Local MoE models show large gaps in agentic coding efficiency
A Reddit author benchmarked Flint 40B-A2.5B, Ornith 1.5 35B-A3B, and Nemotron 3.5 Lightning 30B-A3B as parallel coding workers under Opus 5.5 in Atomic Agent v0.6.10. Flint completed the Three.js task in 7:36 with six fixes and $0.41 in Opus usage, versus 9:04, 14 fixes, and $0.49 for Ornith, and 30:32, 16 fixes, and $0.95 for Nemotron. The results are preliminary because each model ran only once, output quality was not assessed in detail, and the author works on the agent runtime.
The comparison suggests that a worker model’s edit reliability and planning round trips can matter more than raw tokens per second in agentic coding, but the single-run design limits how broadly the findings can be applied.
| Three small MoEs dropped within a couple of weeks, so I put all of them in the same seat. In Fusion mode Opus 5.5 plans and reviews, and three workers on the local model write the code in parallel. Only the worker model changes between runs. Same prompt, planner, quant, settings. The task was one prompt: a 20 s Halloween chain reaction in Three.js (flint strikes steel → spark lights a fuse → fuse fires a rocket → rocket knocks a marble down a spiral → marble tips a match into a jack-o'-lantern), follow camera, seamless rewind loop. About 1,000 to 1,700 lines across 6 to 9 files. Setup Time & speed Tokens & cost What stood out Stuff I only saw in the logs Caveats Has anyone run these as sub-agents yet? Does raw tok/s predict wall clock on agentic coding for you, or is it edit success rate like here? Disclosure: I work on Atomic Agent, the open source agent this ran in so ask me anything! submitted by /u/GapNew4766 [link] [comments] |
#Flint 40B-A2.5B#Ornith 1.5 35B-A3B#Nemotron 3.5 Lightning 30B-A3B#Opus 5.5#Atomic Agent v0.6.10#Three.js#llama.cpp#OpenRouter#RTX PRO 6000 Blackwell 96 GB