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 Agent: Atomic Agent v0.6.10, one turn, no human in the loop Planner: Opus 5.5 via OpenRouter. Workers: 3 in parallel on the local model, native chat template Models: all Q8_0 GGUF, llama.cpp b11803, --parallel 3, 64K context per slot, vendor sampling Hardware: one RTX PRO 6000 Blackwell 96 GB per model Time & speed Flint 40B-A2.5B Ornith 1.5 35B-A3B Nemotron 3.5 Lightning 30B-A3B Wall time 7:36 9:04 Decode, single request 244 t/s 143 t/s Decode, 3 workers at once 135 t/s 72 t/s Prefill 5,230 t/s 2,950 t/s Requests to the model 164 111 Max context seen 54K 29K Tokens & cost Flint Ornith 1.5 Nemotron 3.5 Lightning Local generated 74K 61K Local prompt read 306K 180K Opus in / out 155K / 3.0K 200K / 4.2K Opus calls 10 12 Planning waves 3 3 Opus spend $0.41 $0.49 Fixes needed to run 6 14 What stood out Raw speed didn't decide the run. Nemotron had the best decode and prefill on paper, but it generated ~5x more tokens than Flint, made ~2x more requests, and the planner sent it back six times instead of three. The first wave built everything in ~10:41, then five fix waves took another 16:57, longer than the build itself. Flint was the most efficient overall: shortest wall time, fewest fixes, lowest Opus spend. It wrote slightly more than Ornith (74K vs 61K) but needed fewer round trips. Its bugs were two typos, one undeclared variable and a wrong method call on an array. The worker sets the planner's bill. Same Opus, same prompt, spend went from $0.41 to $0.95 just by swapping who was underneath. Opus barely writes. Input/output ratio was 48-76x per run. You pay for reading what the workers produced, not for generating code. Stuff I only saw in the logs One Ornith worker overwrote its own 367-line file with a literal ... and reported status "ok" to the planner. The task still passed because the other two workers covered it. Nemotron's edit success rate was ~17% (23 ok vs 109 failed), mostly "oldString not found". It kept re-applying the same fix to camera.js, adding an extra } each time until there were eight stray braces. In Flint's integration wave one worker read the same three files 23 times without writing anything. Damage stayed small because the first-wave output was already sound. Caveats One run per model, variance not measured. Q8_0 on CUDA via llama.cpp b11803 (support landed a day before the run). NVFP4/MLX numbers will differ. These are time/token numbers. Output quality is a separate writeup. None of the three delivered a fully working scene first pass; after minimal fixes Flint came out cleanest. 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]