Local MoE coding workers compared: Flint beats Ornith and Nemotron on efficiency

What it's about

In the r/LocalLLaMA community, a developer compared three recently released small MoE models as code workers. MoE (Mixture of Experts) means only a fraction of the model's parameters is active for each token. The models were Flint 40B-A2.5B, Ornith 1.5 35B-A3B and Nemotron 3.5 Lightning 30B-A3B. The number after "A" is the active parameters per token. The author says they work on Atomic Agent, the open-source agent the test ran in.

Test setup

In "Fusion" mode, Claude Opus 5.5 (via OpenRouter) plans and reviews, while three workers running the local model write the code in parallel. Only the worker model changed. Prompt, planner, quantization and settings stayed the same.

• Task: a 20-second Halloween chain reaction in Three.js (flint hits steel, spark lights a fuse, a rocket pushes a marble down a spiral, the marble tips a match into a pumpkin), with a chase camera and a seamless rewind loop; roughly 1,000 to 1,700 lines across 6 to 9 files.
• Agent: Atomic Agent v0.6.10, a single pass, no human in the loop.
• Models: all Q8_0 GGUF (8-bit quantization), llama.cpp b11803, --parallel 3, 64K context per slot, sampling per vendor recommendations.
• Hardware: one RTX PRO 6000 Blackwell with 96 GB each.

Time and speed

Tokens and cost

Observations

Raw speed didn't decide the run. On paper, Nemotron had the best decoding and prefill. But it produced about five times more tokens than Flint, made roughly twice as many requests, and the planner sent it back six times instead of three. The first wave built everything in about 10:41. The five fix waves took another 16:57.

Flint was the most efficient overall: shortest time, fewest fixes, lowest Opus cost. Its bugs were two typos, an undeclared variable and a wrong method call on an array.

The worker drives the planner's bill. With the same Opus and prompt, costs rose from $0.41 to $0.95. Opus barely writes anything itself; the input-to-output ratio was 48–76:1. You pay for reading the workers' results.

Oddities from the logs

• An Ornith worker overwrote its 367-line file with a literal "..." and reported status "ok" to the planner. The task still succeeded because the other two workers covered it.
• Nemotron's edit success rate was about 17% (23 succeeded, 109 failed), mostly "oldString not found". It applied the same fix to camera.js again and again, adding another "}" each time until eight extra braces piled up.
• In Flint's integration wave, one worker read the same three files 23 times without writing. The damage was small.

Context

These results come from a single forum post with clear limits: one run per model, variance not measured; llama.cpp support only landed a day before the run; NVFP4/MLX would give different numbers. They are pure time and token figures, and output quality is covered separately. None of the models produced a fully working scene on the first pass; after minimal fixes, Flint was the cleanest. The author is involved with the tested agent. The core argument is useful: for agentic coding, edit success rate and the number of rounds matter more than tokens per second. General conclusions need multiple runs and other tasks.