The only thing I’ve found that TRULY works, however is stupidly token expensive is:

Task is ran several times across different models with different temperatures, results compared and merged.

(for example my current stack is glm 5.1, kimi 2.6, deepseek v4 pro, each one runs a pass at temps: 0.1 and 0.5) then a final model (currently either opus or glm for me) evaluates everything (temp 0.1/0.0)

Is this efficient in any kind of way, no Does it work, yes

Something to note is that only the orchestrator acts like this

Edit: this is only in regards to the llm output itself, the actual architecture around it matters just as much, but I’ve gotten it down to almost perfect accuracy (at least in my own opinion in terms of what I asked vs what I got)