Decision model cuts main LLM calls 29.6% and cost 26.6% on 360 SOP-Bench tasks
vivekhaldar · x · 2026-10-13
vivekhaldar explores a "decision model" architecture: many agent steps don't need a full LLM turn. Across 360 SOP-Bench cases, a hosted decision model (Jev) handles step-completion checks and bounded choices — decisions at ≥0.8 confidence are accepted, anything less certain falls back to the main model.
Results on that workload:
- 29.6% fewer main-model calls
- 26.6% lower estimated cost
- 19.7% lower mean latency
- 360/360 tasks correct vs 357/360 for the main model alone
He argues the pattern is useful today — give the small model the evidence and allowed choices, validate a confidence threshold, keep the general-purpose model as fallback — while noting the numbers need replication beyond this dataset.
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