When Does LLM Orchestration Pay Off? A Controlled Evaluation of Accuracy, Cost, and Task Difficulty

2026-08-01

AI Summary

This study investigates whether the additional inference cost of large language model (LLM) orchestration is justified and whether its effectiveness varies with task difficulty. Under a unified evaluation framework that controls for optimization budget and stratifies tasks by difficulty, the authors systematically assess orchestration strategies—including Self-Refine, Best-of-N, and Debate—across programming, mathematics, and chess domains in terms of accuracy–cost trade-offs. Results show that orchestration improves accuracy by up to 4.6 percentage points on average but incurs a 2–4× increase in token consumption. Crucially, the gains depend strongly on the base model’s capability rather than task difficulty, challenging the common assumption that more elaborate reasoning structures inherently yield better performance.

Abstract

LLM orchestration is often assumed to improve reasoning by allocating additional inference-time computation, yet its gains may not justify its cost. Existing comparisons also frequently overlook differences in optimization effort, making it difficult to isolate the value of orchestration itself. We conduct a controlled evaluation of Self-Refine, Best-of-$N$, and Debate against task-only and chain-of-thought (CoT) single-call baselines across five LLM backbones and three domains: competitive programming, chess puzzles, and mathematics. For comparability, we optimize each method with GEPA under the same optimization budget and evaluate all methods on the same difficulty-stratified benchmark items. Orchestration yields moderate but benchmark-dependent gains: averaged across backbones within each benchmark, the largest improvement is 4.6 percentage points over optimized CoT inference and 4.5 points over task-only inference, while requiring approximately 2 to 4 times the mean total tokens of task-only inference. Human-derived difficulty is associated with lower absolute accuracy in all three benchmarks, but within-benchmark analyses do not indicate that orchestration effects increase with task difficulty. By contrast, exploratory mixed-effects analyses reveal strong interactions between orchestration method and backbone model across all three benchmarks, showing that orchestration effectiveness depends substantially on the underlying model. Our results suggest that orchestration decisions should be model-specific and account for whether moderate accuracy gains justify the additional inference cost. More broadly, evaluations of LLM orchestrations should control optimization effort and report model-specific accuracy--cost trade-offs rather than treating additional inference-time structure as uniformly beneficial.

Problem

• LLM orchestration
• accuracy–cost trade-off
• task difficulty
• optimization effort
• model-specific effects

Innovation

• LLM orchestration
• controlled evaluation
• optimization budget
• accuracy-cost trade-off
• model-specific effects