Tiered Super-Moore's Law: Price Evolution, Production Frontiers, and Market Competition in LLM Inference Services
First systematic empirical analysis of LLM token pricing across 3,237+ models (2020-2026); ~600x price decline; 'Tiered Super-Moore' hypothesis (economy 1.10yr / mid 1.55yr price half-life vs 2yr Moore benchmark; flagship/reasoning resists via ~31.5x premium); cost decline ~103.7% software/architecture-driven, ~-0.9% hardware.
Tiered Super-Moore's Law: Price Evolution, Production Frontiers, and Market Competition in Large Language Model Inference Services
Abstract
"This paper provides the first systematic economic analysis of token pricing in the large language model (LLM) inference market. Assembling a novel dataset integrating OpenRouter API data (318 models), Epoch AI records (3,237 models), and 62 cross-validated milestone observations spanning 2020-2026, we document an approximately 600-fold decline in token prices and propose the 'Tiered Super-Moore' hypothesis."
Key Contributions
- Builds the largest known cross-validated dataset on LLM token pricing: 318 models (OpenRouter), 3,237 models (Epoch AI), 62 milestone observations, 2020-2026.
- Documents an ~600-fold decline in token prices across the study window.
- Proposes the "Tiered Super-Moore" hypothesis: price declines are not uniform across the model stack — cheaper tiers fall faster than Moore's Law, premium tiers resist.
- Identifies a structural break in market dynamics around May 2024 (F = 5.74, p = 0.005), separating a technology-driven price-decline regime from a competition-driven one.
- Decomposes the cost decline by driver: software/architectural innovation ≈ 103.7% of the reduction, GPU hardware ≈ -0.9% (i.e., hardware improvements alone did not meaningfully drive price down over this period — algorithmic/serving-stack efficiency did essentially all the work).
Methodology
- Data Envelopment Analysis (DEA) to establish production-frontier efficiency across models/providers.
- Chow structural-break testing to locate and validate the regime change in pricing dynamics.
- Cost decomposition analysis separating hardware vs. software/architecture contribution to price decline.
Results
| Segment | Price half-life | vs. Moore's Law (2-yr benchmark) |
|---|---|---|
| Economy tier | 1.10 years | ~1.8x faster |
| Mid tier | 1.55 years | ~1.3x faster |
| Flagship / reasoning tier | Resists the trend | Reasoning commands a ~31.5x premium over non-reasoning price |
- Structural break: May 2024, F = 5.74, p = 0.005 — before this point pricing behaves like a technology curve; after, it behaves like a competitive market (entrants, undercutting, tiered product lines).
- Cost-decline attribution: ~103.7% software/architecture, ~-0.9% GPU hardware (the negative figure implies hardware cost/perf was a mild drag or noise term relative to the software-driven decline, not a meaningful independent contributor).
Limitations
- Author and institutional affiliation not resolved from the abstract-level fetch; full PDF body not ingested in this pass (abstract + reported findings only — flagged as summary-derived).
- "Reasoning premium" (31.5x) is a cross-sectional average; the paper does not (per available summary) break this down by lab or model generation.
- DEA-based frontier estimates are sensitive to model/provider list composition (OpenRouter + Epoch AI); coverage of closed, enterprise-only pricing (e.g., custom enterprise contracts) is likely incomplete.
Why this matters for the KB
Directly instantiates the AI LENS's "Compute economics" required chapter and the content-focus.md spine (inference economics / tokenomics — "who captures the value; the real constraints"). The tiered-decay finding is a testable frame for MenFem's Token Price Index work in kb/inference-economics/ and for reading GPT-5.6's three-tier (Sol/Terra/Luna) launch this same window as tier-differentiated pricing behavior, not a single "the market" price.
Source: Tiered Super-Moore's Law (arXiv:2603.28576), Mingdeng Du, submitted 2026-03-30.