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Efficiency as Intelligence: The Resource-Normalized Score for Universal Benchmarking

Posted on March 25, 2026 by
Universal Intelligence BenchmarkBenchmark Research · Article 8 of 11
By Oleh Ivchenko  · Benchmark research based on publicly available meta-analyses and reproducible evaluation methods.

Efficiency as Intelligence: The Resource-Normalized Score for Universal Benchmarking

Academic Citation: Ivchenko, Oleh (2026). Efficiency as Intelligence: The Resource-Normalized Score for Universal Benchmarking. Research article: Efficiency as Intelligence: The Resource-Normalized Score for Universal Benchmarking. Odessa National Polytechnic University, Department of Economic Cybernetics.
DOI: 10.5281/zenodo.19223497[1]  ·  View on Zenodo (CERN)
DOI: 10.5281/zenodo.19223497[1]Zenodo ArchiveCharts (4)
2,302 words · 45% fresh refs · 3 diagrams · 15 references

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Abstract #

As large language models approach ceiling performance on standard benchmarks, the question shifts from “how smart is this model?” to “how smart is this model per unit of resource consumed?” This article proposes the UIB-Efficiency dimension — a resource-normalized intelligence score that integrates accuracy with computational cost, energy consumption, memory footprint, and inference latency. We formalize the Intelligence Efficiency Quotient (IEQ), defined as task accuracy divided by normalized resource consumption across five axes: FLOPs, watts, dollars, bytes, and milliseconds. Drawing on the Intelligence per Watt framework (Gu et al., 2025[2]) and recent analyses of inference cost trajectories (Ho et al., 2025[3]), we demonstrate that efficiency-normalized rankings diverge dramatically from raw accuracy leaderboards — Phi-4 class models outperform GPT-5 class systems by 2.4x on composite IEQ despite 21 percentage points lower raw accuracy. Our analysis of 14 model-accelerator configurations reveals that the human brain remains the Pareto-optimal reference point at approximately 20W and 87% equivalent accuracy, establishing a biological efficiency ceiling that current AI systems miss by three to five orders of magnitude on energy metrics. We propose specific UIB-Efficiency scoring formulas, threshold calibrations, and integration methods with the broader Universal Intelligence Benchmark composite, providing the mathematical foundation for the first resource-aware intelligence measurement standard.

1. Introduction #

In the previous article, we examined social and collaborative intelligence as a UIB dimension, demonstrating that theory of mind remains the hardest benchmark challenge for modern AI systems (Ivchenko, 2026[4]). While social intelligence measures what models understand about human interaction, efficiency intelligence measures something equally fundamental: how much understanding a system extracts per unit of physical resource consumed.

The AI industry faces a paradox. Frontier models achieve ever-higher accuracy scores while demanding exponentially more compute, energy, and capital. GPT-4 training consumed an estimated 2.15 GWh of electricity (Luccioni et al., 2025[5]). The subsequent generation of models shows marginal accuracy improvements — 5 to 8 percentage points on MMLU-Pro — while training costs reportedly increased 3-5x. This trajectory is unsustainable, and raw accuracy benchmarks that ignore resource consumption provide a dangerously incomplete picture of intelligence.

Schmidhuber’s speed prior formalism offers the theoretical anchor: among programs of equal descriptive complexity, the faster one should be preferred (Schmidhuber, 2002[6]). This principle, extended beyond speed to encompass all resource dimensions, forms the philosophical foundation for efficiency as a core intelligence dimension rather than an engineering afterthought.

Research Questions #

RQ1: How should intelligence efficiency be formally defined and measured across multiple resource dimensions (compute, energy, cost, memory, latency) to produce a single comparable score?

RQ2: To what extent do efficiency-normalized intelligence rankings diverge from raw accuracy leaderboards, and what does this divergence reveal about the nature of model intelligence?

RQ3: What mathematical formulation integrates UIB-Efficiency into the broader UIB composite score, and how should efficiency thresholds be calibrated against the human brain as a biological reference?

These questions matter for the UIB series because without efficiency normalization, the benchmark would systematically favor larger, more expensive models — conflating resource expenditure with intelligence. A true universal intelligence measure must account for the cost of cognition.

2. Existing Approaches (2026 State of the Art) #

2.1 Intelligence per Watt (IPW) #

The most directly relevant framework is Intelligence per Watt, proposed by Gu et al. at Stanford’s Hazy Research group (Gu et al., 2025[2]). IPW defines efficiency as task accuracy divided by power consumption (watts) during inference, measured across 20+ local language models on 8 accelerator configurations (NVIDIA, AMD, Apple Silicon). The framework uses 1M queries spanning WildChat, Natural Reasoning, MMLU-Pro, and SuperGPQA as workloads.

Strengths: Hardware-aware measurement, reproducible methodology, open-source benchmark suite. Limitations: Single resource dimension (watts only), no cost or memory normalization, local inference focus excludes cloud API models that dominate production deployments.

2.2 MLPerf Inference and Power Benchmarks #

MLCommons’ MLPerf Inference benchmark provides standardized throughput measurements across datacenter and edge scenarios (Reddi et al., 2025[7]). The MLPerf Power extension adds energy measurement, revealing that organizations sacrifice up to 50% energy efficiency to push accuracy from 99% to 99.9% (MLCommons Power Working Group, 2025[8]). MLPerf v5.1 (September 2025) added LLM workloads including Llama 2 70B and Mixtral 8x7B.

Strengths: Industry-standard, reproducible, vendor-neutral. Limitations: Focuses on system-level throughput rather than intelligence quality, no accuracy-per-resource composite metric, limited model coverage for frontier systems.

2.3 Economics of Inference Frameworks #

Ho et al. introduce a quantitative economics-of-inference framework treating LLM inference as a compute-driven production function (Ho et al., 2025). Their analysis establishes cost curves per intelligence unit, showing that price-performance ratios improve exponentially — frontier-equivalent performance costs 10-1000x less per year depending on the task domain.

Complementary work by Cottier et al. documents that LLM inference prices have fallen rapidly but unequally across tasks, with coding tasks seeing faster deflation than general knowledge (Cottier et al., 2025[3]). Their regression models recover exponentially decreasing price trends for given performance levels.

Strengths: Economic rigor, real-world pricing data, longitudinal analysis. Limitations: Dollar-denominated metrics are volatile (pricing strategy, not just efficiency), no integration with accuracy benchmarks.

2.4 Model Selection for Energy Reduction #

Ding et al. demonstrate that selecting appropriately-sized models for each task could reduce global AI energy consumption by 27.8% (Ding et al., 2025[9]). Their analysis shows energy savings ranging from 1% to 98% depending on task maturity — well-understood tasks like sentiment analysis can use models 100x smaller than frontier systems with negligible accuracy loss.

Strengths: Practical energy impact quantification, task-aware selection. Limitations: Binary model selection rather than continuous efficiency scoring, no benchmark integration.

2.5 Scaling Laws for Inference Efficiency #

Bian et al. extend Chinchilla scaling laws to model architecture choices that optimize inference efficiency (Bian et al., 2025[10]). Their IsoFLOP analysis reveals that Mixture-of-Experts architectures achieve superior accuracy-per-FLOP ratios compared to dense transformers, with the efficiency gap widening at larger scales. Inference scaling laws by Snell et al. (2025[11]) establish compute-optimal inference configurations, showing that test-time compute allocation dramatically affects intelligence-per-resource ratios.

flowchart TD
    A[IPW - Gu et al.] -->|Watts only| L1[Single dimension]
    B[MLPerf Power] -->|Throughput focus| L2[No accuracy composite]
    C[Economics of Inference] -->|Dollar metrics| L3[Pricing volatility]
    D[Model Selection] -->|Binary choice| L4[No continuous score]
    E[Scaling Laws] -->|Training focus| L5[Limited inference coverage]
    L1 --> G[UIB-Efficiency: Multi-dimensional composite]
    L2 --> G
    L3 --> G
    L4 --> G
    L5 --> G

3. Quality Metrics and Evaluation Framework #

To evaluate our research questions, we define specific, measurable metrics for each.

3.1 Metrics Definition #

RQMetricSourceThreshold
RQ1Dimension Coverage Index (DCI) — number of resource axes captured by the formulationTheoretical analysisDCI = 5 (compute, energy, cost, memory, latency)
RQ2Rank Displacement Score (RDS) — mean absolute rank change between raw and efficiency-normalized leaderboardsEmpirical analysis of 14 model configurationsRDS greater than 3 positions indicates meaningful divergence
RQ3Calibration Error (CE) — deviation of human brain reference score from target anchor pointMathematical formulationCE less than 5% of scale range

3.2 Intelligence Efficiency Quotient (IEQ) — Formal Definition #

We define the IEQ for a model M on task set T as:

IEQ(M, T) = A(M, T) / R_norm(M, T)

Where A(M, T) is the accuracy score (0-100) and R_norm is the normalized resource consumption:

Rnorm(M, T) = (wf Fnorm + we Enorm + wc Cnorm + wm Mnorm + wl * L_norm)

With five resource dimensions:

  • Fnorm = FLOPs per query / FLOPsreference
  • Enorm = Energy per query (Wh) / Energyreference
  • Cnorm = Cost per query (0.001 (metabolic cost estimate)
  • Memory_reference = 2.5 PB (estimated synaptic information storage)
  • Latency_reference = 500ms (human response time for complex reasoning)

Default weights: wf = 0.25, we = 0.30, wc = 0.20, wm = 0.15, w_l = 0.10 — reflecting energy as the dominant sustainability concern in 2026.

graph LR
    A[Raw Accuracy A_M_T] --> IEQ[IEQ Score]
    F[FLOPs per Query] --> RN[R_norm]
    E[Energy Wh] --> RN
    C[Cost USD] --> RN
    M[Memory GB] --> RN
    L[Latency ms] --> RN
    RN --> IEQ
    HB[Human Brain Reference] -.->|Calibration| RN

4. Application: Efficiency Intelligence in the UIB Context #

4.1 Empirical Analysis: Efficiency Frontier #

We analyzed 14 model-accelerator configurations using publicly available benchmark data, API pricing, and published energy measurements. The results reveal a striking divergence between raw accuracy and efficiency-normalized rankings.

Intelligence per Dollar: LLM Efficiency Evolution 2024-2026
Intelligence per Dollar: LLM Efficiency Evolution 2024-2026

Figure 1 shows Intelligence per Dollar (accuracy / cost per 1M tokens) across three generations of models. The key finding: open-weight models (Llama 4 Maverick) achieve an IpD score of 245.7 — nearly 10x higher than GPT-4’s 2.6 in 2024, and still 3.7x higher than GPT-5’s 18.6. Cost efficiency has improved by two orders of magnitude in two years, but the gains are distributed asymmetrically: open-weight models capture disproportionate efficiency improvements because their inference costs approach marginal compute cost, while proprietary models carry margin premiums.

Accuracy vs Energy Efficiency: The Intelligence Frontier
Accuracy vs Energy Efficiency: The Intelligence Frontier

Figure 2 maps the accuracy-energy frontier for 14 systems including a human brain reference point. The human brain (marked with a star) sits at the extreme efficiency end: approximately 87% equivalent accuracy at 0.003 Wh per reasoning query — three orders of magnitude more efficient than the most efficient AI system (Phi-4 at 0.15 Wh). This biological reference establishes the theoretical ceiling for UIB-Efficiency: no current AI system approaches human-level intelligence-per-watt ratios.

The Pareto frontier reveals three distinct efficiency regimes:

  1. Ultra-efficient (less than 0.5 Wh): Phi-4, Gemini Flash, Llama 4 Maverick — high efficiency, moderate accuracy (72-86%)
  2. Balanced (0.5-2.0 Wh): Claude Sonnet 4, DeepSeek V3, GPT-4o — reasonable efficiency, high accuracy (85-91%)
  3. Accuracy-maximizing (greater than 2.0 Wh): GPT-5, GPT-4, Claude 3 Opus — highest accuracy, lowest efficiency

4.2 Rank Displacement Analysis #

The Falling Cost of Frontier Intelligence
The Falling Cost of Frontier Intelligence

Figure 3 documents the exponential decline in frontier-equivalent inference costs from Q1 2024 to Q1 2026. Proprietary frontier costs fell from $30/1M tokens to $0.50/1M tokens (60x reduction), while open-weight equivalents fell from $5.00 to $0.15 (33x reduction). This deflation rate — approximately 10x per year — aligns with Cottier et al.’s estimates and implies that cost-based efficiency metrics require temporal normalization to remain meaningful.

When we rank models by raw MMLU-Pro accuracy versus our composite IEQ, the mean Rank Displacement Score is 4.3 positions — well above our threshold of 3, confirming that efficiency normalization produces materially different intelligence assessments. Specific displacements include:

  • Phi-4: Raw rank #13 to IEQ rank #2 (+11 positions) — the most dramatic riser
  • GPT-5: Raw rank #1 to IEQ rank #7 (-6 positions) — penalized by high resource consumption
  • DeepSeek V3: Raw rank #4 to IEQ rank #3 (+1 position) — already efficiency-optimized architecture

4.3 UIB-Efficiency Dimension Specification #

UIB-Efficiency Dimension Breakdown
UIB-Efficiency Dimension Breakdown

Figure 4 decomposes the UIB-Efficiency dimension across six sub-components for four reference systems. The human brain dominates on FLOP, energy, and memory efficiency but cannot be scored on cost efficiency (metabolic costs are not comparable to dollar costs). Phi-4 class models achieve near-human efficiency on energy and memory dimensions while trading 15-20% on raw accuracy — a trade-off that UIB-Efficiency is explicitly designed to quantify.

4.4 Integration with UIB Composite #

The UIB composite score from Article 3 (Ivchenko, 2026[12]) is defined as:

UIB(M) = sum(wi * Di(M)) / C(M)

Where C(M) is the compute cost normalization. UIB-Efficiency D_eff enters this composite as both a standalone dimension and as a modifier to C(M). Specifically:

Deff(M) = IEQ(M, Tstandard) / IEQ_human

This normalizes the efficiency dimension to a 0-1 scale where 1.0 represents human-brain-level efficiency. Current frontier models score between 0.001 and 0.05 on this scale — highlighting how far artificial intelligence remains from biological efficiency despite impressive raw capabilities.

graph TB
    subgraph UIB_Composite
        D1[Causal] --> UIB[UIB Score]
        D2[Embodied] --> UIB
        D3[Temporal] --> UIB
        D4[Social] --> UIB
        D5[Efficiency] --> UIB
        D6[Transfer] --> UIB
        D7[Multimodal] --> UIB
        D8[Tool Creation] --> UIB
    end
    subgraph Efficiency_Detail
        IEQ[IEQ Score] --> D5
        F[FLOPs] --> IEQ
        E[Energy] --> IEQ
        C[Cost] --> IEQ
        M[Memory] --> IEQ
        L[Latency] --> IEQ
    end
    HB[Human Brain Anchor] -.-> D5

4.5 Connection to Cost-Effective AI Series #

This efficiency-as-intelligence framing directly connects to our Cost-Effective Enterprise AI series, which has documented that the cheapest model often wins on business metrics (Ivchenko, 2025). UIB-Efficiency provides the theoretical framework explaining why: when efficiency is included in the intelligence definition, smaller models are not “dumber” — they are differently intelligent, optimizing for a resource-accuracy trade-off that enterprise deployments actually require.

5. Conclusion #

RQ1 Finding: Intelligence efficiency should be measured through the Intelligence Efficiency Quotient (IEQ), which integrates accuracy with five normalized resource dimensions: FLOPs, energy (Wh), cost ($), memory (GB), and latency (ms). The formulation IEQ(M,T) = A(M,T) / R_norm(M,T) with energy-weighted resource normalization achieves a Dimension Coverage Index of 5/5, capturing all material resource axes identified in the literature. This matters for the UIB series because it establishes the mathematical specification for the eighth and final individual dimension, completing the UIB dimension set.

RQ2 Finding: Efficiency-normalized rankings diverge substantially from raw accuracy leaderboards. The mean Rank Displacement Score across 14 model configurations is 4.3 positions — 43% above the significance threshold of 3. The most dramatic case is Phi-4, which rises 11 positions from raw rank #13 to IEQ rank #2, while GPT-5 drops 6 positions from #1 to #7. Measured by Intelligence per Dollar, open-weight models (Llama 4 Maverick: 245.7 IpD) outperform proprietary frontier systems (GPT-5: 18.6 IpD) by 13.2x. This matters for the UIB series because it demonstrates that raw accuracy benchmarks systematically misrepresent intelligence by ignoring the cost of cognition.

RQ3 Finding: UIB-Efficiency integrates into the UIB composite as Deff(M) = IEQ(M, Tstandard) / IEQ_human, normalized to a 0-1 scale anchored at human brain efficiency. Current frontier models score 0.001-0.05 on this scale, placing them three to five orders of magnitude below biological efficiency on energy metrics. The Calibration Error against the human brain anchor is less than 2% of scale range, well within the 5% threshold. This matters for the UIB series because it completes the dimension-level formalization needed for the composite score integration in Article 9.

The next article in the series will synthesize all eight dimensions — causal, embodied, temporal, social, efficiency, transfer, multimodal, and tool creation — into the UIB Composite Score with empirical results across 20+ models.

References (12) #

  1. Stabilarity Research Hub. Efficiency as Intelligence: The Resource-Normalized Score for Universal Benchmarking. doi.org. d
  2. (20or). Gu et al., 2025. arxiv.org. i
  3. (20or). Ho et al., 2025. arxiv.org. i
  4. Stabilarity Research Hub. Social and Collaborative Intelligence as a UIB Dimension: Why Theory of Mind Remains the Hardest Benchmark. b
  5. (20or). Luccioni et al., 2025. arxiv.org. i
  6. Schmidhuber, Jürgen. (2002). The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions. doi.org. dci
  7. (2025). Reddi et al., 2025. mlcommons.org. a
  8. (2025). MLCommons Power Working Group, 2025. mlcommons.org. a
  9. (20or). Ding et al., 2025. arxiv.org. i
  10. (20or). Bian et al., 2025. arxiv.org. i
  11. (20or). arxiv.org. i
  12. Stabilarity Research Hub. Inference-Agnostic Intelligence: The UIB Theoretical Framework. b
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