Last updated: April 5, 2026 · Author: Daniel Ashford

Evaluation Methodology

The LLM Judge Index™ Formula

Index = (Accuracy × 0.20) + (Reasoning × 0.20) + (Safety × 0.15) + (Coding × 0.18) + (Creativity × 0.12) + (Instruction Following × 0.15). Weights reflect relative importance for general-purpose use. Use-case-specific recommendations apply different weights via our recommender tool.

Data Sources

Dimension scores are derived from: (1) Automated benchmark suites including MMLU-Pro, GPQA Diamond, AIME, LiveCodeBench, HumanEval, SWE-bench Verified, and IFEval; (2) Pricing and speed data from the Artificial Analysis API (artificialanalysis.ai), updated daily; (3) Community Arena preference votes collected anonymously on our platform; (4) Editorial assessment by Daniel Ashford.

Attribution

Benchmark data provided by Artificial Analysis (artificialanalysis.ai). We provide attribution per their terms. Community Arena data is proprietary to LLMJudge.com.

Update Frequency

Benchmark and pricing data updates daily via API. Community Arena data updates in real-time. Editorial dimension assessments are reviewed quarterly or when a major model update is released.

Independence Guarantee

We have no financial relationship with any model provider that influences scores. Affiliate commissions are earned on click-through referrals and are completely independent of evaluation outcomes. A model's affiliate status has zero effect on its Index score.

Certifications

Quarterly "LLM Judge Certified" awards are determined by the highest Index scores within each category (Overall, Coding, Safety, Value, Open Source, Context Window) at the end of each quarter.

Limitations

No single metric captures all aspects of model quality. The Index reflects general-purpose capability and may not align with specialized use cases. We recommend using our use-case recommender and cost calculator in addition to the Index.

Questions? Contact research@llmjudge.com