# methodology

OGM scores are transparent, reproducible, and computed only within the open-weights set — a score says how a model compares to other open-weights models, not to closed frontier models. Three principles:

• No imputation. When a signal is missing, its weight is dropped and the remaining weights renormalize. A missing benchmark never becomes a fake number.
• Confidence is explicit. ● high = all signals present, ● medium = one missing, ● low = a single signal. Unranked models have none.
• Every score is explainable. The published rankings.json embeds each model's normalized inputs and the weights below, so anyone can recompute any score.

## overall weights

## coding weights

## agentic weights

## signal normalization

• aa_intelligence / aa_coding — Artificial Analysis indices, already 0–100, used as-is.
• arena_elo — LMArena Elo, min–max scaled to 0–100 using the open-weights set's 5th/95th percentiles as bounds (outlier-resistant), clamped.
• bench_composite — mean of within-set percentile ranks across the LLM Stats benchmark matrix; requires at least 3 benchmarks before it counts.
• swe_bench_verified, aider_polyglot, arena_elo_code — within-set percentile ranks (best open-weights submission per model).

## data flow

A pipeline pulls nine sources daily (Hugging Face, OpenRouter, Artificial Analysis, LLM Stats, LMArena, Epoch AI, SWE-bench, Aider, LiteLLM), resolves every record to a canonical model through a hand-curated registry, and publishes the merged dataset — the same JSON this site and the ogm CLI read, public at /data/v1/. Provider prices refresh every 6 hours. Failed sources fall back to their last snapshot and are marked stale in the footer.

The scoring implementation lives in the repo as pure, golden-tested functions.