AISI and EvalEval release evaluation cards for five benchmarks and six frontier models

The cards cover six frontier large‑language models – Claude Opus 4, Claude Opus 4.5, Claude Opus 4.6, GPT‑5, GPT‑5.2 and GPT‑5.4 – and are built on the Every Eval Ever (EEE) schema that EvalEval developed to standardise reporting. AISI’s prior work on OptStop, HiBayES and transcript‑level transparency underpins the effort, aiming to make benchmark replication less costly and more statistically rigorous. By providing full run metadata, the initiative lets researchers diagnose how inference‑time compute and evaluation protocols influence reported performance, and offers a reference point for future meta‑research.

The collaboration hopes that wider adoption of the EEE schema and Evaluation Cards will improve reproducibility across the AI evaluation ecosystem, helping model developers, evaluators and policy analysts understand the conditions behind published scores.