Large pretrained models and the techniques around them: architectures, training recipes, fine-tuning methods, open weights, context handling and model evaluation.
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How foundation models signals are evaluated
The same Breakout Score applies to every topic, so scores are comparable across areas. What differs is which evidence is usually available — the questions below describe how that plays out here.
Frequently asked questions
Do you cover closed models?
Yes, when there is observable developer-facing change such as a new API capability with documentation. Claims that cannot be checked outside the vendor are marked as self-reported and reduce evidence strength.
How are model releases scored differently from tools?
They use the same model. Releases with open weights or a reproducible evaluation score higher on evidence strength; releases with only a blog post and a leaderboard screenshot score lower and often carry a hype penalty.
Methodology
How the Breakout Score is built
Nine factors add up to 100. Three are analyst judgements, five are measured from stored sources and time series, one is an estimate. A hype penalty of up to −15 is subtracted when attention runs ahead of evidence.
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