MedMCQA

What it measures

194,000+ Indian medical entrance exam questions across 21 clinical and preclinical medical subjects.

Reading the measurement

Accuracy is only part of the picture. Inspect unsupported answers, abstentions, language coverage and the evidence available to the model. A confident response can still be wrong, and a broad average can hide a specialist gap.

Published evaluation

The paper’s printed decimal accuracy values are retained without converting their scale. The no-context, Wikipedia and PubMed conditions are distinct.

Where this fits in an evaluation suite

Read accuracy together with error and abstention rates. Domain averages can hide language gaps, specialist failures, and poorly calibrated confidence.

Start with a capability hypothesis, then choose a second test with a different failure mode. Keep the checkpoint, reasoning effort, agent tools, sampling policy, and scoring procedure fixed when measuring a training intervention. Record failures and missing outputs in the denominator.

A reproducible comparison

The paper’s printed decimal accuracy values are retained without converting their scale. The no-context, Wikipedia and PubMed conditions are distinct.

Publish the exact run configuration, item coverage, score units, and available uncertainty. If a source supplies only an aggregate or a single run, preserve that limit. A new model name or a rounded score alone does not establish a capability improvement.