Context Reasoning

Context understanding and reasoning tasks test accurate answers grounded in provided context. This capability is essential for knowledge-base support bots, policy lookup systems, and internal knowledge Q&A applications. Models are evaluated on their ability to provide accurate answers that are properly grounded in the given context rather than hallucinating information.

100
Duration
11s
Input Tokens
7754
Output Tokens
300
Cost
$0.00
Context
Input
Which patient had a more dangerous medical timeline?
Expected output
Melisa Farrow
Model output
Melisa Farrow had a more dangerous medical timeline. Her community-acquired pneumonia required immediate hospitalization, IV antibiotics, and oxygen support, representing a potentially life-threatening condition if left untreated. Oscar's condition, while chronic, was less acute and manageable with outpatient treatment.