Context Reasoning – fireworks/glm-4.5-air Task | Opper AI

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.

Input Tokens

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Output Tokens

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Duration

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Cost

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Context

Input

Which patient had a more dangerous medical timeline?

Expected output

Melisa Farrow

Model output

Melisa Farrow had a more dangerous medical timeline. While both patients presented with respiratory symptoms, Melisa experienced rapid deterioration requiring hospitalization with IV antibiotics and oxygen support. She developed systemic signs of illness including high fever, tachycardia, and reduced oxygen saturation, while Oscar's condition remained manageable with outpatient treatment. Melisa's pneumonia involved a confirmed infiltrate with pleural effusion compared to Oscar's milder asthma/viral infection diagnosis.