Medical Long Context Reasoning (MLCR-AA)
MLCR-AA Score
MLCR-AA Accuracy (Judged Responses)
MLCR-AA Completeness (Judged Responses)
MLCR-AA Conciseness
Score
MLCR-AA: Score
Medical Long Context Reasoning (MLCR-AA) Accuracy vs. Completeness Among Judged Responses
Token Usage
MLCR-AA: Output Tokens per Task
Cost
MLCR-AA: Cost per Task
Speed
MLCR-AA: Time per Task
Score vs. Release Date
MLCR-AA: Score vs. Release Date
Example Tasks
Frequently Asked Questions
MLCR-AA is Artificial Analysis' evaluation of MLCR, an open benchmark from Wisedocs that tests whether AI models can reason over long, fragmented medical records, performing the kind of multi-document synthesis claims professionals do when reviewing insurance and healthcare cases. Cases are synthetic medical files of roughly 25,000 to 64,000 tokens, with questions graded across six tiers of difficulty, from locating a single fact to expert-level clinical synthesis and compound, multi-part reasoning.
Each model response must first pass a conciseness gate: an answer longer than five times the reference answer scores zero. Concise responses are then graded by a panel of three LLM judges, with accuracy and completeness decided by majority vote, and the headline overall pass rate credits a response only when it is concise and judged both complete and accurate. On this page the primary score is the overall pass rate, evaluated over the expert and compound multi-part (hard) question sets.
Claude Fable 5 (Adaptive Reasoning, Max Effort, Opus 4.8 Fallback) has the highest MLCR-AA score, with an overall pass rate of 64.4% among models with published MLCR-AA results. View model
A claims professional reviewing a record spanning hundreds of visits is not just searching for a single fact; they are reconstructing chronology, causality, treatment patterns, and claim relevance across fragmented documents. MLCR-AA measures exactly this capability, which matters for any organization evaluating off-the-shelf AI for medical record review, insurance claims, and other long-document healthcare workflows.
No. MLCR-AA is reported as a standalone evaluation and does not contribute to the Artificial Analysis Intelligence Index. It is benchmarked independently by Artificial Analysis.
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Medical Long Context Reasoning (MLCR-AA)An open benchmark from Wisedocs measuring how well models reason over long, fragmented medical records, performing the multi-document synthesis claims professionals rely on when reviewing insurance and healthcare cases. Artificial Analysis evaluates a private held-out set of the hardest cases (expert and compound categories), with accuracy and completeness graded by LLM judges against expert-annotated ground truth.