medbench.kr

Qwen3.8 Max 2.4T-A95B on clinicalqa@0

Run a988457822dd · baseline tier · evaluated 2026-10-29

Scores

accuracy0.906 (0.889–0.923)
parse rate0.994
n1045
tokens (in / out)419053 / 1840391
cost (USD)0.0000

By category

Allergy and Immunology0.917 (n=36)
Cardiology0.933 (n=89)
Dermatology0.917 (n=12)
Emergency Medicine1.000 (n=32)
Endocrinology0.882 (n=68)
Endocrinology, metabolic, and nutritional disorders1.000 (n=1)
Forensic Medicine1.000 (n=1)
Gastroenterology0.929 (n=112)
General Surgery0.897 (n=58)
Geriatric Medicine1.000 (n=1)
Gynecology0.786 (n=70)
Hematology and Oncology0.979 (n=48)
Infectious Diseases0.865 (n=52)
Musculoskeletal (e.g., costochondritis)1.000 (n=1)
Nephrology0.865 (n=111)
Neurology / Neurosurgery0.980 (n=49)
Obstetrics0.778 (n=18)
Ophthalmology1.000 (n=1)
Orthopedic Surgery0.958 (n=24)
Otolaryngology1.000 (n=14)
Otolaryngology (ENT)1.000 (n=14)
Pediatrics0.875 (n=56)
Pharmacology1.000 (n=1)
Psychiatry0.889 (n=45)
Pulmonology (Respiratory Medicine)0.938 (n=65)
Radiology1.000 (n=1)
Rheumatology0.939 (n=49)
Urology0.846 (n=13)
Vascular Surgery1.000 (n=2)
nan0.000 (n=1)

Reuse this result

Download per-item predictions (parquet) · JSON

@misc{medbench_a988457822dd,
  title = {Qwen3.8 Max 2.4T-A95B on Korean clinical knowledge (KMLE-level MCQ) (clinicalqa@0)},
  author = {{medbench.kr}},
  year = {2026},
  howpublished = {\url{https://medbench.kr/r/a988457822dd}},
  note = {Evaluated 2026-10-29, harness p0.1, run key 27dc14f2d687}
}

Versions

modelqwen3.8-max-ax-2.4t-a95b (Alibaba)
task spec hash77e37cd0ca6bded7fba03792ec1922342b3f6a7dc3ce602881ce196e6aa7377c
harnessp0.1
run key27dc14f2d6879275c93596f54465c5dbcb90cbdf4d9d1fad8f4f87e1d8ef073c
config{"max_tokens": 16384, "prompt": "clinicalqa-p0", "temperature": 0}

Data

ClinicalQA: Korean Medical Knowledge QA Benchmark, CC BY 4.0 (upstream Apache-2.0).

SNUH-HARI. ClinicalQA: Korean Medical Knowledge QA Benchmark. Seoul National University Hospital Healthcare AI Research Institute, 2025. doi:10.23421/2026.0800