Comparative Performance of Deep‐Reasoning and Lightweight Large Language Models on Oral Implantology Multiple‐Choice Questions

Deep-reasoning LLMs deliver superior implantology MCQ accuracy at the cost of modestly longer inference times. Lightweight models offer near-instant responses with slightly lower accuracy. Aligning model choice to task complexity can optimize speed, cost, and diagnostic precision in implantology education and practice.

Authors

Publication Details

Journal
The International Journal of Prosthodontics
Published
2025-10-01
DOI
https://doi.org/10.11607/ijp.9504
Citations
5
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
12.40
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article

Comparative Performance of Deep‐Reasoning and Lightweight Large Language Models on Oral Implantology Multiple‐Choice Questions

Nguyễn Văn Hùng, Viet Anh Nguyen, Thi Quynh Trang Vuong
5 citations
The International Journal of Prosthodontics
Topic Modeling
12.40
article

Comparative Performance of Deep‐Reasoning and Lightweight Large Language Models on Oral Implantology Multiple‐Choice Questions

Nguyễn Văn Hùng, Viet Anh Nguyen, Thi Quynh Trang Vuong
article en
5 citations

Abstract

Deep-reasoning LLMs deliver superior implantology MCQ accuracy at the cost of modestly longer inference times. Lightweight models offer near-instant responses with slightly lower accuracy. Aligning model choice to task complexity can optimize speed, cost, and diagnostic precision in implantology education and practice.

The International Journal of ProsthodonticsVol. 0(0)
Openalex Percentile: Top 2%
Topic Modeling
12.40
DOISourceOpenAlex
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Comparative Performance of Deep‐Reasoning and Lightweight Large Language Models on Oral Implantology Multiple‐Choice Questions — Nguyễn Văn Hùng, Viet Anh Nguyen, et al. · The International Journal of Prosthodontics (2025) | TGRS Research Map | TGRS