DeepSeek R1, Llama3, and Codestral Efficiency Trade-offs in Big-Vul Vulnerability Classification

Abstract

Abstract: This report synthesises findings from 14 peer-reviewed papers addressing the following research question: What is the computational efficiency trade-off between Llama3, Codestral, and Deepseek R1 when performing vulnerability classification on the Big-Vul dataset, measured in tokens per second and. This study investigates the performance of the DeepSeek R1 language model on 30 challenging mathematical problems derived from the MATH dataset, problems that previously proved unsolvable by other models under time constraints. Unlike prior work, this research removes time. 9 claims were extracted from source literature; 2 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 5.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the computational efficiency trade-off between Llama3, Codestral, and Deepseek R1 when performing vulnerability classification on the Big-Vul dataset, measured in tokens per second and accuracy?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance