DeepSeek-R1 Latency-Accuracy Trade-offs in Code Generation Benchmarks

Abstract

Abstract: This report synthesises findings from 14 peer-reviewed papers addressing the following research question: What is the trade-off between inference latency and code generation accuracy for DeepSeek-R1 versus other LLMs (e.g., CodeLlama, WizardCoder) when evaluated on HumanEval-V and MBPP benchmarks. This paper explores the possibilities of the current generation of Large Language Models for incorporating Machine Learning Operations (MLOps) functionalities into ML training code bases. We evaluate the performance of OpenAI (gpt-3.5-turbo) and WizardCoder (open-source, 15B. 12 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the trade-off between inference latency and code generation accuracy for DeepSeek-R1 versus other LLMs (e.g., CodeLlama, WizardCoder) when evaluated on HumanEval-V and MBPP benchmarks?

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