DeepSeek-R1 and Claude Token Efficiency and Latency in Iterative Code Repair with Repository Context

Abstract

Abstract: This report synthesises findings from 10 peer-reviewed papers addressing the following research question: What is the difference in token efficiency and inference latency between DeepSeek-R1 and Claude when performing iterative code repair on FeedbackEval with full repository context. Recent generations of frontier language models have introduced Large Reasoning Models (LRMs) that generate detailed thinking processes before providing answers. While these models demonstrate improved performance on reasoning benchmarks, their fundamental capabilities, scaling. 8 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the difference in token efficiency and inference latency between DeepSeek-R1 and Claude when performing iterative code repair on FeedbackEval with full repository context?

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