DeepSeek R1 and Codestral Performance on Qiskit HumanEval: Latency and Accuracy Across Quantum Circuit Complexities

This report synthesises findings from 6 peer-reviewed papers addressing the following research question: How do Deepseek R1 and Codestral compare in inference latency and token generation accuracy when evaluated on the Qiskit HumanEval benchmark across different quantum circuit complexity levels. Large Language Models (LLMs) have garnered remarkable advancements across diverse code-related tasks, known as Code LLMs, particularly in code generation that generates source code with LLM from natural language descriptions. This burgeoning field has captured significant. 8 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.6/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How do Deepseek R1 and Codestral compare in inference latency and token generation accuracy when evaluated on the Qiskit HumanEval benchmark across different quantum circuit complexity levels?

Autonomous literature synthesis. Automated review score: 7.6/10. Full text and citation available at Assignee Research.