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Comparisons

Debugging Gap Analysis

Understanding the performance gap between code generation and debugging tasks.

General LLMs perform well on code generation, achieving 70%+ on many benchmarks. However, the moment the task becomes diagnosis + multi-file repair, accuracy falls to below 15%.

Code Generation vs Debugging

ModelCode GenerationDebuggingGap
Claude 4.5 Sonnet72.7%~14%58.7 points
Claude 4.1 Opus72.5%14.2%58.3 points
GPT-4.154.6%13.8%40.8 points
Chronos-1—80.33%Specialized model

Why the Gap Exists

Debugging requires:

  • causal reasoning
  • multi-hop traversal across files
  • log + trace interpretation
  • validation via real test execution
  • iterative refinement

General LLMs cannot perform these steps. Chronos-1 is engineered for them from the ground up.

Chronos-1 vs General-Purpose Models

A detailed comparison showing why general LLMs fail at debugging and how Chronos is engineered for repository-scale repair.

Technical Differences

Architectural differences that make Chronos-1 fundamentally different from traditional LLMs.

On this page

Code Generation vs Debugging
Why the Gap Exists